Vtusolution - VTU Solution - Get Vtu notes & question …vtusolution.in/uploads/9/9/9/3/99939970/software_testing...vtusolution.in S sytem te stin g guid eli ne s, ASF (Ato icm System

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SO WTF RA E TSET NI G

hcS e em and Sy ll a ub s Su jb ce t doC e: 248SC01 I. .A Mar sk : 52

H uo r eW/s ek : 40 E max uoH r :s 30

T lato H uo rs : 25 E max Mar :sk 001

TRAP – A

NU TI 1 6 uoH rs

A P re eps c vit e no Te gnits , E pmax sel : aB is c de nif i snoit , Test cases, snI i sthg f mor a Ve nn id a rg am,

dI e tn fi gniy te ts ac ses, rorrE a dn af lu t ta imonox es, Leve sl of et ts gni . xE am lp e :s Gene dezilar

sp e odu c edo , hT e rt ai lgn e lborp e ,m hT e Ne Dtx ate f nu itc ,no hT e comm si si no lborp em, hT e MTAS

lpmiS( e otuA am tic Tel rel aM chine) lborp e ,m ehT c rru ency c vno e tr e ,r Sa nrut ihsdniw le d wipe .r

NU TI 2 7 uoH rs

B uo n ad ry ulaV e Te ts i gn , uqE i av el nce ssalC tseT i gn , eD c noisi lbaT e- saB ed T :gnitse B dnuo a yr

v la ue a sisylan , ssentsuboR et ts i gn , W ro ts -c sa e set it gn , pS ce ial v la ue te gnits , xE ma p el s, Ra modn

et ts i gn , uqE ivalen ec cla ss es, uqE ivale cn e t tse sac es f ro ht e irt a gn el borp lem, Ne Dtx ate f nu cti ,no

a dn ht e commi noiss borp el m, diuG el ni es a dn sbo e vr ati sno . De ic is no ta selb , Te ts cas se f ro t eh

t ir a gn le borp el ,m Ne Dtx a et f nu tc ,noi a dn eht c mmo ssi i no borp l ,me uG ide nil es a dn sbo e vr ati .sno

NU TI 3 7 uoH rs

htaP Te ts i gn , taD a lF wo eT ts i :gn DD p ta hs, Te ts c vo erage m te ir cs, B sisa p ta h t se ti gn , diug ile sen

a dn sbo e vr a .snoit eD finiti no - sU e et ,gnits cilS e- sab ed tes it ,gn uG ide nil es dna sbo e vr ita no s .

NU TI 4 6 uoH rs

eL v sle of Te gnits , I tn egr ta i no eT ts i :gn rT ad ti noi al v ei w of et ts i gn el vels, lA et nr a vit e fil e-cyc el

m do e sl , hT e MTAS tsys em, Separati gn tni e rg ati no a dn tsys em et ts i gn . A lc so er kool ta the MTAS

tsys me , D ce noitisopmo -base ,d c la l rg a hp -base ,d P ta h-based tni e rg ati sno

RAP T – B

NU TI 5 7 uoH rs

tsyS em Tes gnit , etnI rac noit eT :gnits rhT ae ,sd Basic c no ec tp s f ro re uq ireme stn ps ce if ci ita ,no

Fi dn i gn thr ae sd , St ur tc ru al rts ateg ei s a dn f noitcnu la rts ateg ei s f ro erht ad gnitset , MTAS et st rht ae ,sd

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.in S sy et m te gnits diug le seni , FSA A( to cim tsyS me nuF cti )sno testi gn exa pm le. C tno e tx of i tn e ar itc ,no

A ta onox my fo i tn eract ,snoi tnI e ar c ,noit com op is ,noit a dn deter im ni ,ms C il en /t Se vr er Tes it ,gn

NU TI 6 7 uoH rs

Pr co e ss Fr ma ewor :k V la di ati no a dn ve ir fi ac noit , De rg ee s of fr ee mod , iraV te ies of os f wt a er . Ba is c

nirp ic lp e :s Sen tis vi i yt , dnuder ancy, re irts ct noi , trap ti noi , v isi ilib ty, F ee abd ck. hT e uq a il ty orp ec ss ,

P al gninn a dn m no i rot i gn , uQ a ytil og a sl , Depen ilibad ty porp er eit s, anA l sy si , T se ti gn , ivorpmI ng ht e

orp ec ,ss grO aniza noit al fac .srot

NU TI 7 6 uoH rs

tluaF -B sa ed Te ts i gn , Te ts E ex c :noitu Over iv ew, As us mp it no s in af ul bt ased te gnits , M tu a noit

an la sisy , Fau tl -based a uqed acy crite air , Vari ta i sno no mu at ti no ana sisyl . seT t xE ce :noitu vO e ivr ew,

f or m t se t case ps ce if ci a snoit to test ac ses, acS f gnidlof , Gene cir vers su ps ce ific cs aff gnidlo , tseT

ro ca le ,s Self-ch ce sk as ro ca le ,s Capt eru a dn re lp a .y

NU TI 8 6 uoH rs

nnalP i gn dna M otino r gni ht e Pr co e ,ss oD c mu e tn i gn anA l sisy dna eT :ts uQ ali yt a dn orp ec ,ss

Te ts a dn anal sy si rts ateg ei s a dn lp a ,sn R si k lp a gninn , M ino t gniro eht orp c ,sse Im vorp i gn t eh

orp ec ,ss ehT uq a il ty team, grO a zin i gn od c mu e ,stn Te ts st ar et yg od c mu e ,tn anA l sisy a dn et ts lp a ,n

Te ts ed is ng s ep cific ta i sno od c mu ent ,s Te ts a dn an la sy si re rop st .

ET TX :SKOOB

1. Pa lu C. J gro e sn en: oS f wt are Tes it gn , A Cr fa mst a s’n orppA ca h, 3rd noitidE , uA e hcabr

buP acil ,snoit .8002 L( i ts ed ipot cs no ly f mor Chap et sr ,1 ,2 ,5 ,6 ,7 ,9 ,01 ,21 31 , ,41 )51

2. Ma oru Pe ezz , iM chal :gnuoY oS f wt a er Te ts i gn a dn nA la sisy – orP ec ss , rP inc lpi es and

T ce uqinh e ,s W li ey dnI i ,a .8002 siL( et d t ipo cs ylno f mor Cha tp e sr ,2 ,3 ,4 ,61 ,71 ,02 )42

FER ERE CN E B KOO :S

1. aytidA P Ma :ruht dnuoF a snoit fo oS f wt a er Tes it gn , eP a nosr cudE at ,noi .8002

2. rS inivasan De kis an, poG ala ws ma y Rame :hs oS f wt a er te ts i gn rP inc pi el s a dn rP tca ic se , dn2

tidE i ,no P ae nosr cudE ta ,noi .7002

3. B ir an Ma irr c :k ehT Cra tf fo oS ftwa er Te ts i ,gn P ae rs no udE c ,noita .5991

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Ta lb e of co tn e stn

nU it De cs ir tp i no

1- 51 A Pers ep itc ev no Tes it ,gn xE a lpm es

TINU 2

dnuoB a yr Va eul Te ,gnits uqE ivale ecn Cla ss Te ts gni , eD c noisi Ta lb e- B desa 61 - 16

Tes it gn

TINU 3 26 - 99

Pa ht T tse ,gni D ta a wolF Testi gn

TINU 4 001 -

Lev sle fo Te ts i ,gn tnI eg ar noit Te its gn 521

TINU 5 621 -

S tsy em Te ts i ,gn tnI e ar c noit Tes it gn 551

TINU 6 651 -

orP ec ss rF ame krow 771

TINU 7 871 -

Fa tlu - aB sed Te ,gnits Te ts xE ce noitu 691

TINU 8 791 -

P al gninn a dn M ino t gniro t eh orP ec ,ss oD c mu en it gn nA la y is s a dn Te ts 522

egaP oN .

Sl No.

TINU 1 1

2

3

4

5

6

7

8

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UNIT 1

A EP R EPS CT VI E ON ITSET NG

W yh od we t se t? hT e er are owt ma ni r ae :snos ot am ke a j mgdu ent ab tuo uq la yti ro a cc ep at lib ti ,y a dn ot sid c vo er borp el sm . We test eb ca su e we wonk ht ta we are fal lbil e — this si e eps cially t ur e in the mod ia n of os ftware a dn sof wt a er c lortno el d tsys me s. hT e og la of siht cha tp er si to c er ta e a pe epsr ctive ro( c tno e )tx no sof wt are tes it gn . We w lli po erate wi niht siht c no et tx f ro ht e rema edni r fo t eh te .tx

1.1 B cisa Defin oiti ns

uM ch of te gnits l ti erat eru si rim ed ni c no f gnisu (a dn os item mes ni c netsisno t) et ygolonimr , borp a ylb beca su e set ti gn t ce onh l ygo has e ov lved over d ace des a dn iv a sc ro es of tirw e sr . hT e et r im on l ygo here (a dn uohguorht t siht )koob si taken f or m sta dn a dr s deve pol ed yb the nI its ut te of elE c scinort a dn elE c rt ic la gnE in ee sr Com up et r oS ci yte . oT get ts a tr ed le ’t s kool ta a su ef lu rgorp e ss i no of et smr [ EEEI .]38 E orr r Pe lpo e ma ek e .srorr A doog onys n my si m“ tsi ake”. When pe po le make im ts akes lihw e c gnido , ew ac ll ht ese tsim akes “ ”sgub . srorrE et dn ot porp ag eta ; a re riuq emen st e rorr am y be am ing fied gnirud

de ,ngis a dn ma p il if ed lits l erom rud i gn co .gnid

aF u tl A fa tlu si the r se u tl of an e rorr . tI si m ro e erp ic se ot say ht at a fa tlu si the re rp esent noita of an e ,rorr

hw ere re rp ese noitatn is ht e m do e of e rpx e noiss , us ch as na rr a vit e te tx , data wolf id a rg ma s, ih erarc yh cha str , ruos ce c edo , a dn os no . “ eD fect” si a doog onys n my f ro fa lu t; os si “ ”gub . Fa stlu ac n eb e evisul . When a de is ng er am kes an e rorr of om noissi , ht e er su tl gni fa tlu is that s gnihtemo si im gniss that s dluoh be rp esent in ht e re rp ese tn a noit . sihT ggus e sts a esu f lu r fe meni ent; ot worrob f mor the C ruh ch, we m hgi t ps ae k of fa stlu of c noissimmo a dn af tlu s of noissimo . A fa tlu of c noissimmo o cc sru hw en we e tn er os em gniht into a re rp e nes at ti no ht ta si ni c rro ce .t Fa tlu s of imo iss no rucco

hw en we fa li ot e tn er c rro ce t ni f ro m ta i no . Of these owt pyt es, fa lu ts of mo issi no are m ro e fid ficult to det ce t a dn res vlo e .

liaF ure A f erulia co c sru hw en a af u tl e ex c tu es. owT s ltbu e it es ar si e here: no e si ht ta f lia ru es ylno co c ru ni an e ex c tu a lb e re rp ese atn it ,no hw ci h si ausu ll y kat en ot eb uos cr e c ,edo ro rom e erp ic se ,yl loaded

jbo e tc c .edo ehT s ce dno bus telt y is aht t siht de tinif i no er al tes fail ru es no ly to fa stlu fo c mmo issi .no woH can we d ae l htiw “f ia rul es” th ta c rro se dnop ot fa stlu of o im noiss ? eW ac n hsup t sih s it ll

f ru ther: tahw a uob t fa stlu that never ha pp en ot e ex cute, ro ma by e ’nod t e ex cu et f ro a l gno mit e? ehT M ci h ea la gn le o suriv si na ex ma p el of us ch a af ult. It t’nseod ex ce tu e tnu il ciM h ale gn e s’ol bi dhtr ay,

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.in March .6 Revie sw rp e nev t am yn af ruli es yb gnidnif fa lu t ,s in f ca ,t well nod e re iv e sw can f dni faul st fo mo issi .no

nedicnI t

When a fail eru co c ,sru ti may ro may ton eb er a ylid a pp are tn to eht u es r ro( cu ts mo er ro te ts e .)r nA inc di e tn si t eh )s(motpmys a oss ciated iw th a f ia erul taht alerts t eh su er to t eh co c rru e ecn fo a fail .eru T se t Test gni si ylsuoivbo c no ec nr ed iw th e ,srorr fau tl ,s fai rul e ,s a dn ni cide .stn A te ts si t eh ca t fo exerc si i gn os f wt a er iw th et ts ac se .s hT e er a er owt di tcnits og a sl fo a te :ts e ti her to fi dn fa ruli e ,s ro to d me no ts ar te c rro e tc e .noitucex T tse C esa A te ts ac es has an di e ytitn , a dn is a ss ociated htiw a rgorp am beha .roiv A tset ca es la os has a set fo i ,stupn a tsil fo e px ec et d .stuptuo

Fi ug re 1.1 A Te gnits Life Cyc el Figure 1.1 rop tra sy a il ef cycle m edo l f ro te gnits . toN eci that, ni ht e deve mpol ent hp ases, ht ere are t rh ee itinutroppo es f ro e srorr to be am ed , re gnitlus in af ul st ht at porp aga et t guorh h the rema dni er fo the dev le mpo e .tn nO e nimorp ent et ts er mus ma zir se this life cycle sa fol :swol the if tsr erht e sahp es are “ uP tt gni B sgu NI ”, ht e te ts i gn esahp si Fi gnidn sguB , a dn t eh sal t t rh ee hp ases a er “Ge tt i gn B sgu UO T” [ notsoP ]09 . ehT Fa tlu Re noitulos ts ep si a hton er roppo t inu ty f ro e rorr s (a dn wen af u tl s). When a “f xi ” uac es s f ro em ylr c rro ce t sof wt are ot m bsi ehave, the fix si de if cie tn . We’ ll

re siv ti t sih hw en ew ucsid ss re rg e noiss te .gnits

morF this se uq ence fo ter sm , ew ees that te ts ac ses o cc ypu a central op is noit in te ts i .gn ehT corp e ss fo te gnits ac n eb vidbus ided into separ ta e ets :sp te ts lp a ,gninn te ts ca es ved le mpo e ,tn gninnur te ts

case ,s a dn eva ul ita gn te ts re us l .st ehT foc su fo siht koob is woh ot identify su ef lu es ts fo test case .s

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.in 1.2 T tse C sesa

hT e e ss e cn e of wtfos are t tse gni si to ted e nimr e a s te of test ca es s f ro the ti me be gni et s et d. eB f ero iog ng no , we n ee d to cla ir fy hw at ni f mro a noit s dluoh be ni a te ts ca es . hT e m so t suoivbo

inf ro m noita is i stupn ; upni st a er er a yll of t ow :sepyt rp e-c tidno i sno ric( c tsmu an ec s that oh ld rp i ro ot et ts case ex ce )noitu a dn ht e actual stupni that were di e itn fied yb mos e itset gn m te doh . hT e ne tx

m so t vbo i uo s pa tr of a et ts case si the e epx etc d ;stuptuo aga ni , ht e er a er owt pyt e :s tsop c dno snoiti a dn a utc la uo t stup . hT e tuptuo rop it no of a et ts c sa e si fre uq e yltn vo erl oo ked. nU f nutro a et , eb ca esu this is often ht e ha dr trap . ,esoppuS f ro exa elpm , uoy were te gnits sof wt are ht ta deter im ned an

tpo ami l tuor e f ro an a rcri aft, vig en ce atr in AAF a ri c rro i rod c stniartsno dna ht e we hta er ad at f ro a fli thg day. woH luow d oy u onk w hw ta the po lamit tuor e really is? hT ere have b ee n va suoir res snop se ot ht si melborp . hT e acade im c re snops e si to op ts ul ta e ht e e tsix en ec of an ro acle, ohw “ swonk la l ht e a wsn e ”sr . nO e irtsudni al re snops e ot ht si borp el m si nwonk as eR fere cn e Tes it ,gn

hw ere the sys et m si test de ni ht e rp ese cn e of e px ert u es sr , a dn ht ese e repx ts am ke j mgdu en st as ot hw te her ro on t uo t up ts fo an e ex c tu ed tes of test ac es i upn st are acce tp ab el . hT e a tc of t tse gni e tn a sli

e ts ab il hs i gn ht e nece ss a yr pre-c dno ti snoi , vorp idi gn the te ts ac se stupni , bo es vr i gn the uo t up st , a dn then c pmo a gnir ht ese htiw the e epx cted uo t up st ot d ete r im ne hw e ht er ro on t ht e test pa ss ed. ehT re am gnini inf noitamro ni a we ll -deve pol ed test case rp imari yl stroppus t tse gni manageme tn . Te ts cases dluohs have an itnedi ty, a dn a er as no f ro be gni r( e riuq ements rt ca gni si a f eni er a .)nos tI si a osl esu f lu ot r ce dro ht e e ex c tu i no sih t yro of a t tse ac es , ni c dul i gn hw en a dn yb mohw it w sa nur , the pa f/ss lia er su tl of ea hc e ex c tu i no , a dn the ve noisr o( f os tf ware) no ihw ch ti was nur . orF m lla fo this, it dluohs be clear th ta test cas se are va ul a elb — at el tsa as v la ua lb e sa ruos ec c edo . Test ac ses n ee d to eb deve pol e ,d ver ei we ,d su e ,d manage ,d a dn save .d 1.3 sthgisnI f mor a nneV d margai

Test gni si f adnu em tn ally c cno e nr ed w ti h behavi ;ro a dn beha roiv si ro t goh on la to ht e st ur tc ru al view com nom ot os ftware (a dn sys et )m devel po e sr . A uq ci k id fferent ai it no is ht at ht e st ur ct ru al view foc su es no “ hw at ti si ” a dn ht e behavi ro la iv ew c disno e sr “ hw at it od e ”s . enO fo eht c gniunitno

ruos ec s fo id ff icu tl y f ro t se ters is that ht e ba es ucod em stn a er usu lla y irw tt ne yb a dn f ro deve pol e ,sr a dn ht e er f ro e ht e me ahp is s si no urts tc laru , ra reht ht an beha roiv al, ni f mro a noit . nI t sih s ce ti no , ew deve pol a elpmis Ve nn id a rg am ihw ch c al ir fies ves eral na igg ng uq e ts i sno tuoba t se ti .gn

ugiF re 1.3 pS ec fi ied a dn lpmI emen et d rgorP ma Beha roiv s C sno ider a nU iver es of rgorp ma beha roiv s. oN( t ci e that ew are f ro ic gn atten it no no the e ss e cn e fo et ts i gn .) Given a rgorp am a dn sti eps cifi ac ti no , snoc ider the set S of eps cified behavi ,sro a dn eht set

P of rgorp a mm ed beha roiv s. Fi rug e 1.3 wohs s the relat noi ship be wt ee n ruo vinu er es of disc esruo a dn eht eps cified a dn rgorp emma d beha sroiv . fO la l the lbissop e rgorp am behavi sro , ht e eps ic f ei d

no es are ni ht e ic cr el labeled S; a dn all t esoh behavi sro ac ut lla y rp o rg a mm ed ton( e the thgils

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.in differe cn e ewteb en P a dn U, the nU iver es ) are in P. W hti siht id a rg am, ew ac n es e rom e cl ae ylr ht e

borp el sm ht ta c no f tnor a te ts er. What fi reht e a er eps cified behavi sro ht ta have on t be ne rgorp a emm d? nI ruo earlier te ygolonimr , these ra e af lu ts of om noissi . miS ali rly, hw at if ht ere are rgorp a emm d lpmi( eme tn e )d behavi sro that have on t b ee n eps cified? sehT e c rro e dnops ot fau tl s fo

com noissim , a dn to e srorr hw ci h co c rru ed a tf er ht e eps cifica it no was c lpmo e et . hT e tni e sr e noitc fo S a dn P (the f abtoo ll pahs ed regi )no is the c“ tcerro ” rop t noi , ht at si beha roiv s ht at are ob th ps ce ified a dn mi lp eme tn ed. A ve yr doog v ei w of te ts i gn si ht at it si the dete nimr ta noi of the e tx ent fo

rgorp am beha roiv ht at is ob th ps eci if ed a dn lpmi e em n et .d sA( a is de thgil , on te ht at “c rro ectne ”ss ylno has meani gn htiw pser e tc to a ps ce if ci ita no dna an lpmi eme tn a it .no It is a rel vita e te ,mr ton an

a etulosb .)

ugiF re 1.4 pS e ic fie ,d lpmI eme tn e ,d a dn Te ts ed Behavi sro

hT e new ic rcle ni giF . 4.1 si f ro Te ts Cas se . oN ecit there si a thgils disc per ancy htiw ruo nU ive sr e of siD c esruo , ht e es t fo rgorp am beha roiv s. niS ec a te ts ac se ca su es a rgorp am beha roiv , t eh

am ht e itam c ai sn im thg f gro ive su . woN , c sno ider ht e rela hsnoit i sp ma gno ht e s te s S, P, a dn T. hT e er am y be s ep c ifi ed beha sroiv that are ton t tse ed ger( i sno 2 a dn )5 , eps c fi ied beha sroiv that are t se ted

r( egi no s 1 a dn )4 , a dn t tse cas se t ah t c rro e dnops ot nu specified be roivah s r( egi no s 3 a dn 7). S mi li a ylr , there am y be rgorp a emm d beha roiv s that are on t et ts ed r( egi no s 2 a dn )6 , rgorp mma de beha sroiv that are te dets r( e snoig 1 a dn )3 , dna et ts ac ses ht at c dnopserro to rgorpnu a mm ed beha sroiv r( e snoig 4 a dn )7 . aE ch of the es regi sno si tropmi a tn . If ht e er are eps cified behavi sro f ro

ihw ch ht ere are on t se t ac ses, the et s gnit si en ec ss a ir ly ni c lpmo e et . If there are et ts cases that c rro e dnops ot epsnu cif dei beha sroiv , t ow ilibissop eit s ar si :e e hti er us ch a te ts ac se is wnu a rr a tn e ,d

ro the eps cif ci a noit is def ci ie tn . nI( ym e px eri ne ce, doog testers often lutsop ate test ac ses of t sih al tt er pyt e. ihT s si a f ni e r ae nos to have doog et srets par icit pate in ps ce if taci i no a dn de is ng re iv ews.)

eW are a rl ae yd at a op i tn hw ere we ac n s ee s mo e ilibissop ties f ro te ts gni as a c ar ft: hw ta ac n a te ts er od to am ke ht e re noig hw ere th se e se st a ll tni e esr ct r( egi no )1 be as al gr e as elbissop ? onA ther yaw

to g te ta this si ot a ks h wo ht e et st ca es s ni ht e es t T are ide itn fied. ehT s troh a wsn er si ht at te ts cases are di e itn fied yb a te gnits met doh . hT si fra em krow gives us a way to c pmo are t eh e ff ec vit ene ss fo vid e esr tes it gn me ,sdoht as ew hs a ll see ni cha tp e sr 8 a dn .11 1.4 fitnedI y gni T tse C sesa

hT e er a er owt f dnu ame tn al a orpp ca hes ot identif gniy et ts ac se ;s the es a er nwonk as f nu itc no al a dn st ur ct ru al te .gnits aE ch fo ht se e a aorpp ches has ves eral tsid i tcn t tse ca es di entifi ac noit m ,sdohte m ero c ylnommo ca ll ed set it gn em t .sdoh

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.in 1.4.1 nuF cti ano l Te ts i gn

nuF c noit al t se ti gn si based no t eh iv ew that a yn rgorp am ac n be con is dered ot be a f tcnu i no that am sp v la ues f mor i st upni t od iam n to va ul es ni i st uo t up t ra gn e. nuF( c noit , od m nia , a dn ra gn e a er

defined ni Cha tp er 3.) hT is on it no si c ylnommo u es d ni e nign eeri gn , nehw tsys me s are c sno idered to be “ alb ck ”sexob . sihT lea sd to the term B al ck B xo Te ,gnits ni hw ci h ht e c tno e tn

lpmi( eme tn a it )no of a lb ca k xob is ton nwonk , a dn ht e f nu c noit fo eht b cal k xob si dnu e dootsr com lp e ylet ni ter sm of its stupni a dn uo t up ts. nI Zen dna hT e trA of Motorcycle tniaM e cnan e , Pi sr ig refe sr ot siht as “ mor anti ”c c rpmo ehen is no [Pi gisr 37 ]. aM yn it mes, we po erate ve yr e ff ec vit le y w ti h b cal k xob wonk el gd ;e in caf t ht is is ec nt ar l to jbo e tc ro ei atn it no . sA an exa ,elpm m tso pe elpo us cce ss fu ll y po era et aut libomo es htiw ylno alb ck xob wonk l de ge.

TUPNI UPTUO T

ugiF re 1.5 nA enignE e s’r alB ck xoB W hti the f nu c anoit l a orpp ca h ot te ts ca es ide itn ficati no , the ylno inf ro am noit that is su ed si eht ps e ic f ci a noit of ht e os f wt a er . hT ere are t ow di nits tc a vd an at g se ot f cnu t noi al t tse ac s :se they a er

i dn epe dn e tn of woh eht os tf ware si lpmi emen et d, so fi ht e mi lp eme tn ta noi cha gn es, ht e et ts cases are s lit l esu f ,lu a dn te ts ac se deve mpol e tn can o cc ru ni para ll el w ti h eht lpmi ement ta i no , ht ere yb re ud c gni vo er lla orp ject dev le po em tn tni e vr al. nO the neg ita ve edis , f nu cti no al tset cases fre uq e yltn us ffer f or m t ow elborp :sm ht ere can be ingis fi ac tn re dnud a cn ei s ma gno te ts cases, a dn ht is si

com dnuop ed yb ht e ssop ib li yti of ga sp of tnu e ts de os ftwa er . Fi rug e 1.6 s swoh the re lus st of tset cases identi eif d yb t ow f nu itc no al m sdohte . Me doht A di en it fies a la gr er s te of t tse ca es s ht an seod Me doht B. oN t ci e that, rof htob tem sdoh , the set of te ts cases is com lp ete yl c no at ni ed nihtiw ht e s te of eps cified beha roiv . Since f nu cti no al me doht s are based no ht e eps cified behavi ro , ti si ha dr ot

ami enig ht e es m te sdoh ide itn f gniy beha roiv s ht at ra e ton eps cifie .d 1.4.2 tS ruc ut r la eT s gnit St ur tc ru al te its gn si the other f dnu ame tn al a orpp ca h ot te ts c esa di e tn i cif ati no . oT c rtno a ts it htiw

nuF c noit al Tes it gn , ti si os tem i em s c la led W tih e B xo ro( even aelC r )xoB Te ts i gn . hT e c el ar xob em ta rohp is borp a ylb more a irporpp a et , eb ca su e ht e e ss ential id fference is that ht e pmi el me tn ati no

o( f ht e alB ck B )xo is nwonk a dn su ed to di e itn fy et ts case .s B ie gn able to s“ ee disni e” the lb ca k xob a swoll eht set ter to di e itn fy et ts cases based no woh ht e f nu itc no is a tc ua yll mi lp eme tn e .d St ur tc ru al Tes it gn has eb en ht e jbus ect of mos e riaf ly ts or ng the yro . oT really dnu e tsr a dn ts ur tc ru al et ts i ,gn the c cno e stp of l ni ae r rg a hp ht e yro hC( a tp er )4 are e ss en it la . W hti ht ese c no ec stp , the te ts er ac n ri suorog ly de cs ri eb exa yltc hw ta si b ie gn te ts ed. Beca su e of sti ts gnor the ro e cit la ba ,sis

st ur ct ru la te ts i gn le dn s i st elf to the d fe ni i noit dna su e of t tse c vo erage em irt cs. Te ts c vo era eg em irt cs divorp e a way to e lpx ci i lt y s at te t eh e tx e tn ot ihw ch a os ftware eti m has been t tse e ,d a dn

this ni t ,nru am kes t tse gni manageme tn erom naem i gn f .lu 1.4.3 T eh nuF c lanoit eV r sus tS ruc ut r la De etab

viG en t ow f dnu ema tn la yl id f ef rent a orpp aches ot te ts ac se ide itn f ci a noit , ht e rutan la uq e ts i no si ihw ch is be tt er? If uoy read um ch fo eht til era erut , uoy w lli fi dn gnorts hda eren st to either c .ecioh

Refe gnirr to st ur ct ru la gnitset , boR e tr notsoP rw ite :s t“ his t loo has eb en wa ts i gn t se te s’r mit e si ecn

egaP 5

Vtusolution.in

Vtusolution.in

vtuso

lution

.in the s0791 . . . ti[ ] od es ton troppus doog os f wt are te gnits rp ac it ec a dn uohs ld on t be ni ht e te ts e sr t oo l tik ” [ notsoP ]19 . nI defe sn e of urts c larut t gnitse , wdE a dr M li ler [ lliM er 19 ] tirw :se “ rB a hcn c vo erage [a st ur tc aru l tset c vo erage m te ]cir , if tta a ni ed at eht 58 pe cr e tn ro be tt er level, te sdn ot ide itn fy iwt ce eht un m reb fo ed fe tc s ht at dluow vah e eb en f dnuo yb ’intu ti vi e’ [f cnu it ]lano te ts i gn .” 1.5 E rorr dna aF u tl Tax no seimo

ruO d ife n ti snoi of e rorr a dn fau tl hi gn e no the tsid i noitcn b te w ee n ecorp ss a dn udorp :tc orp c sse refe sr to woh we od mos te gnih , a dn udorp tc si t eh e dn re tlus of a corp se s. hT e op int ta ihw ch te gnits a dn oS f wt are uQ a il ty russA ance meet si t tah AQS t ipy ca ll y rt ei s to mi vorp e t eh udorp ct yb mi vorp ing ht e corp e ss . nI ht at se esn , t se ti gn si cl ae ylr erom udorp ct ro ei tn e .d AQS si erom

c cno e nr ed htiw re ud c gni e rorr s e dn me ic in the deve mpol e tn corp e ,ss hw li e t gnitse si erom c cno e nr ed htiw disc vo eri gn af stlu ni a udorp ct. htoB di nilpics se be en fit f or m a clearer ed f tini i no fo t py se of fa lu t .s Fa stlu ac n eb lc a iss fied in ves er la wa :sy t eh deve mpol ent hp ase hw ere t eh c rro e gnidnops e rorr o cc rru ed, the c sno e uq e cn es of c rro e dnops i gn fa li seru , fid cif ytlu ot re vlos ,e ksir of on r se ol tu i no , a dn so no . My fa tirov e si based no a mon aly oc rruc en :ec no e t emi no l ,y in et rm tti e tn , er c gnirru , ro rep ae table. Fi rug e .1 9 c tno a sni a af u tl at ymonox eB[ zi er 48 ] t tah d itsi hsiugn es fa stlu yb eht seve ytir fo the ri c sno e uq e cn e .s 1.6. Lev le s fo Tes gnit

suhT raf ew evah dias gnihton tuoba eno fo eht yek stpecnoc fo gnitset — slevel fo noitcartsba . L leve s fo gnitset ohce eht slevel fo noitcartsba dnuof ni eht llafretaW ledoM fo eht erawtfos tnempoleved efil elcyc . elihW siht om led sah sti ,skcabward ti si lufesu rof gnitset sa a snaem fo gniyfitnedi tcnitsid slevel fo ,gnitset

dna rof gniyfiralc eht sevitcejbo taht niatrep ot hcae level .

elbaT 1 tuptuO/tupnI stluaF pyT e secnatsnI

upnI t tcerroc tupni ton detpecca tcerrocni tupni detpecca

noitpircsed gnorw ro gnissim sretemarap gnorw ro gnissim

uptuO t gnorw tamrof gnorw tluser tcerroc tluser ta gnorw emit oot( ,ylrae oot

late) etelpmocni ro gnissim tluser

suoirups tluser eps rammarg/gnill

citemsoc elbaT 2 cigoL stluaF gnissim )s(esac

etacilpud )s(esac emertxe noitidnoc detcelgen

noitaterpretnisim gnissim noitidnoc suoenartxe )s(noitidnoc

egaP 6

Vtusolution.in

Vtusolution.in

vtuso

lution

.in

tset fo gnorw elbairav tcerrocni pool noitareti

gnorw rotarepo ,.g.e( < daetsni )≤ elbaT 3 noitatupmoC stluaF tcerrocni mhtirogla

gnissim noitatupmoc tcerrocni dnarepo

tcerrocni noitarepo sisehtnerap rorre

tneiciffusni cerp noisi dnuor( - ,ffo )noitacnurt gnorw tliub - ni noitcnuf

hT ere is a rp act ci al rel ta pihsnoi be ewt en el vels of te gnits a dn f tcnu i ano l a dn urts tc ru la et gnits . tsoM rp ca noitit e sr a rg ee aht t st ur tc ru al te gnits si tsom a airporpp te ta ht e nu it el ve ,l ihw le f nu cti no al et ts i gn si som t a irporpp a et at ht e ys ets m leve .l While ht is is generally eurt , it si la os a like yl

c sno e uq ence of the ba es inf noitamro cudorp ed rud i gn the re uq ir eme stn ps ce if ci a noit , erp l nimi a yr de ngis , a dn deta li ed de ngis hp as se . hT e c no ts ur stc ed fined f ro arutcurts l test gni am ke the tsom se sn e at ht e tinu lev ;le dna is lim ar c urtsno c st a er ylno won b ce gnimo avai al lb e f ro the i tn e rg a noit a dn ys ets m el vels of t se ti gn . We deve pol us ch ts ur tc ru es ni Pa tr VI ot us p rop t s urt c rut al et gnits at t eh i tn e rg ta noi a dn ys ts me lev le s f ro htob tradi noit al a dn ejbo ct- iro e tn ed os ftwa .er

elbaT 4 ecafretnI stluaF tcerrocni tpurretni gnildnah

O/I gnimit llac ot gnorw erudecorp

llac ot non - tnetsixe erudecorp retemarap hctamsim ,epyt( )rebmun

elbitapmocni sepyt suoulfrepus noisulcni

elbaT 5 ataD stluaF tcerrocni noitazilaitini

i tcerrocn ssecca/egarots gnorw xedni/galf eulav tcerrocni gnikcapnu/gnikcap gnorw desu elbairav gnorw atad ecnerefer gnilacs ro stinu orre r tcerrocni atad noisnemid

tcerrocni tpircsbus tcerrocni epyt

tcerrocni atad epocs es n ros atad tuo fo stimil

ffo yb eno tnetsisnocni atad

egaP 7

Vtusolution.in

Vtusolution.in

vtuso

lution

.in 1.2 ehT T elgnair orP b mel

ehT y ae r fo this tirw i gn am skr eht twe itn eth a vinn er as yr of bup l ci a snoit su i gn eht irT a lgn e elborP m as an ex ma .elp emoS fo eht m ero ton a elb e irtn es ni t ih s gener ta noi fo tes it gn l ti er ta eru a er [ urG e bn e gr er ,]37 [ nworB ,]57 [ yM e sr ,]97 [ rP e mss an 28 (a dn eht s ce dno dna thi dr ed ti ,]snoi [ lC a ekr ,]38 [Cla ekr ,]48 C[ h le la app ,]78 a dn [He zt el .]88 hT e er a er borp a ylb othe ,sr tub siht l si t

dluohs fus fic .e 1.2.1 Pr bo el m tatS em ne t

ehT irT angle rgorP ma ca ce stp t eerh tni ege sr as upni ;t ht e es a er taken to eb sides fo a irt a .elgn ehT tuptuo fo t eh rgorp am si eht pyt e fo irt a gn le determ ni ed yb eht t eerh is de :s liuqE atera ,l sosI c le e ,s

S lac e ,en ro oN t rTA ai gn l .e moS e mit es siht borp l me si e tx e dn ed to ni lc edu ri thg rt ai lgn es as a fif ht t py ;e ew iw ll esu siht e tx e noisn in emos fo t eh e rex ic se .s 1.2.2 siD c noissu Pe hr a sp eno fo eht er a snos f ro the l gno ev ti y of ht is ex ma lp e si ht at, a gnom hto er sgniht , it t ipy fies

mos e of the ni c lpmo ete de tinif i no ht ta sriapmi cinummoc a noit a gnom c motsu e sr , dev le po e sr , a dn et ts ers. sihT eps cifi ac ti no rp e mus es the deve repol s wonk some ted a sli a uob t tr ai gn el ,s in pa itr c ralu

the rT ai lgn e porP ert :y ht e us m of a yn pa ri of sedis m tsu be irts ltc y rg eater than the thi dr si ed . If a, ,b a dn c de ton e the rht ee ni et ger dis es, ht en the t ir a lgn e porp erty si mat eh am it c la ly ats ted as t eerh ine ilauq tie :s a < b + c, b < a + c, a dn c < a + b. If a yn no e of these af ils ot be t eur , ht e i tn egers ,a ,b a dn c od ton c tutitsno e sides of a irt a elgn . If la l rht ee sides are e uq al, yeht c sno it tut e an e uq ilate ar l t ir a gn el ; if e ax c ylt no e pa ri fo edis s si e lauq , ht ey f ro m an i sos ce el s t ir a gn el ; a dn if on p ia r fo is des si e uq a ,l ht ey c itsno t tu e a cs ale en t ir a gn l .e A doog tes et r mi thg f htru er cla ir fy ht e lborp me st ta eme tn yb up itt ng mil sti no the le shtgn of the dis es. What re snops e dluow we e px ce t if we rp ese tn ed t eh

rgorp am w ti h the is des -5, -4, -3? eW iw ll re uq ire ht ta all dis es be at lea ts 1, a dn hw ile we are at ,ti we may as we ll ed clare s emo ppu er limit, say 02 , 000 . emoS( la ugn age ,s l ki e Pasc la , have na a motu a it c l mi it, ac ll ed TNIXAM , ihw ch si eht al gr e ts bina yr tni eger re rp ese tn ab el in a cer at ni

bmun er fo ib ts.) 1.2.3 Tr lanoitida I lpm em ne tat ion

hT e “ rt a tid i no al” melpmi e tn ita no of siht rg a fdn ta her of lla e selpmax sah a rather OF R NART - kil e st ly e. hT e f wol chart f ro siht lpmi eme tn ati no a pp ae sr ni Fi rug e 2.1. hT e f wol cha tr xob bmun ers c rro e dnops to co emm nt bmun e sr ni the NARTROF( - il ke) PobruT as ac l rgorp am vig en ne tx . hT( ese

bmun e sr c rro e dnops e ax tc yl ot soht e ni [ rP e namss ]28 .) I t’nod r ae lly il ek siht mi lp ement noita ve yr much, os a erom st ur ct ru ed pmi le em tn a noit is vig en in es c noit 2.1.4. ehT va ir ab el tam ch si su ed ot rec dro e uq a ytil ma gno pai sr of ht e sides. hT ere si a lc cissa al rtni ic ca y of the TROF R NA lyts e c enno cted iw th the vari lba e ma hct : on ecit ht ta a ll rht ee te sts f ro ht e t ir a lgn e porp e ytr od ton occ ru . fI t ow sides are e lauq , say a a dn c, it si ylno n ce sse a yr ot c pmo are a+c w ti h b. S( in ec b sum t be rg ae ret than ze or , a + b m tsu eb rg eater than c, beca esu c e uq la s a.) hT is sbo e vr ati no cl ae ylr re cud se t eh a nuom t of compar si sno ht ta sum t be ma .ed ehT ef if ciency of siht ve noisr si tbo a ni ed at t eh e px e esn of lc ar ti y (a dn ae se of )!gnitset . We liw l f dni this ver is no su ef lu la et r in Pa tr III hw en we disc ssu i fn ea elbis rgorp am ex ce u it no pa .sht hT at is eht be ts r ae nos f ro reta gnini this ver is .no

egaP 8

Vtusolution.in

Vtusolution.in

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.in P RGOR AM irt a 1elgn ni( p tu , uo pt tu );

AV R ,a ,b ,c hctam : IN ET GER ;

BE IG N nletirw retnE'( 3 tni e sreg ihw ch era dis es fo a tr ai n lg e' ;)

re nlda (a, b, )c ; nletirw ediS'( A si ,' a) ; nletirw ediS'( B is ,' )b ; nletirw ediS'( C is ,' ;)c

hctam =: 0; a FI b = 1{ }

NEHT hctam =: tam ch + ;1 { } 2 FI a = c 3{ }

NEHT hctam =: tam ch + ;2 { } 4 FI c = b 5{ }

NEHT hctam =: tam ch + ;3 { } 6 FI tam ch = 0 7{ }

NEHT FI b+a( ) =< c 8{ } NEHT nletirw ('N to a airT n lg e') 21{ . }1

ESLE FI )c+b( =< a 9{ } NEHT nletirw ('Not a lgnairT e') }2.21{

ESLE FI )c+a( =< b 01{ } NEHT nletirw ('N to a Tri na lg e )' {1 .2 }3 ESLE nletirw ( elgnairT' is Sc la ene') 11{ }

ESLE FI hctam =1 31{ } NEHT FI +a( )c < b= {14 }

NEHT nletirw ('Not a lgnairT e') }4.21{ ESLE etirw ln ( irT' a elgn is lecsosI es') }1.51{

ESLE FI 2=hctam 61{ } NEHT FI ( c+a ) =< b {17 }

NEHT nletirw ('N to a Tri na lg e )' {1 .2 }5 ESLE nletirw ( elgnairT' is Is cso e sel 51{ . }2

ESLE FI tam ch=3 81{ } NEHT FI ( c+b ) =< a {19 }

NEHT etirw ln '( N to a airT n lg e') {12 6. } ESLE etirw ln irT'( an elg si Is lecso es') 1{ .5 3}

ESLE tirw e nl ( airT' n elg is Eq etaliu ar l'); {20 } END . 1.2 ehT T elgnair orP b mel

hT e y ae r of siht irw it gn ma skr ht e twentie ht a vinn e sr a yr of bup acil snoit su i gn ht e rT ai lgn e lborP me as an ex ma elp . oS me of ht e rom e on at b el e rtn ei s ni siht genera noit of set ti gn l ti era rut e are [ urG e bn e gr er ,]37 [ nworB 57 ], [ yM e sr ]97 , [ rP e ss am n 28 (a dn ht e es c dno a dn driht e id t ]snoi , [ lC a kr e ]38 , [ lC a kr e ,]48 [ hC ella pp a ]78 , a dn [He zt el ]88 . hT ere are borp a ylb othe sr , tub th si l tsi

dluohs fus fic .e 1.2.1 Pr bo el m tatS em ne t

hT e rT ai elgn rgorP ma ca ce stp t erh e in et ge sr as upni ;t ht e es are taken ot be dis es of a rt ai elgn . hT e tuptuo of the rgorp am si ht e t py e of rt ai lgn e d te re m ni ed yb ht e t rh ee sedis : uqE ilateral, csosI eles,

S lac e en , ro oN t rTA ai gn le. moS e mit es siht elborp m si e tx e dn ed ot cni l edu ri hg t rt ai lgn se as a fifth t py ;e ew iw ll esu siht e tx e noisn in emos fo t eh e rex ic se .s 1.2.2 siD c noissu

egaP 9

Vtusolution.in

Vtusolution.in

vtuso

lution

.in

Pe hr a sp eno fo eht er a snos f ro the l gno ev ti y of ht is ex ma lp e si ht at, a gnom hto er sgniht , it t ipy fies

mos e of the ni c lpmo ete de tinif i no ht ta sriapmi cinummoc a noit a gnom c motsu e sr , dev le po e sr , a dn et ts ers. sihT eps cifi ac ti no rp e mus es the deve repol s wonk some ted a sli a uob t tr ai gn el ,s in pa itr c ralu

the rT ai lgn e porP ert :y ht e us m of a yn pa ri of sedis m tsu be irts ltc y rg eater than the thi dr si ed . If a, ,b a dn c de ton e the rht ee ni et ger dis es, ht en the t ir a lgn e porp erty si mat eh am it c la ly ats ted as t eerh ine ilauq tie :s a < b + c, b < a + c, a dn c < a + b. If a yn no e of these af ils ot be t eur , ht e i tn egers ,a ,b a dn c do ton c tutitsno e sides of a irt a elgn . If la l rht ee sides are e uq al, yeht c sno it tut e an e uq ilate ar l t ir a gn el ; if e ax c ylt no e pa ri fo edis s si e lauq , ht ey f ro m an i sos ce el s t ir a gn el ; a dn if on p ia r fo is des si e uq a ,l ht ey c itsno t tu e a cs ale en t ir a gn l .e A g doo tes et r mi thg f htru er cla ir fy ht e lborp me st ta eme tn yb up itt ng mil sti no the le shtgn of the dis es. What re snops e dluow we e px ce t if we rp ese tn ed t eh

rgorp am w ti h the is des -5, -4, -3? eW iw ll re uq ire ht ta all dis es be at lea ts 1, a dn hw ile we are at i ,t we may as we ll ed cla er emos ppu er limit, say 02 , 000 . emoS( la ugn age ,s l ki e Pasc la , have na a motu a it c l mi it, ac ll ed TNIXAM , ihw ch si eht al gr e ts bina yr tni eger re rp ese tn ab el in a cer at ni

bmun er fo ib ts.) 1.2.3 Tr lanoitida I lpm em ne tat ion

hT e “ rt a tid i no al” melpmi e tn ita no of siht rg a fdn ta her of lla e selpmax sah a rather OF R NART - kil e st ly e. hT e f wol chart f ro siht lpmi eme tn ati no a pp ae sr ni Fi rug e 2.1. hT e f wol cha tr xob bmun ers c rro e dnops to co emm nt bmun e sr ni the NARTROF( - il ke) PobruT as ac l rgorp am g vi en ne tx . hT( ese

bmun e sr c rro es dnop e tcax ly ot t esoh ni [ rP e mss an ]28 .)

ehT var ai elb tam ch si desu ot r ce dro e uq a il ty a gnom pai sr fo t eh is de .s rehT e si a cla iss ac l rtni i ac cy fo t eh OF RTR NA yts le nnoc ce ted htiw t eh va ir a elb ma hct : ton i ec ht at lla rht ee tests f ro eht rt ai elgn

porp erty od ton co c .ru fI owt dis es a er e uq a ,l say a a dn ,c ti si no ly nece ss a yr ot c pmo a er a c+ htiw b. ecniS( b must eb rg ae ter than ze ,or a + b m tsu eb rg ea et r ht an ,c eb ac esu c e uq a sl a.) sihT

sbo e vr ita no clea ylr re cud es t eh ma tnuo fo compa ir s sno ht at tsum eb am .ed ehT fe f ci ei ncy fo this ve noisr is tbo ained at eht e px e esn fo clari yt (a dn ea es fo tes it .)!gn We wi ll fi dn siht ve sr i no su ef lu al et r ni Pa tr III hw en ew disc ssu i fn ae elbis rgorp ma e ex cu it no pa sht . tahT si eht be ts aer s no f ro reta gnini siht ve sr i .no P RGOR AM irt a 1elgn ni( p tu , uo pt tu );

AV R ,a ,b ,c hctam : IN ET GER ;

BE IG N nletirw retnE'( 3 tni e sreg ihw ch era dis es fo a tr ai n lg e' ;)

nldaer (a, b, )c ; nletirw ediS'( A si ,' a) ;

egaP 10

Vtusolution.in

Vtusolution.in

vtuso

lution

.in

nletirw ediS'( B is ,' )b ; nletirw ediS'( C is ,' ;)c

hctam =: 0; a FI b = 1{ }

NEHT hctam =: tam ch + ;1 { } 2 FI a = c 3{ }

NEHT hctam =: tam ch + ;2 { } 4 FI c = b 5{ }

NEHT hctam =: tam ch + ;3 { } 6 FI tam ch = 0 7{ } HT NE FI b+a( ) =< c 8{ }

NEHT nletirw ('N to a airT n lg e') 21{ . }1 ESLE FI )c+b( =< a 9{ }

NEHT nletirw ('Not a lgnairT e') }2.21{ ESLE FI )c+a( =< b 01{ }

NEHT nletirw ('N to a Tri na lg e )' {1 .2 }3 ESLE nletirw ( elgnairT' is Sc la ene') 11{ }

ESLE FI hctam =1 31{ } NEHT FI +a( )c < b= {14 }

NEHT nletirw ('Not a lgnairT e') }4.21{ ESLE etirw ln ( irT' a elgn is lecsosI es') }1.51{

ESLE FI 2=hctam 61{ } NEHT FI ( c+a ) =< b {17 }

NEHT nletirw ('N to a Tri na lg e )' {1 .2 }5 ESLE nletirw ( elgnairT' is Is cso e sel 51{ . }2

ESLE FI m ta ch=3 81{ } NEHT FI ( c+b ) =< a {19 }

NEHT etirw ln '( N to a airT n lg e') {12 6. } ESLE etirw ln irT'( an elg si Is lecso es') 1{ .5 3}

ESLE tirw e nl ( airT' n elg is Eq etaliu ar l'); {20 } END . 1.2.4 tS ruc ut er d lpmI em ne oitat n Figure 2.2 si a ad tafl wo id a rg am desc ir p it no fo t eh t ir a elgn rgorp a .m eW c uo ld elpmi em tn it as a

am ni rgorp am wi ht eht f ruo idni c ta ed corp e rud e S .s ince ew lliw esu this exa elpm tal er f ro tinu et ts i ,gn eht f ruo orp ec rud es ha ev been me gr ed ni to eno PobruT asc la rgorp a .m C mmo e tn lines rela et

sec it sno fo t eh c edo to eht ed c noitisopmo vig en ni Fi rug e 1. .2

P RGOR AM irt a 2elgn ni( p tu , uo pt tu );

AV R ,a b, c : GETNI ER;

irTAsI an elg : B OO LEAN; BE IG N

tcnuF{ i no :1 teG I pn ut} nletirw retnE'( 3 inte sreg w hcih era sides of a tr ai n lg e');

nldaer a( ,b, )c ; nletirw S'( i ed A si ,' )a ; nletirw S'( i ed B is b,' ) ; nletirw S'( i ed C is ,' )c ;

tcnuF{ i no :2 sI A Tr ai n elg ?}

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FI a( < b + )c DNA b( < a + c) DNA c( < a + )b NEHT irTAsI a elgn =: TRUE ESLE sI AT elgnair =: FA ;ESL

tcnuF{ i no :3 eteD rmi en irT an elg Type} FI lgnairTAsI e

NEHT FI a( = b) DNA (b = )c NEHT nletirw ( irT' a gn le si Equ li a aret l'); ESLE FI a( <> b) DNA (a >< )c A DN (b >< )c

NEHT nletirw irT'( a elgn is lacS ene' ;) ESLE nletirw ( irT' a elgn si Is so c le es')

ESLE etirw ln ('N to a Trian lg e'); {N :eto noitcnuF ,4 eht Output tnoC r ,rello sah neeb em r deg into

sesualc ni noitcnuF 3.} END . 1.3 ehT NextD eta noitcnuF

hT e c lpmo exi yt ni the rT ai lgn e rgorP am si ud e to r itale no s spih be wt ee n upni st a dn c rro ect uo t up t .s eW iw ll su e the Ne Dtx ate f nu cti no to illu ts r ta e a di ff ere tn dnik fo com lp e ytix — gol ical

re al spihsnoit ma gno eht tupni var ai lb es ht emse .sevl 1.3.1 Pr bo el m tatS em ne t NextDate si a f nu tc noi of rht ee va ir a lb es: m no t ,h day, a dn year. tI re ut snr the date of the day fa ret the tupni da et . hT e htnom , day, a dn y ae r va ir abl se have mun e cir al value :s wi ht 1 ≤ m no th ≤ 21 , 1 ≤ day ≤ ,13 a dn 2181 ≤ raey ≤ .2102 1.3.2 siD c noissu

hT ere are t ow s cruo se of com lp e ytix in ht e Ne Dtx ate f nu cti :no ht e tsuj me noitn ed c lpmo ex ti y of the i tupn mod a ni , a dn t eh ur el ht at di ugnits i hs es oc mm no y ae sr f or m el ap years. cniS e a ey ar si

563 . 2242 days gnol , le pa y ae rs are su ed f ro the e“ xtra day” borp el m. fI we d alce red a leap ye ra eve yr f htruo y ae ,r the er uow ld eb a hgils t e rorr . hT e rG e irog an Cale dn ar (i sn it ut et d yb poP e

rG e og ry in )2851 re os lves siht yb adju ts gni el ap y ae sr no ec nt yru y ae .sr suhT a y ae r si a leap y ae r fi ti is sivid ib el yb 4, sselnu ti si a ce yrutn y ae r. Ce yrutn years are leap y ae sr ylno fi ht ey are tlum i lp es

of 004 [ ilgnI s ]16 , [ OSI ]19 , os ,2991 6991 , a dn 0002 a er leap yea sr , lihw e the y ae r 0091 si a com nom ey a .r hT e Ne Dtx ta e f nu cti no a osl rtsulli ates a si hgiled t of os f wt are te ts i .gn aM yn ,semit we f dni e lpmax es of Zipf s’ La ,w ihw ch ts ates t ah t %08 fo ht e a ytivitc oc sruc ni %02 of ht e s ap c .e

itoN ce woh um ch fo t eh ruos ce c edo si de tov ed ot l ae p ey ar c disno era .snoit 1.3.3 I lpm em ne tat ion

P RGOR AM Ne etaDtx ( NI P ,TU OUTP )TU ;

YT PE htnom T epy = 1.. 21 ; da pyTy e 1 = ..31;

aey r epyT 1 = 8 21 .. 102 2; tad eT epy = er cor d

htnom : htnom T epy ; ad y : dayT epy ;

raey : raey T epy ;dne tad*( eT epy er cord *)

AV R dot ay , romot r epyTetad: wo ; N*( NIGEB e taDtx e*)

nletirw ( retnE' t do a 'y s' etad in eht f mro MM DD YYY Y'); nldaer dot( a ,htnom.y dot ay.da ,y today.ye ;)ra

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romot r wo =: dot ay; HTIW t do ay DO

ESAC m no th OF ,7,5,3,1 8 1, 0: FI day < 13 NEHT romot r d.wo ay =: day + 1

ESLE Begin d.worromot ay =: ;1

romot r nom.wo th =: nom th + 1 ;dnE

11,9,6,4 : FI day < 03 NEHT t romo r d.wo ay =: day + 1 ESLE Begin

d.worromot ay =: ;1 romot r nom.wo th =: nom th + 1

;dnE :21 FI day < 13 NEHT t romo r d.wo ay =: day + 1

ESLE Be nig worromot .day =: 1;

romot r wo . nom th =: 1; FI raey = 2102

NEHT nletirW ( 2102' si evo r' ) ESLE romot row. aey r =: ye ra + 1

dnE ; :2 FI day < 82 NEHT romot r d.wo ay =: day + 1

ESLE FI day = 82 NEHT FI (( raey DOM )4 )0= DNA (( aey r DOM 400) 0>< )

NEHT romot r d.wo ay =: 92 el{ ap aey r} ESLE Begin nommoc{ year}

d.worromot ay =: ;1 romot r nom.wo th =: ;3

nE d ESLE FI day = 92

NEHT Be ig n romot r wo .day =: 1; romot r wo . htnom =: 3;

;dnE ESLE nletirw ( naC' n to ha ev Feb. ,' day ;)

(;dnE * ESAC m no th*) ;dnE HTIW*( t do a *y ) etirW ln romoT'( row s'' etad is', romot ro m.w on ht :3,

d.worromot a ,3:y tom ro r wo . )5:raey ; END. DtxeN*( a *et ) 1.4 ehT C simmo s noi melborP

ruO driht ex ma elp is erom t ipy ac l fo commercial .gnitupmoc tI c tno a sni a xim fo com tup a noit a dn d ce si i no ma ,gnik os ti sdael ot i tn ere gnits te gnits uq es it .sno 1.4.1 Pr bo el m tatS em ne t

iR fle s la se pe snosr in ht e nozirA a Te rr ti o yr dlos rifle loc sk , ots c sk , a dn ba rr els made yb a snug tim h in iruossiM . oL cks c so t 54$ . 00 , ots c sk c tso 03$ . 00 , a dn ba rr e sl c tso 52$ . .00 Sale ps e snosr had to s le l at el a ts no e c lpmo e et rifle per m no th, a dn tcudorp i no lim ti s are us ch ht ta ht e most no e as lespe nosr c uo ld lles ni a m no th si 07 loc sk , 08 stoc sk , a dn 09 ba rr e sl . aE ch rifle las e ps ers no sent a et le rg am ot the M ssi iruo c pmo a yn htiw the ot at l dro er f ro ea hc nwot (s eh) vi ;stis sale ps ers sno vi tis ta el a ts eno t nwo per m no th, ub t vart el d ffi ic lu eit s made ten snwot the ppu er timil . tA the e dn of e ca h ,htnom the c pmo a yn com tup ed mmoc issi sno as f :swollo %01 no s ela s pu ot 0001$ , %51 no ht e ne tx 008$ , a dn %02 no any as les ni e cx sse of 0081$ . hT e c pmo a yn had f ruo s ela ps e nosr s. hT e tele rg sma f or m each sale ps ers no were s tro ed into piles yb( per )nos a dn at ht e e dn of each htnom a da at f li e si

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.in rp epared, c tno aini gn eht sale ps e ’nosr s n ma ,e fol wol ed yb no e line f ro e ca h tele rg am dro e ,r s gniwoh

the number of loc sk , ots c sk , a dn ba slerr ni that dro er. tA ht e e dn of ht e as el s data nil es, there is an e yrtn of “-1” in ht e sop i noit hw ere the bmun er fo ol c sk uow ld be ot si ng al ht e e dn of i tupn f ro that salesperson. hT e rgorp am udorp ec s a m htno ly as sel re op rt ht at vig es ht e sale ps e s’nosr na em , t eh to at l un mber fo loc ,sk ts oc sk , a dn ba rr e sl os l ,d t eh sale ps ers s’no t to al od llar sales, a dn if na yll h /si her c mmo issi no . 1.5 ehT AS TM Sy mets

oT b ette r disc ssu ht e si su se of tni e rg ita no a dn s ety m te its gn , we en ed an exa lpm e htiw la gr er sc .epo hT e a amotu et d t lle er machi en described here si a refinement of t tah ni [ ppoT er ]39 ; ti c tno ai sn na

in et re gnits va ir e yt of nuf tc noi a il ty a dn tni e ar c snoit . Al hguoht it pyt fi ies er al-t mi e sys tem ,s rp ca noitit e sr ni ht e c mmo erci la PDE od iam n are f gnidni that even tradi noit al OC B LO ys ets sm ha ev am yn fo eht lborp ems usu a yll a icoss ated w ti h r ae l- emit tsys me .s

1.5.1 Pr bo el m tatS em ne t

hT e MTAS s sy et m c mo m cinu ta se htiw ba kn c otsu em sr iv a eht etfif en scr ee sn nwohs in rugiF e 2.4. gnisU a ter im nal w ti h fe ta ru es as nwohs in giF ure 1.3, MTAS c motsu e sr ac n s le ect a yn of t eerh

transa itc no pyt e :s de isop ts, wi rdht awals, a dn ba nal ec iriuqni es, a dn these ac n be nod e no t ow t py es of acc stnuo , c eh cki gn a dn savin sg . When a ba kn cu ts mo er a virr es at an S MTA ts ta noi , sc er en 1 si d si p al yed. hT e ba kn c motsu er acce ss es the TAS M s sy et m wi ht a p al s cit ca dr enc do ed iw th a Pers no al A cc nuo t bmuN er AP( N), ihw ch si a yek to an tni e nr al c su t remo cca nuo t fi el , c tno ia ni ,gn a gnom hto er thi sgn , t eh cu ts ome s’r an me a dn tnuocca ni f mro ati no . fI ht e c su t mo e s’r NAP matc seh the inf amro t noi ni eht otsuc em r acc tnuo f eli , eht s tsy em rp esen st sc er en 2 to eht c su to em r. If t eh c motsu e s’r NAP si on t f dnuo , sc er en 4 si di lps ayed, a dn the ac dr si ke tp . tA sc er en 2, ht e c remotsu si tpmorp ed ot e tn er h/sih er Pe nosr al dI entific ta i no bmuN er P( I )N . If ht e NIP is c rro tce (i. ,.e am tches ht e ni f mro ita no ni ht e c motsu er acc tnuo f li e), the sys et m lpsid a sy screen 5; o eht ,esiwr

sc er en 3 si lpsid ayed. hT e c motsu er has t rh ee hc ances ot g te the IP N c rro ect; after t rh ee fail seru , sc er en 4 si d si lp aye ,d a dn t eh ca dr si ke .tp

nO e yrtn ot scr ee n 5, eht tsys me a sdd t ow eip ec s of inf ro am noit ot the c su to em s’r a cc nuo t elif : t eh

c rru e tn etad , a dn an ni crement ot the un mber of MTA es iss sno . hT e c motsu er es el c st the de deris transa itc no f mor eht snoitpo nwohs no sc er en ;5 then t eh ys mets im em diate yl di lps a sy screen ,6

hw ere ht e c su to em r c sooh se ht e ca c tnuo ot ihw ch ht e eles cted rt a asn c noit liw l be a lpp ei d. fI ab l na ec si re uq e ts ed, ht e s tsy me c eh c sk ht e l co al MTA f li e f ro a yn tsopnu ed transa itc sno , a dn

rec no c li es seht e iw th eht begi nn i gn b ala cn e f ro ht ta day f or m the c motsu er a cc tnuo file. Sc er en 41 si then d si p al yed. If de tisop si re uq e ets d, ht e st uta s of the De sop it vnE el po e slot si deter im ned f or m

a fie dl ni ht e Te nimr la noC t lor iF el . If on borp l me si nwonk , the ys ets m lpsid ays cs er en 7 ot get t eh transa itc no a tnuom . If there is a lborp me htiw t eh de sop ti e vn e pol e ls to , the s tsy em id sp al sy sc er en 12. cnO e the de tisop nuoma t has eb en e tn ered, ht e tsys em lpsid ays sc neer 31 , accep st the de op sit e vn e epol , a dn corp e ss es ht e de op is t. hT e de isop t ma nuo t is e tn ered as an tsopnu ed a nuom t ni t eh local MTA f eli , a dn eht c nuo t of de stisop per m no th is ni cr eme tn ed. B hto of ht ese (a dn rehto inf ro m )noita are corp se sed yb the Ma ts er MTA c( entr ila ze )d tsys em ecno per day. hT e s tsy em ht en d si p al sy scr ee n 41 . If wi dht rawal is re uq ested, ht e tsys me c eh cks the sta sut (ja mm ed ro f er e) of t eh

rdhtiw awal c uh et ni ht e Te nimr la C no t lor F eli . fI ja emm d, sc er en 01 is di ps al yed, hto e wr si e, scre ne

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7 si d si lp ayed so eht c motsu er ac n e tn er t eh rdhtiw awal ma tnuo . nO ec ht e w ti rdh aw la ma tnuo si e tn ered, the sys et m c kceh s ht e Te mr inal tS atus F eli ot es e if ti has e hguon nom ey ot d si pe esn . If it od es ton , sc er en 9 si d si p al ye ;d othe siwr e ht e w ti rdh aw la si orp ec ss ed. hT e tsys me ch ce sk t eh

c motsu er lab ance (as sed cribed in ht e Ba al nce re uq est rt ansact )noi , a dn if ht ere a er usni ff icie tn f sdnu , sc er en 8 is yalpsid ed. fI the acc tnuo ba nal ce si fus ficie tn , sc er en 11 si di ps al yed, a dn t eh m no ey is psid e sn ed. hT e rdhtiw awal am nuo t si irw ett n to the tsopnu ed ol c la TA M file, a dn the c nuo t of rdhtiw awa sl per nom th ni ni creme tn ed. hT e lab a cn e si rp i tn ed no eht tra sn a tc i no cer e tpi sa ti is f ro a ab al n ec re uq e ts tra asn tc noi . tfA er ht e ca hs has eb en r me vo ed, ht e ys ets m lpsid ays sc neer

14. When ht e oN nottub si rp e ss ed in scr ee sn 01 , 21 , ro 41 , the tsys me rp esen st scr ee n 51 a dn ret snru ht e cu ts mo e s’r MTA ac dr . cnO e ht e ca dr si re vom ed f mor ht e ac dr tols , sc er en 1 is d si p al yed. When t eh Yes ub tt no si rp e ss ed ni sc er e sn 01 , ,21 ro 41 , ht e s sy et m rp esen st sc er en 5 os t eh cu ts mo er can se el ct idda it no al tra sn ca ti .sno 1.5.2 siD c noissu

hT ere si a gnisirprus ma nuo t of ni f ro am ti no irub“ ed” in ht e tsys me d cse ri tp i no j tsu vig en. roF in ats nce, if uoy read ti solc e yl , uoy ac n fni er aht t ht e et r im nal no ly c tno ains ten llod ar b li sl ees( sc er en .)7 sihT te utx la defini noit si borp a ylb rom e rp e ic es ht an hw at si usu la yl enc uo tn ered ni rp ca ecit . ehT e ax mple is d ile berately elpmis h( e ecn eht name .)

A lp te roh a of uq e ts i sno c uo ld be re vlos ed yb a l tsi of a snoitpmuss . roF exa elpm , si ht ere a

worrob ing l timi ? W tah kee sp a c motsu er f mor at gnik uo t more ht an sih ca ut la bala cn e if he og se to several MTA et rm ni a sl ? hT ere are ol ts of “ ts art pu ” uq se ti sno : woh m hcu cash si in ti i lla y ni t eh

cam enih ? woH are new c motsu e sr a dd ed ot the tsys em? hT e ,es a dn hto er “ er al row l ”d er f ni ement ,s a er e nimil ated to ma tni a ni ilpmis c .yti 1.6 utaS nr niW d leihs d repiW Con rellort

hT e hsdniw lei d wiper no ht e Sa nrut autom elibo (at lea ts no ht e 2991 dom le )s si c no t llor ed yb a el ver wi ht a id al. hT e vel er h sa f ruo snoitisop , FFO , TNI (f ro tni e imr tte )tn , OL W, a dn HGIH , a dn

the di la has erht e op is snoit , un mbered mis ply 1, 2, a dn 3. hT e id al op tis snoi i dn icate t eerh in et rm tti ent ps ee sd , a dn the id al tisop i no si rele tnav no ly hw en the el ver si at the TNI sop i noit . ehT d ce si i no ta lb e be wol swohs ht e hsdniw ield wiper spee sd (in piw es per m uni et ) f ro ht e el ver a dn di la

isop it no s.

Le ev r FFO TNI TNI TNI LOW GIH H iD al n/a 1 2 3 n a/ n/a

repiW 0 4 6 21 03 06

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UNIT 2

BOUN AD YR VALUE TSET ING, EQUI AV EL CN E CLA SS

TSET GNI , DECI IS NO TA ELB - BA ES D ET STI :GN 2.1 B yradnuo eulaV nA sisyla

roF ht e sake of c rpmo ehe lbisn e rd a gniw s, ht e disc noissu relates ot a f nu c noit , F, of t ow va ir ab el s x1 dna x2 . When ht e f nu tc noi F si mi p el me tn ed as a rgorp am, the upni t va ir a elb s x1 a dn x2 lliw ha ev

emos op( iss bly tsnu ate )d dnuob ar ei :s

nU f nutro ate yl , ht e tni e vr a sl [a, b] a dn [c, d] are er fe rr ed ot as ht e ra gn es of x1 a dn x2 , os hgir t awa ,y we have an vo e olr aded te mr . ehT i tn e dn ed m ae gnin iw ll a wl a sy eb c el ar morf i st c tno e tx . St gnor ly t py ed al ugn ages us( ch as dA a a dn Pas lac ) pe timr e px lic ti ed f tini i no of hcus var ai lb e ra gn es. nI fac ,t part of the sih t iro ac l er a nos f ro orts ng t ipy ng was to rp eve tn rp o rg a mm ers f or m maki gn the dnik of e srorr th ta re tlus in fa stlu that are ea is yl rev ae led yb dnuob ra y value et ts i gn . Other la ugn ages us( ch as C LOBO , NARTROF , a dn C) are on t gnorts ly t py ed, so dnuob a yr v la ue et gnits si more a rporpp ai te f ro rgorp a sm c do ed ni such al ugn a seg . hT e i tupn ps ace mod( a )ni of ruo f nu c noit F si

nwohs ni rugiF e 2. .1 ynA op i tn iw thin eht hs aded r ce ta elgn is a le mitig a et upni t to t eh f nu cti no .F B dnuo a yr v la ue ana sisyl foc su es no the dnuob a yr of the i upn t s cap e to di entify et st ac ses. ehT ra it lano e behi dn dnuob a yr v la ue te ts gni is ht ta e srorr et dn ot o cc ru n ae r ht e e rtx eme va ul es of an i tupn va ir able. hT e SU . ymrA (CEC )MO am de a yduts of ti s os tf wa er , a dn f dnuo ht ta a gnisirprus

rop t noi of fa stlu nrut ed uo t ot be dnuob a yr va ul e fa stlu . pooL c snoitidno , rof exa elpm , may te ts f ro < hw en they s dluoh te ts rof ≤, a dn c tnuo e sr often are “ fo f yb ”eno . ehT de ks t po gnihsilbup rgorp ma in ihw ch siht am sun c pir t was t py ed has an i retn e gnits dnuob a yr value borp el m. hT ere are owt m do es of et xtual lpsid a :y a cs or l gnil iv ew in hw ci h new pages are idni ac det by a ttod ed nil e, a dn a page iv ew ihw ch lpsid ays a full page i am ge s gniwoh hw ere the et tx si alp ec d no ht e pag ,e t go e ht er

htiw h ae ders a dn f oo et .sr If the c ru s ro is at ht e last line of a page a dn new te tx si a dd ed, an a mon a yl o cc :sru in ht e f ri ts m ,edo the new nil e )s( mis ply ppa ae r, a dn the od ett d l eni p( age rb ea )k si a tsujd e .d nI t eh pa eg lpsid ay ,edom woh eve ,r ht e new te tx si tsol — it od e t’ns a pp e ra no htie er page .

ugiF re 2.1 tupnI oD ma ni fo a nuF c noit fo owT Va ir a lb es

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hT e ba is c edi a of dnuob a yr va ul e an la sisy si ot su e tupni var ai lb e va seul at the ri im n mi mu , j tsu a vob e ht e muminim , a on m ni la va eul , j tsu eb l wo the ri am mumix , a dn at ht ie r ma ix m mu . hT ere is a commercia yll avai al lb e te gnits t loo (named )T aht t generat se us ch et ts ca es s f ro a porp erly ps ce ifi de

rgorp ma . hT is oot l has b ee n s cu ec s fs yllu tni e rg eta d w ti h t ow upop al r f nor t-e dn ESAC t oo ls T( mae krow f mor Ca erd syS et ,sm a dn oS ftwa er hguorhT iP ct ru se f mor tnI e ar ctive Dev le po m tne

vnE i mnor en st ). hT e T loot refe sr to ht ese v la ues sa min, im +n , mon , am x- a dn max; we iw ll su e siht c vno ent noi he er . hT e xen t pa tr fo dnuob a yr va ul e ana sisyl si sab ed no a c citir al a ss ;noitpmu s’ti

nwonk as ht e “ lgnis e af ult” a noitpmuss in re il abil ti y ht e yro . sihT sa sy that f lia ru es a er no ly rare yl the re lus t of the tlumis ane suo co c rru e cn e of owt ro( m ro e) fau tl s. suhT eht dnuob a yr va ul e ana sisyl et ts ac ses are tbo a ni ed yb idloh ng the va ul es of lla tub no e va ir ab el at ht e ri animon l v la ues, a dn el t it gn ht at var ai lb e sa mus e i st extreme v la ue .s hT e dnuob a yr va ul e an la y is s et ts cases f ro ruo

f tcnu i no F fo t ow va ir a lb es are:

x<{ mon1 , x nim2 ,> x< mon1 , x im2 n+ x<,> on1 m, x n2 om x<,> on1 m, x xam2 - ,>

x< mon1 , x xam2 ,> x< nim1 , x mon2 ,> x< 1min+, x on2 m ,> x< on1 m, x on2 m ,>

x< 1 xam -, x mon2 ,> x< 1m xa , x mon2 > }

hT ese a er i ll u ts ar et d in rugiF e 2. .2

ugiF re 2.2 B dnuo a yr aV l eu nA la sisy Te ts Cases rof a nuF cti no fo owT raV iab el s 2.1.1 Gen re zila i gn B adnuo ry ulaV e sisylanA

hT e ba is c dnuob a yr value ana sisyl et c uqinh e c na be generalized in t ow way :s yb the un mber fo va ir ab el s, a dn yb eht ki sdn of ra gn e .s Genera zil i gn the bmun er of va ir ab el s is ae ys : if ew have a f tcnu i no of n var ai lb es, we oh ld lla ub t no e ta ht e on im lan va ul es, a dn let ht e er am gnini var ai lb e a muss e ht e nim , +nim , mon , max- a dn max v la ues, a dn re ep ta th si f ro each va ir able. uhT s f ro a f tcnu i no of n va ir ab el ,s dnuob a yr v la ue ana yl is s y ei sdl n4 + 1 et st c sa es. Gener ila gniz ra gn es depe sdn no ht e rutan e ro( m ro e rp ice se yl , ht e pyt e) of ht e var ai lb es eht esm vl es. nI the Ne Dtx a et f tcnu i no , f ro exa elpm , we have va ir a selb f ro eht m no t ,h ht e da ,y a dn the y ae r. nI a OF RTR NA -like al ugn age, we uow ld som t l ki le y enc do e the es , os ht ta Ja un a yr dluow c rro e dnops to 1, Fe urb a yr to ,2

a dn os no . nI a la ugn age ht ta stroppus su er de denif t py es l( ike Pa cs la ,) we c dluo de nif e the va ir a elb m no th as an e mun erated pyt e J{ a .n , Fe .b , . . . , Dec.}. htiE er way, ht e va ul es f ro nim , +nim , ,mon

am x- a dn max a er c el ar f mor the c no et tx . W neh a var ai lb e has sid cre et , dnuob ed va ul e ,s as t eh va ir ab sel in the c noissimmo lborp me have, the ,nim m +ni , mon , max- a dn max are a osl ea ylis dete nimr ed. When the er are on e px il tic ,sdnuob as in ht e rt ai gn el lborp e ,m we usu ally have ot crea et “a itr ficial” dnuob s. hT e wol er dnuob of side le shtgn si cl ae rly 1 (a nega vit e is de le gn th si is )yll , ub t

hw ta thgim we od for an ppu er dnuob ? By default, ht e la gr est re rp ese tn a lb e tni eger ( llac de TNIXAM ni s mo e la ugn age )s si no e ssop i ib il yt , ro we thgim im sop e an rtibra a yr ppu er l timi us ch

as 002 ro .0002

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.in B dnuo a yr v la ue ana sisyl od es t’n make much se sn e f ro B eloo an va ir a lb e ;s the ext er em va ul es a er

EURT a dn FA ESL , tub there is on cl ae r c cioh e rof the rema gnini rht ee. eW will es e in hC a tp er 7 that B oo lean va ir ab el s el dn th me se vl es ot de ic is no tab el - sab ed te .gnits Log aci l va ir a lb es a osl rp ese tn a lborp em f ro dnuob a yr v la ue a sisylan . nI ht e TA M exam ,elp a cu ts mo e s’r Pers no al dI e tn i cif ati no bmuN er )NIP( si a l igo c la var ai elb , as is the tra sn acti no epyt (de tisop , wi rdht awa ,l ro

i yriuqn ). We c dluo “ og guorht h ht e tom i sno ” of dnuob a yr va ul e ana syl is te gnits f ro us ch va ir a lb e ,s tub eht exer sic e is ton yrev sa it sf gniy to eht t“ est s’re .”noitiutni

2.1.2 L snoitatimi fo uoB n ad ry eulaV anA ly is s B dnuo a yr va ul e ana sisyl krow s we ll hw en t eh rp o rg am ot be te ets d si a f nu c noit of several i dn epe dn e tn va ir a lb es ht at re rp ese tn dnuob ed syhp laci uq ant iti es. hT e key drow s here a er i dn epe dn ent a dn isyhp c la nauq tit ies. A uq ci k l koo at ht e dnuob a yr va ul e a ylan is s et ts ac ses f ro NextDate ni( es c noit 5. )5 swohs ht me ot be inade uq a et . hT ere si ve yr il tt le sserts no Fe urb a yr a dn no l ae p ey a sr , f ro exa elpm . hT e real lborp em here si ht at there are i tn ere gnits depe dn encies ma gno

the m no th, day, a dn ye ra var ai lb es. dnuoB a yr va eul ana sisyl rp e mus es ht e var ai lb es to be t ylur i dn epe dn ent. vE en os , dnuob a yr v la ue anal sy si ha pp e sn ot catch e dn - fo - htnom a dn e dn -of-year af u tl s. B dnuo a yr v la ue na al sy si te ts cases are vired ed f or m ht e ext er am of dnuob ed, i dn epe dn e tn

va ir ab sel t tah refer to isyhp cal uq anti eit s, w ti h on c disno e ar t noi of ht e na rut e of ht e f nu ct noi , ron of the semantic em a gnin of the var ai lb e .s We es e dnuob a yr v la ue ana yl is s te ts cases to eb midur en at ,yr in the es sn e that they are tbo a ni ed iw th ve yr l ti elt i hgisn t a dn i am gin ita .no As w ti h so ma yn thin ,sg

uoy g te hw at uoy pay f .ro

hT e isyhp ac l uq a tn i yt c tir e noir si e uq a yll ropmi t tna . When a var ai lb e er f sre to a cisyhp al uq an it ,yt us ch as t me pera erut , rp e russ e, ia r eps ed, a lgn e of ta t ca k, ol ad, a dn so f ro t ,h isyhp lac b dnuo a ir es can

be extre em ly mi trop a .tn nI( an in et re ts i gn e pmax el of siht , ykS Ha obr r tnI e nr ita no al Ai ropr t in ohP enix had ot cl eso no nuJ e 62 , 2991 eb ac su e ht e air te pm er ta ru e was 221 F° . Aircraft pi stol we er

nu ab el to make cer at ni urtsni em tn s te sgnit bef ro e at ke- fo f: ht e ni ts mur en st c uo ld no ly ca ce tp a am mumix ria te pm era rut e fo 021 F° .) nI a hton er ca es , a me id c la ana syl si tsys me u es s ts e repp

mot sro ot op is noit a ca suor el of s ma lp es ot be ana zyl ed. tI snrut tuo ht at ht e em cha in cs of gnivom the ca suor el ab ck ot ht e ts ar it gn ec ll often ca su es ht e obor t arm ot m ssi ht e f ri ts cell. sA an ex ma elp of gol aci l v( e susr yhp acis )l var ai lb es, we hgim t kool at NIP s ro tele nohp e bmun e sr . s’tI ha dr to

ami enig hw at fa lu ts mi hg t eb revea el d yb NIP s fo ,0000 ,1000 05 ,00 ,8999 a dn .9999 2.2 R bo u ts n sse T gnitse R ntsubo e ss te gnits is a s mi p el e tx e sn i no of dnuob a yr va ul e ana yl is :s in a dd i noit to ht e fi ev

dnuob a yr value anal sy si va ul es of a var ai elb , we s ee hw ta ha pp e sn hw en ht e e rtx ema are e cx ee ded htiw a va ul e hgils ylt rg etae r than ht e ma mumix m( a )+x a dn a va ul e sl hgi t yl le ss ht an ht e muminim nim( -). ntsuboR e ss te ts ca es s f ro ruo c no t uni i gn xe ma elp a er nwohs in Fi rug e 5. .3

tsoM of ht e disc noissu of dnuob a yr va ul e a an l sy si a ilpp es dir ce ylt ot bor u nts e ss set it gn , espe aic ll y

the gener la azi t snoi a dn imil t snoita . hT e mo ts tni eres it gn pa tr of ssentsubor t tse i gn si ton iw th t eh i ,stupn ub t wi ht ht e pxe ce ted stuptuo . What ha pp ens hw en a yhp cis al uq a ytitn e cx ee sd sti

am mumix ? If it is ht e lgna e of a att ck of an a pri al ne w gni , the a ri c ar ft hgim t stall. If ti s’ the daol capa ytic of a bup lic elev ta ro , we poh e on thi gn sp ce ial dluow ha pp en. If it s’ a d ta e, kil e May 23 , we

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uow ld e epx tc an e rorr em ss a eg . hT e m ia n va ul e of subor tne ss tes it gn si ht ta ti f ro ces a tt ent noi no e cx ept noi ha dn l gni . iW ht st gnor ly t py ed la ugn ages, subor tne ss testi gn may be v re y a wkw a dr . nI Pasc la , f ro exa elpm , fi a va ir able is ed f ni ed to be nihtiw a ce tr a ni ra gn e, values stuo ide ht at ra egn resu tl in nur - mit e e srorr ht ta a rob t ron lam ex ce noitu . hT is r ia ses an tni ere gnits uq tse noi fo mi p el me tn a noit :yhposolihp si ti b te ter ot perf mro e ilpx c ti ra gn e c eh c gnik a dn su e e cx e noitp

ha dn l gni to de la htiw “ tsubor v la ue ”s , ro si it be tt er ot ts ay htiw ts gnor pyt i gn ? ehT e cx e noitp ha dn l gni c ecioh ma etadn s ntsubor se s t tse .gni

ugiF re 2.3 R ubo ts ne ss Test C esa s f ro a tcnuF i no fo owT Var ai lb es 2.3 roW st C esa T gnitse B dnuo a yr v la ue an la ,sisy as we s ia d ae ilr er, ma sek the is lgn e fa lu t a noitpmuss of r le ia lib ti y ht e .yro R je e gnitc siht a uss mpt noi aem ns ht at we are tni er se ted ni hw at ha snepp hw en rom e aht n eno va ir ab el has an e rtx eme va ul e. nI ele c nort ci c ri cuit a sisylan , siht si called “ row ts ac se an la ;”sisy ew

su e ht at edi a he er ot ge ren a et tsrow case te ts ca ses . roF ae ch var ai elb , we ts art iw th eht vif e e el ment set that c atno ins ht e ,nim im +n , mon , max- a dn max va ul es . We ht en at ke ht e Ca tr e is an tcudorp ees( Cha tp er )3 fo ht e es es st to gene tar e test cases. hT e re lus t of the owt -var ai lb e ve noisr of ht si si

nwohs ni Fi rug e 2.4. tsroW ac se te gnits si cl ae ylr m ro e t oroh u hg in ht e sen es th ta dnuob a yr v la eu an la sisy test cases are a porp er sbus et of srow t case te ts cas se . It a osl re rp esents um ch rom e fe f tro :

srow t ac se set it gn f ro a nuf cti no of n va ir a lb es generates 5n test cases, as soppo ed ot 1+n4 test c sa se f ro dnuob a yr v la eu a .sisylan W tsro ac se te ts i gn f lo l wo s ht e genera zil a noit p ta nret we saw f ro dnuob a yr v la ue anal sy is. tI a osl has the as em li im t ita sno , pa itr c lu arly t oh es related ot dni epe dn e cn e. borP a ylb ht e best a cilpp a noit f ro

srow t ac se te gnits is rehw e isyhp cal va ir a lb es have un em uor s tni erac snoit , a dn hw ere f lia ru e of the f tcnu i no si extr me yle c so t yl . roF r llae y para dion te ,gnits we c dluo og to tsubor row ts ca es set it .gn

sihT i ovn lves the aC r et is an udorp tc of ht e seven el me ent sets we u es d in ntsubor e ss t se ti gn . Figu er 2.5 swohs eht tsubor srow t ca es te ts ca es s f ro ruo owt va ir a elb f nu itc .no 2.4 laicepS eulaV T nitse g

pS eci la va eul te gnits si borp ably ht e tsom wi ed ly rp tca ci ed f mro of f nu tc noi al t itse .gn tI a osl si t eh m so t vitiutni e a dn t eh el a ts inu f mro . epS cial v la ue te gnits co c sru hw en a te ts er su es sih /her amod in

wonk el gd e, e px e ir e cn e iw th mis ilar rgorp a sm , a dn ni f mro ita no a tuob os“ ft stops ” ot de siv e te ts cases. We thgim la os ac ll siht “ad oh c t tse gni ” ro “se ta of ht e pan st /s trik ” et ts i gn . hT ere are on

diug e il nes, other ht an to su e “be ts e nign eeri gn j gdu em .tn ” As a r se ul ,t ps ce ial v la eu et ts i gn si ve yr depe dn e tn no eht a tilib i se fo t eh tes et .r

ugiF re 2.4 W ro st Ca es Test Cases f ro a tcnuF i no fo owT Var ai lb es

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ugiF re 2.5 R ubo st W sro t Ca es Te ts Cases f ro a nuF itc no fo owT Var ai elb s Despite a ll the a pp are tn nega vit es, eps c ai l value te ts i gn can be ve yr su fe lu . nI ht e ne tx es itc no , uoy

liw l fi dn te ts cases ge ren a et d yb ht e em t sdoh ew j tsu sid cussed f ro ruo rht ee tinu lev le exam lp es on( t ht e MTA tsys me ). fI uoy kool ac er f yllu at t eseh , e eps c lai ly f ro ht e Ne Dtx eta f nu tc noi , uoy f dni

that non e is ve yr sa afsit tc o yr . If an tni ere ts ed tset er ed fined s ep c ai l va ul e test c sa es f ro NextDa et , we uow ld es e several test cases i vlovn i gn Fe urb a yr 82 , Fe urb a yr ,92 a dn l ae p y ae sr . vE en guoht h ps e ic al v la ue tes it gn si hgih ly bus jec evit , ti tfo en re stlus ni a tes fo test cases ihw ch si rom e

e ff ec vit e ni rev ae li gn af stlu than the test s te s neg erated yb the other me doht s we have duts ei d— et ynomits to eht c ar ft fo os ftwa er te its .gn

2.5 Ex ma p sel E ca h of the t erh e c unitno i gn ex ma p el s si a f noitcnu of t rh ee var ai lb es. nirP it gn all ht e te ts ac ses f or m all ht e met sdoh rof each melborp si ve yr ps a ec c no s mu i gn , os we’ ll j tsu have s le ec et d ex ma lp se . 2.5.1 Te ts saC es f ro ht e Tr elgnai Pr lbo em nI ht e elborp m ts ateme tn , ht ere are on c tidno i sno no eht rt ai lgn e side ,s hto er than nieb g tni ege .sr

vbO i suo ly, the l wo er sdnuob of the ra gn es are lla 1. We a rtibr a ylir at ke 002 sa an ppu er .dnuob Table 1 c no t snia yradnuob va ul e te ts ac ses a dn Table 2 c no t snia srow t ca es t tse ac ses su i gn ht ese ra gn es .

elbaT 1 yradnuoB eulaV sisylanA tseT sesaC esaC

a b c detcepxE tuptuO

1 01 0 001 1 csosI e el s

2 01 0 001 2 csosI e el s

3 01 0 001 01 0 laretaliuqE

4 01 0 001 91 9 csosI e el s

5 01 0 001 02 0 toN a elgnairT

6 01 0 1 01 0 csosI e el s

7 01 0 2 01 0 csosI e el s

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8 01 0 001 01 0 laretaliuqE

9 01 0 991 01 0 csosI e el s

10 01 0 002 01 0 toN a elgnairT

11 1 001 01 0 csosI e el s

12 2 001 01 0 csosI e el s

13 01 0 001 01 0 laretaliuqE

14 91 9 001 01 0 csosI e el s

15 02 0 001 01 0 toN a elgnairT

elbaT 2 tsroW esaC tseT sesaC saC e

a b c detcepxE tuptuO

1 1 1 1 laretaliuqE

2 1 1 2 toN a elgnairT

3 1 1 01 0 toN a elgnairT




7 1 2 2 csosI e el s






13 1 001 01 0 csosI e el s


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19 1 991 91 9 csosI e el s




23 1 002 01 0 toN a irT elgna


25 1 002 02 0 csosI e el s







32 2 2 2 laretaliuqE






38 2 001 01 0 csosI e el s

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39 2 001 91 9 toN a nairT elg





44 2 991 91 9 csosI e el s

45 2 991 02 0 nelacS e




49 2 002 91 9 nelacS e

50 2 002 02 0 csosI e el s



53 01 0 1 01 0 csosI e el s





58 01 0 2 01 0 csosI e el s



61 01 0 001 1 csosI e el s

62 01 0 001 2 csosI e el s

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63 01 0 001 01 0 laretaliuqE

64 01 0 001 91 9 csosI e el s

65 01 0 001 02 0 toN a elgnairT

66 01 0 991 1 toN a airT elgn


68 01 0 991 01 0 csosI e el s

69 01 0 991 91 9 csosI e el s

70 01 0 991 02 0 nelacS e



73 01 0 002 01 0 toN a elgnairT

74 01 0 002 91 9 nelacS e

75 01 0 002 02 0 csosI e el s




79 91 9 1 91 9 nelacS e

80 91 9 1 02 0 oN t a elgnairT




84 91 9 2 91 9 csosI e el s

85 91 9 2 02 0 nelacS e


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88 91 9 001 01 0 csosI e el s

89 91 9 001 91 9 csosI e el s

90 91 9 001 02 0 nelacS e

91 91 9 991 1 csosI e el s

92 91 9 991 2 csosI e el s

93 91 9 991 01 0 csosI e el s

94 91 9 991 91 9 laretaliuqE

95 91 9 991 02 0 csosI e el s


97 91 9 002 2 nelacS e

98 91 9 002 01 0 nelacS e

99 91 9 002 91 9 csosI e el s

001 91 9 002 02 0 csosI e el s





501 02 0 1 02 0 csosI e el s




901 02 0 2 91 9 nelacS e

011 02 0 2 02 0 csosI e el s

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311 02 0 001 01 0 toN a elgnairT

411 02 0 001 91 9 nelacS e

511 02 0 001 02 0 csosI e el s


711 02 0 991 2 nelacS e

811 02 0 991 01 0 nelacS e

911 02 0 991 91 9 csosI e el s

021 02 0 991 200 csosI e el s

121 02 0 002 1 csosI e el s

221 02 0 002 2 csosI e el s

321 02 0 002 01 0 csosI e el s

421 02 0 002 91 9 csosI e el s

521 02 0 002 02 0 laretaliuqE

2.5.2 Te ts saC es f ro ht e Ne taDtx e Pr bo el m

elbaT 3 tsroW esaC tseT sesaC saC e

m htno yad year detcepxe tuptuo

1 1 1 2181 yraunaJ ,2 2181

2 1 1 3181 yraunaJ ,2 3181

3 1 1 2191 yraunaJ ,2 2191

4 1 1 02 11 yraunaJ ,2 1102

5 1 1 2102 yraunaJ ,2 2102

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6 1 2 2181 yraunaJ ,3 2181

7 1 2 3181 yraunaJ ,3 3181

8 1 2 2191 yraunaJ ,3 2191

9 1 2 1102 yraunaJ ,3 1102

10 1 2 2102 yraunaJ ,3 2102

11 1 15 2181 yraunaJ ,61 2181

12 1 15 3181 yraunaJ ,61 3181

13 1 15 2191 yraunaJ ,61 2191

14 1 15 1102 yraunaJ ,61 1102

15 1 15 2102 yraunaJ ,61 2102

16 1 30 2181 yraunaJ ,13 2181

17 1 30 3181 yraunaJ ,13 3181

18 1 30 2191 yraunaJ ,13 2191

19 1 30 1102 yraunaJ ,13 1102

20 1 30 2102 yraunaJ ,13 2102

21 1 31 2181 yraurbeF ,1 2181

22 1 31 3181 yraurbeF ,1 3181

23 1 31 2191 yraurbeF ,1 2191

24 1 31 1102 yraurbeF ,1 1102

25 1 31 2102 yraurbeF ,1 2102

26 2 1 2181 yraurbeF ,2 2181

27 2 1 3181 yraurbeF ,2 3181

28 2 1 2191 yraurbeF ,2 2191

29 2 1 1102 yraurbeF ,2 1102

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30 2 1 2102 yraurbeF ,2 2102

31 2 2 2181 yraurbeF ,3 2181

32 2 2 3181 yraurbeF ,3 3181

33 2 2 2191 yraurbeF ,3 2191

34 2 2 1102 yraurbeF ,3 1102

35 2 2 2102 yraurbeF ,3 2102

36 2 15 2181 yraurbeF ,61 2181

37 2 15 3181 yraurbeF ,61 1 318

38 2 15 2191 yraurbeF ,61 2191

39 2 15 1102 yraurbeF ,61 1102

40 2 15 2102 yraurbeF ,61 2102

41 2 30 2181 rorre

42 2 30 3181 rorre

43 2 30 2191 rorre

44 2 30 1102 rorre

45 2 30 2102 rorre

46 2 31 2181 rorre

47 2 31 3181 rorre

48 2 31 2191 rorre

49 2 31 1102 rorre

50 2 31 2102 rorre

51 6 1 2181 enuJ ,1 2181

52 6 1 3181 enuJ ,1 3181

53 6 1 2191 enuJ ,2 2191

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54 6 1 1102 enuJ ,2 1102

55 6 1 2102 enuJ ,2 2102

56 6 2 2181 enuJ ,3 2181

57 6 2 3181 enuJ ,3 3181

58 6 2 2191 enuJ ,3 2191

59 6 2 1102 enuJ ,3 1102

60 6 2 2102 enuJ ,3 2102

61 6 15 2181 enuJ ,61 2181

62 6 15 3181 enuJ ,61 3181

63 6 15 2191 enuJ ,61 2191

64 6 15 1102 enuJ ,61 1102

65 6 15 2102 enuJ ,61 2102

66 6 30 2181 yluJ ,13 2181

67 6 30 3181 yluJ ,13 3181

68 6 30 2191 yluJ ,13 2191

69 6 30 1102 yluJ ,13 1102

70 6 30 2102 yluJ ,13 2102

71 6 31 2181 rorre

72 6 31 3181 rorre

73 6 31 2191 rorre

74 6 31 1102 rorre

75 6 31 2102 rorre

76 11 1 2181 rebmevoN 2, 2181

77 11 1 3181 rebmevoN ,2 3181

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78 11 1 2191 rebmevoN ,2 2191

79 11 1 1102 rebmevoN ,2 1102

80 11 1 2102 rebmevoN ,2 2102

81 11 2 2181 rebmevoN ,3 2181

82 11 2 3181 rebmevoN ,3 3181

83 11 2 2191 rebmevoN ,3 2191

84 11 2 1102 rebmevoN ,3 1102

85 11 2 2102 rebmevoN ,3 2102

86 11 15 2181 rebmevoN ,61 2181

87 11 15 3181 rebmevoN ,61 3181

88 11 15 2191 rebmevoN ,61 2191

89 11 15 1102 rebmevoN ,61 1102

90 11 15 2102 rebmevoN ,61 2102

91 11 30 2181 rebmeceD 1, 2181

92 11 30 3181 rebmeceD ,1 3181

93 11 30 2191 rebmeceD ,1 2191

94 11 30 1102 rebmeceD ,1 1102

95 11 30 2102 rebmeceD ,1 2102

96 11 31 2181 rorre

97 11 31 3181 rorre

98 11 31 2191 rorre

99 11 31 1102 rorre

001 11 31 2102 rorre

101 12 1 2181 rebmeceD ,2 2181

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201 12 1 3181 rebmeceD ,2 3181

301 12 1 2191 rebmeceD ,2 2191

401 12 1 102 1 rebmeceD ,2 1102

501 12 1 2102 rebmeceD ,2 2102

601 12 2 2181 rebmeceD ,3 2181

701 12 2 3181 rebmeceD ,3 3181

801 12 2 2191 rebmeceD ,3 2191

901 12 2 1102 rebmeceD ,3 1102

011 12 2 2102 rebmeceD ,3 2102

111 12 15 2181 rebmeceD ,61 2181

11 2 12 15 3181 rebmeceD ,61 3181

311 12 15 2191 rebmeceD ,61 2191

411 12 15 1102 rebmeceD ,61 1102

511 12 15 2102 rebmeceD ,61 2102

611 12 30 2181 rebmeceD ,13 2181

711 12 30 3181 eceD rebm ,13 3181

811 12 30 2191 rebmeceD ,13 2191

911 12 30 1102 rebmeceD ,13 1102

021 12 30 2102 rebmeceD ,13 2102

121 12 31 2181 yraunaJ ,1 3181

221 12 31 3181 yraunaJ ,1 4181

321 12 31 2191 yraunaJ ,1 3191

421 12 31 1102 yraunaJ ,1 2102

521 12 31 2102 yraunaJ ,1 3102

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.in 2.5.3 Te ts saC es f ro ht e Co ssimm i no Pr bo el m

aR ht er than og t orh ugh 521 rob ing et ts ac ses aga ni , we’ ll kool at os em rom e in et re ts i gn et ts ac ses f ro the co mm noissi borp el m. sihT emit we ll’ l koo at dnuob a yr v la seu f ro t eh uo t up t ra gn e, e eps cially en ar ht e rht e ohs ld stniop fo 0001$ a dn .0081$ ehT tuptuo aps ce fo the c noissimmo si

nwohs in Fi rug e 5.6. ehT in et cr ep st of ht ese t rh e dlohs lp anes wi ht ht e axes are nwohs . hT e ov l emu be wol ht e l wo er p al ne c rro e sdnops ot as les b le wo the 0001$ rht e ohs ld . ehT mulov e be ewt en the t ow p al sen si ht e %51 c ssimmo i no ra gn e. Part of ht e r ae nos f ro gnisu the tuptuo ra gn e ot d ete r im ne te ts cases si ht at cases f mor eht i tupn ra gn e are almo ts la l ni eht %02 enoz . We wa tn ot f dni i tupn va ir ab el c nibmo ita no s th ta rts e ss the dnuob a yr va ul e :s 001$ , 0001$ , 0081$ , a dn 0087$ . hT ese te ts cases we er deve pol ed htiw a s erp adsh ee ,t hw ci h as ves a lot of ca cl lu ta ro p ce gnik . hT e mi mumin a dn max mi mu were ae ys , a dn eht bmun e sr ha nepp ot krow tuo os ht at eht drob er op i stn a er ea ys ot generate. Here s’ hw ere ti g te s tni ere .gnits Test c esa 9 si ht e 0001$ redrob op int. If ew ewt ak t eh i tupn va ir ab el s we get va eul s j su t be wol a dn j tsu a vob e the drob er ( esac s 6 - 8 a dn 10 - 12). If ew wan et d ot , we c uo ld ip ck v la ues n ae r eht tni e cr e tp s ekil 22( , 1, )1 a dn 12( , 1, )1 . sA we c no it un e ni this way, we have a se sn e that we are e“ xer ic si gn ” in et res it gn pa str of the c edo . We m gi th claim that siht si er ally a f mro fo s ep cial va ul e et gnits , eb ac su e we su ed ruo am ht ema it ac l sni i thg ot genera et te ts case .s 2.6 Guid ile n se f ro B yradnuo eulav T gnitse W hti ht e e cx ep it no of ps ce ial value tes it gn , eht te ts met sdoh b esa d no the dnuob a yr va ul es of a f tcnu i no rgorp( ma ) a er the tsom midur e tn a yr of la l f nu itc no al t gnitse tem .sdoh hT ey share t eh com nom a noitpmuss taht the i tupn va ir ab el s are urt ly i dn epe dn e ,tn a dn hw en siht a uss m tp i no is ton wa rr a tn ed, the me sdoht generate snu at si af c yrot et st cases us( ch as Fe urb a yr 13 , 2191 f ro NextDate .)

hT e es m te sdoh have owt other di nits itc sno : mron al ve susr subor t va ul es, dna the si lgn e af ult ve susr the m lu it lp e fau tl ssa tpmu i .no tsuJ u is gn these cnitsid t noi s caref lu ly iw ll re tlus in b te ter test .gni E ca h of ht ese tem sdoh can be a ilpp ed to t eh tuptuo ra gn e of a rp o rg ma , as we did f ro t eh com noissim borp el .m

onA ther esu f lu f mro fo uo t up t-ba es d test cases si f ro tsys e sm ht at genera et e rorr m se sages. ehT et ts er uohs ld de siv e te ts ac ses ot c eh ck t ah t e rorr me ss ages are gene ar et d hw en ht ey are a rporpp i eta ,

a dn are ton laf se yl generated. B dnuo a yr v la ue ana sisyl ac n al os be su ed rof in et nr la var ai lb es, hcus as pool c ortno l var ai lb es, idni c se , a dn tniop e sr . St ir c ylt ps ae ki gn , ht ese are n to tupni var ai lb es, tub e srorr ni ht e su e of ht ese var ai lb es are tiuq e c nommo . R ntsubo e ss te gnits si a doog c oh ice f ro te gnits in et lanr va ir a lb e .s

ugiF re 2.6 tupnI pS a ec fo eht C noissimmo lborP em

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elbaT 4 tuptuO yradnuoB eulaV sisylanA tseT sesaC esaC

kcol s cots sk lerrab s as les moc m mmoC e tn

1 1 1 1 01 0 10 tuptuo muminim

2 1 1 2 21 5 5.21 tuptuo muminim +

3 1 2 1 31 0 13 tuptuo nim mumi +

4 2 1 1 41 5 5.41 tuptuo muminim +

5 5 5 5 05 0 50 tniopdim

6 10 10 9 79 5 5.79 redrob tniop -

7 10 9 01 79 0 97 redrob tniop -

8 9 10 01 59 5 5.59 redrob tniop -

9 10 10 01 0001 01 0 redrob niop t

10 10 10 11 5201 7.301 5 redrob tniop +

11 10 11 01 0301 .401 5 redrob tniop +

12 11 10 01 5401 7.601 5 redrob tniop +

13 14 14 41 0041 61 0 tniopdim

14 18 18 71 5771 2.612 5 redrob tniop -

15 18 17 81 0771 .512 5 redrob tniop -

16 17 18 81 5571 2.312 5 redrob tniop -

17 18 18 81 0081 22 0 redrob niop t

18 18 18 91 5281 22 5 redrob tniop +

19 18 19 81 0381 22 6 redrob tniop +

20 19 18 81 5481 22 9 redrob tniop +

21 48 48 84 0084 28 0 tniopdim

22 70 80 98 5777 5141 tuptuo mumixam -

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23 70 79 09 0777 4141 tuptuo mumixam -

24 69 80 09 5577 1141 tuptuo mumixam -

25 70 80 09 0087 0241 tuptuo mumixam

elbaT 5 tuptuO laicepS eulaV tseT sesaC saC e

kcol s cots sk lerrab s las se moc m mmoC e tn

1 10 11 9 001 5 7.001 5 redrob tniop +

2 18 17 19 971 5 2.912 5 redrob tniop -

3 18 19 17 081 5 22 1 redrob tniop +

2.7 qE u ecnelavi C sessal

eW ton ed taht the im rop at tn a eps tc of e uq iva nel ce cla ss es si t ah t ht ey f ro m a parti it no of a se ,t hw ere partit noi fer e sr to a c lo le tc noi of m utu ally disj tnio sbus ste sohw e noinu si the en it re set. ihT s

has owt im trop ant cilpmi a snoit f ro te gnits : eht caf t ht at eht en it re set is re rp ese tn ed vorp ides a f mro of com lp etene ss , a dn the sid jointne ss ass ru es a f ro m of non -re dnud ancy. Beca su e ht e sbus e st a er dete nimr ed yb an e uq ivale cn e rela noit , the ele stnem of a tesbus have mos ethi gn ni co mm no . ehT idea fo e viuq ale cn e c ssal t se ti gn si ot di ent yfi et st ca es s yb gnisu no e le eme tn f mor each e uq ivalence c al .ss If eht e viuq ale cn e clas es s a er c soh en siw e yl , siht rg ae ylt re cud es ht e op te tn i la re dnud ancy a gnom et st ac ses. nI the rT ai lgn e lborP em, f ro exam elp , we dluow ec tr ia nly have a te ts case f ro an e uq ilateral irt a lgn e, a dn we im thg pi kc the t lpir e 5( , 5, )5 as stupni f ro a te ts ac es . fI ew did ,siht ew dluow ton pxe ect to el a nr much f or m te ts cases us ch as 6( , 6, )6 a dn ,001( 001 , .)001

ruO tiutni i no tel sl us ht at ht ese dluow eb “ rt ea et d the sa em ” as ht e if r ts te ts ac es , t suh ht ey dluow eb re dnud ant. When we c disno er st ur tc laru t tse gni ni Part III , we hs la l ees ht at t“ er ated ht e emas ” am sp

otno “ rt ave gnisr t eh emas ex ce tu i no .”htap

hT e key (a dn the c ar ft )! of e viuq ela nce cl sa s et gnits is ht e c oh ice of ht e e viuq a el nce rel ita no aht t dete nimr es the cla ss e .s Ve yr often, ew ma ek siht c cioh e yb “ es c dno ug e gniss ” the il ke yl mi p el me tn a ,noit a dn kniht i gn a uob t eht f nu cti no al am lupin ati no s ht at m tsu mos e woh be serp e tn ni

the lpmi emen at noit . eW iw ll li rtsul ate this htiw ruo c gniunitno exa lpm es, ub t f tsri , we n ee d to ma ek a nitsid tc noi be wt ee n ew ak a dn gnorts e viuq a el cn e cla ss test gni . After that, we iw ll c pmo are the es to eht rt a noitid al f mro fo e uq iv la ence cl sa s tes it .gn

soppuS e ruo rp o rg ma si a f nu itc no of t rh ee vari lba es, a, b, a dn c, a dn t eh i upn t mod a ni c stsisno of se st A, B, a dn C. woN , soppus e ew c sooh e an a“ rporpp tai e” e viuq ela nce ler a noit , ihw ch i cudn es t eh f gniwollo pa titr i :no

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.in Finall ,y ew de on te ele nem ts fo t eh pa titr i sno as f lo l swo : 2.7.1 We ka uqE i av el ecn Cl ssa Te gnits W hti the aton it no as given a vob e, w ae k e uq ivale cn e cla ss te gnits si ca compl hsi ed yb u is gn eno va ir ab el f or m ae ch e viuq ale cn e class in a test ac es . roF ht e a vob e exa elpm , we dluow e dn pu w ti h t eh f wollo i gn ew ak e uq ivale ecn cl sa s t se t case :s

esaC tseT a b c

1EW 1a 1b 1c

2EW 2a 2b 2c

3EW 3a 3b 3c

4EW 1a 4b 2c

sihT es t of te ts cases sesu no e v la ue f mor each e viuq ela cn e cla ss . We di e itn fy ht ese ni a s tsy em ta ci way, he cn e the a pp are tn pa tt e nr . nI fa tc , we iw ll wla a sy have the sa em bmun er of ew ak e uq ivale ecn c al ss et st cases as the er ra e cla ss es ni t eh pa itr it no iw th eht la gr e ts bmun er fo s esbu t .s 2.7.2 tS r gno aviuqE el ecn Cl ssa Te gnits St or ng e viuq alen ec lc a ss te gnits si b esa d no ht e aC tr e is an udorP ct of ht e parti it no sbus e .st C no it un i gn htiw siht example, ht e Ca tr es ai n udorP tc A x B x C lliw have 3 × 4 × 2 = 24 ele ment ,s resu tl gni ni t eh te ts ac ses ni t eh ta elb be :wol

saC tseT e a b c

SE1 a1 1b c1

SE2 a1 1b 2c

SE3 a1 2b c1

SE4 a1 2b 2c

SE5 a1 3b c1

SE6 a1 3b 2c

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SE7 a1 4b c1

SE8 a1 4b 2c

SE9 a2 1b c1

01ES a2 1b 2c

11ES a2 2b 2c

21ES a2 2b 2c

31ES a2 3b c1

41ES a2 3b 2c

51ES a2 4b c1

61ES a2 4b 2c

71ES a3 1b c1

81ES a3 1b 2c

91ES a3 2b c1

02ES a3 2b 2c

12ES a3 3b c1

22ES a3 3b 2c

32ES a3 4b c1

42ES a3 4b 2c

itoN ce ht e s mi i al ytir be wt ee n the pa tt e nr of the es te ts cases a dn ht e c sno t ur itc no of a urt th at lb e ni soporp ti noi al cigol . hT e aC setr ai n udorP tc ug aran et se ht at we have a noiton of c“ ompl te ene ss ” ni

t ow se sn e :s we c vo er a ll the e uq ivale cn e cla ss es, a dn we have no e of e ca h ssop ib el c nibmo ati no fo i tupn s.

sA we hs a ll es e f mor ruo c no it un i gn ex ma lp e ,s ht e key to “ doog ” e laviuq e cn e cla ss test gni si t eh selec noit of ht e e viuq a nel ec er al noit . W cta h f ro ht e on it no of i stupn be gni “ ert ated the sam ”e . soM t of the t emi , e uq iv ela n ec c sal s te ts i gn d fe ni se cla ss es of ht e i upn t od iam .n hT ere si on r ae nos yhw we c dluo ton ed f ni e e viuq ale cn e relati sno no ht e uo t up t ra gn e of the gorp ar m f nu cti no be gni et ,dets in fac ,t this si t eh mis p el ts a aorpp ch f ro eht rT ai elgn borP el .m

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.in 2.7.3 Tr lanoitida E laviuq ence ssalC T tse i gn

hT e rt a tid i no al iv ew of e uq ivalence cl sa s tes it gn ed fines e uq ivale cn e c al ess s in te smr of v la di i .yt roF e ca h i tupn va ir abl ,e there are v la di a dn vni alid v la ue ;s ni the traditi no al a aorpp ch, seht e a er

ide itn fied a dn bmun e der , a dn then ni c aropro t de into te ts cases in eht w ae k se esn . As a feirb exa ,elpm c sno ider eht va dil ra gn es ed f ni ed f ro eht var ai lb es ni t eh C mmo si si no lborp me :

ehT c rro e dnops i gn i vn la di ra gn es are:

viG en these v ila d a dn i vn alid s te s of stupni , the rt ad iti no al e uq ivale cn e te gnits strate yg di en it if es et ts ac ses as fol :swol

1. roF dilav ,stupni esu eno eulav morf hcae dilav ssalc sa( ni tahw ew evah dellac kaew aviuqe ecnel

gnitset ssalc . etoN( taht hcae tupni ni eseht tset sesac lliw eb ).dilav

2. roF dilavni ,stupni a tset esac lliw evah eno dilavni eulav dna eht gniniamer seulav lliw lla eb lav id. suhT( a elgnis“ ”eruliaf dluohs esuac eht tset esac ot ).liaf

If the tupni var ai lb es have ed f ni ed ra gn es (as we had in the va suoir dnuob a yr va ul e e lpmax e )s , neht the set t cases f mor rt a noitid la e uq iv la en ec c ssal te gnits iw ll a wl a sy be a sbus et of soht e ht at luow d eb generated yb ssentsubor te ts i .gn

hT ere are t ow rp bo el sm iw th rt a id it no al e viuq ale cn e te gnits . hT e f tsri si ht at, ve yr often, t eh ps e ic f ci a noit od es ton ed fine hw at ht e e epx cted uo t up t f ro an vni alid et ts case dluohs eb . ( eW c dluo

a ugr e ht at siht is a defi ic ency of ht e eps cific ta i no , tub ht ta od e t’ns get us a hwyn ere.) suhT te ts e sr ps e dn a lot of mit e ed f ni i gn e px ce ted uo t up st f ro ht ese ca ses . hT e s ce dno lborp em si that st gnor ly

t py ed la ugn ages le i im tan e the n ee d f ro ht e c no is derati no fo i vn a dil i .stupn rT a id it no la e viuq a el cn e et ts i gn si a udorp ct of the time hw en al ugn ages us ch sa OF R NART a dn OC B LO were od m ni a tn ,

he cn e th si t py e of e rorr was co mm no . nI fac ,t ti was ht e hgih inc di ence of us ch e srorr ht ta el d to eht mi p el me tn a noit fo st gnor ly pyt ed la ugn age .s

2.8 qE u ecnelavi C ssal T tse C sesa f ro eht T elgnair orP b mel nI ht e elborp m ts ta eme tn , we ton e ht at there a er f ruo op ssi lb e :stuptuo oN t a irT a gn le, cS ale ,en

sosI ec les, a dn uqE ilateral. We can su e ht e es ot ide itn fy uptuo t r( a )egn e viuq a el nce cla ss es as f wollo s .

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hT ese cla sess iy eld a pmis le set fo et ts cas :se

saC tseT e a b c detcepxE tuptuO

O 1E 5 5 5 laretaliuqE

O 2E 2 2 3 elecsosI s

O 3E 3 4 5 lacS ene

O 4E 4 1 2 toN elgnairT a

If we base e viuq ela n ec c al ss se no ht e tupni amod in, we bo t nia a ir cher tes of test cases. What a er

mos e of the op iss bi eitil s f ro the erht e in et ge sr , a, b, a dn c? hT ey ac n la l be e uq al, ex ca tly no e pa ri can eb equal siht( can ppah en t eerh wa ,)sy ro enon can eb e uq a .l

sA a es parate uq e noits , we ac n a pp ly the rt ai lgn e porp erty to see fi they even c sno it tut e a rt ai .elgn roF( ex ma ple, ht e t lpir te 1< , 4, >1 has e ax c lt y eno p ia r of e auq l dis es, ub t ht ese dis es od ton f ro m a

t ir a gn le.) If we wan et d to be s it ll m ro e hguoroht , we c dluo separate eht “le ss than ro e uq al ot ” otni the t ow d itsi tcn cas ,se suht eht tes 6D dluow b ce emo

a dn imis al rly f ro 7D a dn .8D

eW en ed ot make no e m ro e sbo e vr at noi . oN t ci e that we have on t ta nek a yn f mro of aC tr se ian udorP tc . sihT is becau es a ll of ruo e uq ivalence cla ss se were a erl a yd d ife ned ni et rms of t lpir e st <a,

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.in b, c>. Had we fol wol ed ht e di tc ates of tra id it no al e viuq a el n ec te gnits , we dluow have e viuq a el ecn c al ess s fo tni ege sr rg ea ret than ze or a dn i tn ege sr le ss ht an ro e uq al ot ze or . hT e er uow ld l ae d to a

rob i gn , snu ta si fa tc yro set fo test case .s 2.9 qE u ecnelavi C ssal T tse C sesa f ro eht NextD eta noitcnuF

hT e Next aD et nuF cti no rtsulli ates ve yr we ll the c ar ft of c gnisooh t eh dnu erl gniy e uq iva el nce re al oit n. tI is a osl a doog exa lpm e no ihw ch ot c pmo are rt a noitid a ,l ew ak, a dn gnorts f ro ms of e uq ivalence lc sa s te ts i .gn Next aD te si a f cnu t noi of three var ai lb es, ,htnom day, a dn y ae r, a dn the es have ra gn es ed f ni ed as fol swol : T lanoitidar T tse C sesa

ehT valid e viuq ale ecn cla ss es a er

ehT vni alid e viuq alence lc ssa es a er

hT e es cla ss es iy le d the gniwollof et ts ac ses, hw ere the valid i stupn are em cha cin ally selec et d f mor t eh a mixorpp a et im eldd fo eht va dil ra gn e:

esaC DI tnoM h yaD Year detcepxE

tuptuO

1ET 6 15 2191 1/61/6 219

2ET -1 15 2191 dilavni tupni

3ET 13 15 2191 dilavni tupni

4ET 6 -1 2191 dilavni tupni

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7ET 6 15 3102 dilavni tupni If we m ro e ca er f yllu c ooh es ht e e uq ivalence re noital , ht e r se u tl gni e uq iva nel ce cla ss es w li l be erom

su eful. R ce a ll we sa di e ilra er ht at ht e g tsi of ht e viuqe ale cn e rela noit si t tah el me e stn ni a cla ss a er “ ert ated ht e same wa .”y nO e way ot s ee ht e de if ciency of ht e trad ti noi al a orpp ach si aht t t eh “ ert atme tn ” si at the v ila d/ vni a dil el vel. eW xen t re ud ec ht e rg a lun ari yt by foc gnisu no m ro e ps e ic f ci rt ae t em .tn

What must be nod e ot an tupni d ta e? If it si on t ht e last day of a nom t ,h the Ne Dtx ate f cnu t noi wi ll s mi ply increment ht e day v la eu . At ht e e dn of a htnom , the next yad si 1 a dn the htnom is increme tn ed. tA ht e e dn fo a y ae r, htob ht e yad a dn ht e htnom are ser te to 1, a dn the y ae r is increme tn ed. Finally, ht e lborp me of el ap ye ra makes determ gnini ht e al ts day of a htnom in et re gnits . iW th lla siht ni ,dnim ew hgim t alutsop te t eh fol wol i gn e uq iva nel ce cla :sess By c gnisooh sepa etar cla ss es f ro 03 a dn 13 day m no t sh , we pmis lify ht e last day of the htnom uq e oits n. By at gnik Fe urb a yr as a separate class, we can vig e rom e a tt ent noi to el ap y ae r uq es it .sno

We a osl give ps e ic al atte noitn ot day v la ue :s syad in D 1 a er en( a )ylr a wl ays ni creme tn ed, lihw e days ni D4 no ly have aem ni gn f ro shtnom in 2M . niF al yl , we have rht ee cla ss es of y ae sr , the ps e aic l case of ht e y ae r 0091 , el ap y ae sr , and c mo m no y ae sr . hT is si t’n a perfe tc es t of e uq ivalence cla ,sess

tub sti use lliw reve la ynam top ential e rorr s .

aeW k uqE elavi n ec ssalC T tse C sesa

hT e es lc a ss es iy le d t eh f lo l gniwo weak e viuq ela cn e cla ss te ts cases. sA bef ero , the upni ts a er cem ha in ca ll y s le ected f mor eht a amixorpp te mi eldd fo eht c rro e gnidnops cla :ss

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tuptuO

W 1E 6 14 0091 0091/51/6

W 2E 7 29 2191 2191/03/7

W 3E 2 30 3191 dilavni tupni

W 4E 6 31 0091 dilavni tupni

itoN ce t tah f wollo i gn a s mi ple pa tt e nr iy e sdl et ts ac ses ht ta a er on t pa itr cu al rly r ssae gniru . hT is si row th rem me beri gn , becau es a yn t loo ht ta etta m stp ot a motu ate siht corp ess lliw re yl no mos e fa ylri

s mi ple va eul eles cti no c ir et ri .no S ort ng Equ elavi n ec C ssal tseT C sesa

gnisU ht e same e viuq la ne ec cla ss es, ew fi dn the gnorts e uq ivale cn e cla ss et ts cases nwohs in t eh at lb e b le wo . hT e mas e v la eu eles cti no c tir e noir is su ed. We lits l ’nod t vah e a “pe efr c ”t es t of te ts

cases, tub I thi kn a yn et ts er uow ld be a lot ha reipp htiw ht e 63 gnorts e uq ivale cn e cla ss te ts cases. oT li l su trate ht e se ytivitisn ot ht e c ioh ce of cla ess s, iton ce ht at, gnoma these 63 et ts cases, we ne rev

g te a Feb. 2 .8 If we had sohc en vif e day cla ss e ,s hw ere ’1D uow ld be da sy 1 - 72 , 1D ” uow ld be ,82 a dn the other erht e dluow ts ay ht e sa em . eW uow ld have a tes fo 54 te ts c sa es, a dn am gno the ,es the er dluow eb tteb er c vo era eg fo Fe .b 82 c sno idera .snoit


tuptuO

S 1E 6 14 0091 0091/51/6

S 2E 6 14 2191 2191/51/6

S 3E 6 14 3191 3191/51/6

S 4E 6 29 0091 0091/03/6

S 5E 6 29 2191 2191/03/6

S 6E 6 29 3191 3191/03/6

S 7E 6 30 0091 0091/1/7

S 8E 6 30 2191 2191/1/7

S 9E 6 30 3191 3191/1/7

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01ES 6 31 0091 RORRE

11ES 6 31 2191 RORRE

21ES 6 31 3191 RORRE

31ES 7 14 0091 0091/51/7

41ES 7 14 2191 2191/51/7

51ES 7 14 3191 7 3191/51/

61ES 7 29 0091 0091/03/7

71ES 7 29 2191 2191/03/7

81ES 7 29 3191 3191/03/7

91ES 7 30 0091 0091/13/7

02ES 7 30 2191 2191/13/7

12ES 7 30 3191 3191/13/

22ES 7 31 0091 0091/1/8

32ES 7 31 2191 2191/1/8

42ES 7 31 3191 3191/1/8

52ES 2 14 0091 0091/51/2

62ES 2 14 2191 2191/51/2

72ES 2 14 3191 3191/51/2

82ES 2 29 09 0 RORRE

92ES 2 29 2191 2191/1/3

03ES 2 29 3191 RORRE

13ES 2 30 0091 RORRE

23ES 2 30 2191 RORRE

33ES 2 30 3191 RORRE

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43ES 2 31 0091 RORRE

53ES 2 31 2191 RORRE

63ES 2 31 3191 RORRE

eW c uo ld a osl st er am il ne ruo es t of te ts ac ses yb ta gnik a lc so er l koo at the ey ar cla ss es. If ew em gr e 1Y a dn 3Y , a dn lac l ht e re us lt the s te of c nommo years, ruo 63 te ts cases uow ld pord nwod ot

24. sihT cha gn e rppus e ss es eps ic la ta te noitn ot c disno er ita sno in eht y ae r 0091 , a dn ti a osl a sdd mos e comp el ytix to the dete nimr a noit of ihw ch y ae sr are l ae p ey a sr . Bala cn e th si aga tsni woh much im thg be el a nr ed f mor the rp ese tn te ts case .s Ta ek a kool at t eh test cases ni hw ci h Yl si su ed ,2ES( 4ES , 7ES , .. .). We t’nod r ae yll l ae nr much f mor ht e ,es os ton um ch uow ld eb tsol yb ski pp i gn ht e .m

hT e no ly thi gn r ae yll tni ere gnits a tuob ht e year 0091 is t tse c sa e 82ES , a dn a re al ted et ts case t tah was ton generated yb eht set fo e uq ivale ecn cl ssa ,se Fe .b ,82 .0091 2.9 qE u ecnelavi C ssal T tse C sesa f ro eht C noissimmo melborP

hT e tupnI mod a ni fo ht e noissimmoC borP mel si “na larut ly” pa titr i deno yb ht e imil st no ol c sk , stoc sk , a dn ba slerr . ehT es e uq iv ela nce cla ss es ra e exac ylt soht e th ta dluow al os be di e itn fied yb tradi noit al e viuq alen ec lc a ss et ts i gn . hT e fi sr t class si the valid i upn t, the other owt are i vn a dil . hT e i tupn mod a ni e uq ivale cn e c al ess s lead to ve yr snu ita fs ac yrot s te s of te ts c esa .s We ll’ od a til t el be tt er htiw e viuq ale ecn lc a ss es ed if ned no eht uptuo t ra gn e fo t eh c mo im ss i no f nu tc .noi Va ir a elb tupnI oD am ni viuqE ale ecn lC a ss es

elbairaV tupnI niamoD ecnelaviuqE sessalC

kcoL 1L = { :kcol 1 ≤ kcol ≤ 07 }

2L = { :kcol kcol < 1 }

3L = :kcol{ kcol > 07 }

cotS k 1S = { :kcots 1 ≤ kcots ≤ 08 }

2S = { :kcots kcots < 1 }

3S = { :kcots kcots > 08 }

lerraB 1B = { :lerrab 1 ≤ lerrab ≤ 09 }

2B = { :lerrab lerrab < 1 }

3B = { :lerrab lerrab > } 09

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aeW k nI up t D niamo E ecnelaviuq C ssal tseT sesaC

hT ese cla sess iy eld eht f lo l gniwo w ae k e viuq ale ecn cla ss te ts ca es .s sA wi ht eht hto er ex ma lp e ,s eht i stupn a er mechanic la yl s le ecte .d

saC tseT e col ks cots ks lerrab s las se noissimmoc

W 1E 35 04 54 005 05

W 2E 0 0 0 RORRE ORRE R

W 3E 71 18 19 RORRE ORRE R

T lanoitidar nI p tu D iamo n E ecnelaviuq C al ss T tse C sesa

ehT sa em lc ssa es iy le d eht f wollo i gn rt a lanoitid e viuq ela ecn c al ss et ts ca .ses sA w ti h eht other ex ma lp e ,s t eh i stupn a er m ce ha in c la ly se el cte .d

saC tseT e col ks cots ks lerrab s las se noissimmoc

SE1 35 04 54 005 05

SE2 35 04 0 RORRE ORRE R








01ES 0 04 54 RORRE ORRE R




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ES 22 71 0 54 RORRE ORRE R






tuO p tu R egna ecnelaviuqE C al ss eT st C sesa

itoN ec ht at, of 72 et st cases, no ly no e si a legi mit ate tupni . If we were r ae lly ow rr ei d a tuob e rorr cases, this thgim eb a doog es t of te ts cases. tI nac ha yldr vig e us a se sn e of c fno ide cn e a tuob t eh calc lu ita no noitrop of the borp l me , woh ever. We ac n get mos e he pl yb c disno e gnir e uq ivale ecn c al ess s ed f ni ed no t eh tuptuo ra egn . eR ac ll that as les si a f nu c noit fo eht bmun er of loc sk , ots c sk a dn ba rr e sl sol :d Figure 5.6 he spl us g te a be tt er “feel” f ro ht e i tupn ps a ec . lE eme stn of 1L are stniop w ti h ni et ger c nidroo ates in ht e ryp a im d en ar ht e .nigiro lE ements of 2L a er op ints ni ht e “ irt a lugn ar s il ”ec be ewt en ht e ryp ma di a dn the re ts of ht e tupni ps a ec . niF la yl , le emen st fo L3 are all t soh e stniop ni the er cta lugn ar ov lu em ht at are ton ni 1L ro in 2L . All ht e e rorr cases f dnuo yb ht e ts gnor e uq ivalence cla ss es fo t eh tupni mod ia n a er uo t edis fo t eh er cta ugn al r space nwohs in rugiF e 5. .6

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sA was t eh c sa e iw th ht e rT ai lgn e borP el m, the fact ht ta ruo i tupn si a rt ip el t em ans that we on l gno er at ek test cases f or m a Ca tr es ai n udorP c .t

uptuO t Ra egn viuqE la ence lC a ss Te ts aC se :s

saC tseT e col ks cots ks lerrab s las se moc m noisi

O 1R 5 5 5 005 05

O 2r 15 51 51 0051 571

O 3R 25 52 52 0052 063

hT ese te ts cases vig e su emos se esn ht at ew a er exerc gnisi im rop at tn parts fo eht lborp e .m 2. 01 senilediuG dna Ob oitavres ns

woN taht ew have enog hguorht rht ee ex ma lp e ,s we c no c edul htiw s emo sbo e vr a snoit a ,tuob a dn diug e il nes f ro e uq ivale ecn cla ss te ts i .gn

.1 hT e rt adi noit al f mro of eq viu alence c al ss te ts i gn si generally ton as hguoroht as weak

e viuq alen ec cla ss tes it ,gn ihw ch ni nrut , is ton sa guoroht h as ht e gnorts form of e uq ivale ecn c al ss et ts .gni

.2 hT e ylno t emi it makes es sn e ot su e ht e rt a noitid la a orpp ach si hw en the mi lp eme atn t noi al ugn age si ton ylgnorts pyt e .d

.3 fI e rorr c dno ti snoi a er a hgih roirp ti y, we c dluo e tx e dn gnorts e uq iv ela nce cla ss t tse i gn ot inc edul vni a dil lc a ss e .s

.4 uqE ivale cn e cla ss et gnits si a irporpp ate hw en upni t data si ed f ni ed ni t re ms of ra gn es a dn es st of discrete va ul es. hT si si ce tr ainly the ac se hw en tsys me malf nu c snoit can co c ru f ro

tuo -of- mil it va ir able v la ue .s

.5 uqE ivale cn e lc ssa te ts i gn si ts re htgn ened yb a dirbyh a aorpp ch htiw dnuob a yr va ul e et s it ng. (We ac n “re ”esu eht fe f tro ma ed ni ed f gnini eht e viuq a el nce cla sess .)

.6 uqE ivale cn e c ssal te ts i gn is i cidn ated hw en ht e rgorp am f nu itc no si com lp ex. nI us ch ac ses, the c lpmo ex ti y of ht e f nu itc no can help i ned it fy su fe lu e uq ivalen ec cl ssa es, as in t eh

NextDa et f nu cti .no

.7 gnortS equiv ela cn e cla ss te gnits am k se a rp e pmus it no t ah t ht e va ir abl se are i dn epe dn e tn hw en ht e Ca tr es ai n udorP tc is taken. If ht ere are a yn depe dn encies, th ese w li l often gene etar

e“ rorr ” t tse cases, sa t yeh did in ht e NextDate nuf c noit . hT( e ed c noisi tab el et c nh i uq e ni Cha tp er 7 re os lves this lborp em.)

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.8 Several irt es may be n ee ded eb f ro e “ ht e hgir ”t e uq ivale cn e rel ta noi is disc vo ered, as ew as w ni ht e Ne Dtx ate e elpmax . nI other ca ses , ht ere is an “ vbo i suo ” ro “na rut al” e viuq a el nce

r ale tion. When in ,tbuod eht best bet is ot yrt to es c dno ug e ss a ps ec st of a yn r sae no a elb mi p el me tn ati no .

2 11. De sic oi n Table Ba des eT sti gn Of all the f nu c noit al te gnits me doht s, t soh e based no de noisic ta lb es are the m so t ri suorog , beca su e d ce si i no ta lb es th me se vl es enf ro ce l igo ac l rogir . hT e er a er owt c sol le y related me ,sdoht Ca su e- Ef ef ct rg a gnihp [ mlE e rodn f ]37 , yM{ ers }97 a dn ht e deci nois ta lb ae u m dohte [ lsoM ey ]39 . hT e es are m ro e c bmu e mosr e ot esu , a dn are f lu ly re dnud a tn iw th ed ic nois tab el s, so we liw l ton disc ssu them he .er Both a er c revo ed in [ lsoM ey .]39 2. 11 .1 D noisice Tab sel Dec noisi tab el s have eb en su ed ot re rp esent a dn a zylan e c lpmo ex gol ical ler ati hsno i sp is cn e t eh ea ylr s0691 . hT ey are di eal f ro descri gnib is tua snoit in hw ci h a bmun er of c nibmo ati sno of ac snoit are taken dnu er va yr ing se st of c snoitidno . moS e of the ba is c ed c noisi ta lb e et r sm are i ll u ts ar et d ni Figure 2. 11

hT ere are f ruo rop it sno of a d ce noisi ta lb e: ht e part to the el ft of ht e ob ld ve itr cal il ne si the uts b rop t noi , ot ht e hgir t is ht e e yrtn rop t noi . hT e pa tr a vob e eht dlob nil e si t eh c dno i noit noitrop , b le wo

si t eh a itc no noitrop . suhT we ac n refer to eht c noitidno buts , the c noitidno e irtn es, ht e ac noit ts ,bu a dn ht e ac noit ent ir es. A column in the e yrtn rop t noi si a elur . luR es idni ca et ihw ch ca ti no s a er at ken f ro the c dno iti no al c ri c tsmu anc se idni ca et d ni ht e c dno iti no rop it no of ht e elur . nI eht ed c noisi at lb e ni Fi rug e 7. ,1 nehw c tidno i sno c ,1 c2, a dn c3 are lla eurt , ca noit s 1a a dn a2 co c ru . When 1c

a dn c2 are htob t eur , a dn c3 si f esla , ht en act snoi 1a a dn a3 co c ru . hT e e yrtn f ro c3 in ht e lur e hw ere c1 si urt e a dn c2 si fal es is c lla ed a “ ’noD t Ca er ” e yrtn . hT e ’noD t aC re ent yr has t ow rojam in et rpr etati :sno t eh c idno t noi si rri eleva tn , ro eht c noitidno od es ton a pp ly. mitemoS es pe lpo e wi ll e tn er t eh “n/ ”a ys m ob l f ro this l ta ter i tn e rpr eta .noit When we have bina yr c tidno i sno urT( /e F la es , Ye oN/s , 0/ )1 , ht e c dno i noit rop it no of a ed cisi no ta elb si a t htur ta lb e (f or m tisoporp i ano l l igo c) ht ta has eb en tor ated °09 . hT is ts ur tc ru e ug ara tn ee s ht at

we c disno er eve yr ssop ib el com nib ati no of c idno it no va ul e .s When ew su e d ce noisi ta lb es f ro test case di e itn fic noita , siht lpmoc ete en ss porp e ytr of a ed c noisi ta lb e iw ll ug arantee a f ro m fo c etelpmo et ts i gn . eD c noisi ta lb es in ihw ch a ll ht e c dno ti noi s a er nib a yr a er ca dell L mi ti ed yrtnE Dec noisi

Tab el s. If c snoitidno a er lla wo ed ot have several v la ues, ht e re tlus i gn lbat es are called txE e dn ed yrtnE Deci nois Ta lb e .s We lliw ees ex ma p el s fo htob t py es f ro t eh Ne Dtx a et lborp e .m

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Dec noisi at lb se are mos e hw at d ce la vitar e (as soppo ed ot mi pera vit e :) there is on partic lu ar dro er mi plied yb t eh c dno iti no ,s a dn eles cted ac snoit od ton o cc ru in a yn pa itr c ralu dro e .r

2. 11 .2 Tec uqinh e

oT di entify et ts ac ses wi ht d ce i is no ta selb , we i rpretn et c dno i snoit as i stupn , a dn acti sno as uo t up t .s oS em it m se , c snoitidno dne pu refe rr i gn to e uq ivale cn e c al ss es of i pn stu , dna itca sno refer to am j ro

f tcnu i ano l orp ec gniss rop it no s of the ti me be gni te ts ed. hT e ur el s are then tni e rpr eted as et st ac se .s Beca su e the ed isic no t ba el ac n mecha in c ylla be f cro ed ot eb comp el et , ew wonk ew ha ev a com rp ehe visn e s te of tset ac ses. hT ere are seve lar cet nh i uq es th ta cudorp e ed isic no ta lb es t ah t a er m ro e su fe lu ot te ts e .sr nO e pleh f lu elyts si ot a dd an ac it no to wohs hw en a elur si gol ci ylla mi ssop i elb .

nI the ed ic si no tab el ni Figure 2. 11 .2, we es e exam lp es of ’noD t aC re e rtn ei s a dn bissopmi el elur su age. If ht e tni ege sr a, b, a dn c od on t c tutitsno e a irt a elgn , we t’nod ve en ac re a uob t op ssi elb

e uq a il ties, as cidni ated ni ht e tsrif ur le. nI lur es 3, 4, a dn 6, if t ow p ia sr of i tn ege sr are e uq al, yb tran is ,ytivit eht thi dr pa ri tsum eb e ,lauq t suh eht negative ent yr makes eht ur el )s( .elbissopmi

hT e dec isi no ta lb e ni Fi rug e 2. 11 .3 i ll su trates a hton er c no is dera noit ler ated ot cet uqinh :e ht e c oh ice of c idno it sno can rg ae tly e px a dn ht e is ze of a de ic is no ta elb . He er , ew e px anded t eh dlo c noitidno (c1: a, b, c a er a irt a gn el ?) ot a rom e deta li ed vi we of eht t erh e ine uq a il t sei fo eht irt a gn el porp e .ytr If a yn no e of ht ese af ,sli ht e t erh e i tn ege sr od on t c tutitsno e sides of a irt a gn el . We c dluo e px a dn this st lli f htru e ,r eb ca esu there are owt wa sy an ni e uq a ytil c dluo fa :li eno dis e c uo ld e uq al eht mus of ht e hto er t ow , ro ti c uo ld be ts ir c ylt rg ae ter. hT e ur el ent yr rop it no of this deci is no ta lb e si sa ni Figure 7.3, e cx ept that each c dno iti no ent yr si e px li tic yl nwohs . sihT si a tam ter of st ly e, ton c no tent. I kniht t eh elyts ni rugiF e 7.2 si erom er ada .elb When c snoitidno refer ot e viuq ela n ec classes, d ce noisi at lb es have a hc a ar cte tsir ci a pp ae ra ecn . C idno it no s ni ht e ed ci nois at lb e ni Fi rug e 7.4 ra e f mor ht e Ne taDtx e p bor el m; ht ey r fe er to t eh mutua yll exc sul ive ilibissop ties f ro the nom th va ir a elb . cniS e a m no th is ni ex ca ylt no e e uq ivale ecn c al ss, we ca onn t ever vah e a lur e ni hw ci h owt rtne ei s are eurt . hT e ’noD t aC re e rtn ei s (—) er a yll

aem n “ tsum eb f .”esla emoS ed c noisi table afi noic a sod esu eht ton ati no !F ot ma ek siht op i .tn

When c snoitidno refer ot e viuq ela n ec classes, d ce noisi at lb es have a hc a ar cte tsir ci a pp ae ra ecn . C idno it no s ni ht e ed ci nois at lb e ni Fi rug e 7.4 ra e f mor ht e Ne taDtx e borp el m; ht ey r fe er to t eh mutua yll exc sul ive ilibissop ties f ro the nom th va ir a elb . cniS e a m no th is ni ex ca ylt no e e uq ivale ecn c al ss, we ca onn t ever vah e a lur e ni hw ci h owt rtne ei s are eurt . hT e ’noD t aC re e rtn ei s (—) er a yll

aem n “ tsum eb f .”esla emoS ed c noisi table afi ic no a sod esu eht ton ati no !F ot ma ek siht op i .tn

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sU e of ’noD t Care e irtn es has a tbus le fe fect no ht e way in hw ci h c lpmo ete d ice nois ta selb era rec ngo ized. roF mil ti ed ne t yr d ice nois at lb es, if ht ere are n c dno iti sno , ht ere tsum be 2n lur es. When

t’noD aC re e irtn es r ae yll idni cate ht at ht e c idno t noi si i rr eleva tn , we nac devel po a lur e c tnuo sa f wollo s. luR es in hw ci h on t’noD Care e irtn es co c ru c tnuo as no e elur . aE ch ’noD t Ca er e yrtn ni a

lur e lbuod es ht e c tnuo of ht ta elur . hT e lur e c stnuo f ro t eh ed isic no table in Fi rug e 7.3 a er nwohs be wol . oN t eci that t eh us m fo eht ur le c nuo ts si 46 (as ti uohs ld be .)

snoitidnoC

:1c ?c+b<a F T T T T T T T T T T

:2c ?c+a<b - F T T T T T T T T T

:3c ?b+a<c - - F T T T T T T T T

:4c a ?b = - - - T T T T F F F F

?c = a :5c - - - T T F F T T F F

:6c = b ?c - - - T F T F T F T F

eluR tnuoC 23 16 8 1 1 1 1 1 1 1 1

:1a ton a elgnairt X X X

enelacS :2a X

:3a selecsosI X X X

:4a laretaliuqE X

:5a elbissopmI X X X

If ew a lpp ied this silpmis it c a rogl ti hm to the de noisic ta lb e ni rugiF e 7.4, we get t eh elur c stnuo

nwohs eb l :wo

noitidnoc s R1 R2 3R

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:1c htnom ni 1M T -- --

:2c htnom ni 2M -- T --

:3c htnom ni 3M -- -- T

eluR tnuoC 4 4 4

1a

Sin ec we uohs ld no ly vah e ie hg t lur e ,s we cl ae ylr have a lborp em. oT es e hw e er ht e lborp em lie ,s we e px a dn each fo eht rht ee ,selur re lp ica gn t eh — e tn r ei s htiw t eh T a dn F op iss bi .seitil

noitidnoc s .1 1 2.1 .1 3 4.1 .2 1 2.2 .2 3 4.2 1.3 .3 2 3.3 4.3

:1c om . in 1M T T T T T T F F T T F F

:2c om . in 2M T T F F T T T T T F T F

:3c om . in M3 T F T F T F T F T T T T

eluR tnuoC 1 1 1 1 1 1 1 1 1 1 1 1

1a

itoN ce that we have t erh e selur ni hw ci h all ent ir es are :T ur el s 1.1, 2.1, dna 3. .1 We a osl have t ow lur es wi ht T, ,T F e :seirtn lur es 1.2 a dn 2. .2 Sim ali ylr , lur es 1.3 a dn 3.2 are di e citn a ;l os are selur

2.3 a dn 3.3. fI we dele et the repe it it sno , we e dn pu htiw seven elur ;s the m gnissi lur e is ht e no e ni ihw ch a ll c idno it no s a er af esl . hT e re tlus of siht orp ssec ni nwohs in Fi rug e 7.5. hT e i opm ssi elb

lur es a er sla o .nwohs

hT e ab li yti ot rec ngo ize (a dn deve )pol c lpmo ete d ce i is no ta lb es stup su in a wop erful noitisop htiw res ep ct ot re dnud ancy a dn inc sno i ts ency. hT e deci nois ta lb e ni Fi rug e 7.6 si re dnud a tn — ht ere a er t rh ee c dno ti snoi a dn ni en lur e .s (Rule 9 is identical to elur 4.)

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itoN ec ht ta ht e ac noit e rtn ei s ni lur e 9 are ide lacitn to t soh e in ur el s 1 - 4. sA l gno as ht e acti sno in a re dnud ant lur e are di e itn ac l to ht e c rro es gnidnop pa tr of ht e ed ic nois ta elb , there t’nsi um ch of a

borp el m. fI eht ca ti no e rtn ei s a er di ff eren ,t as ht ey a er in rugiF e 2. 11 . ,7 ew have a bi gg er borp el .m If ht e d ce noisi ta lb e in Fi rug e 2. 11 .7 were ot orp ec ss a rt a asn tc noi ni ihw ch c1 is t eur a dn ob th c2 a dn c3 a er fal ,es ob th ur el s 4 a dn 9 a .ylpp We can ma ek owt sbo e vr a :snoit

1. seluR 4 dna 9 era tnetsisnocni .

2. ehT noisiced elbat si non - citsinimreted . Ru el s 4 a dn 9 are inc no is ts ent beca su e ht e acti no es st are id ffere tn . hT e ohw el at lb e si non - dete itsinimr c beca su e there si on way ot d ice de hw te her ot a pp ly ur el 4 ro ur el 9. hT e mottob line f ro tes et sr si ht at ac er dluohs eb taken hw en ’noD t Ca er ent ir es a er su ed in a deci is no ta .elb 2. 11 .2 Test C sesa f ro eht T elgnair orP b mel

gnisU the ed ic si no ta lb e in Fi rug e 2. 11 .3, we tbo a ni eleven f nu c noit al test cas :se t rh ee mi lbissop e cases, t rh ee ways ot fail ht e irt a lgn e porp e ytr , no e way to get an e uq ilater la rt ai gn le, no e way ot get a cs alene t ir a elgn , a dn rht ee wa sy to get an csosi le se t ir a elgn . If we e tx e dn ed ht e ed cisi no ta lb e ot

wohs ob th ways ot fail an ine uq a ytil , we uow ld ip ck pu t erh e m ro e t tse cases hw( ere no e dis e si e ax c ylt t eh sum of ht e hto er )owt . moS e j gdu em nt si re riuq ed in siht eb ac su e of the e nopx ent lai rg o htw of lur es. nI siht ac es , we dluow e dn pu w ti h ma yn m ro e nod ’t ac re e irtn es, a dn erom mi ssop i elb lur e .s

esaC DI a b c detcepxE tuptuO

DT1 4 1 2 toN elgnairT a



DT4 5 5 5 laretaliuqE

DT5 ? ? ? elbissopmI


DT7 2 2 3 elecsosI s

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DT9 2 3 2 elecsosI s

01TD 3 2 2 sosI elec s

11TD 3 4 5 nelacS e 2. 11 .3 Test C sesa f ro eht NextD eta noitcnuF

hT e Ne Dtx ate f nu c noit was c soh en beca su e ti i rtsull a et s ht e rp lbo em of depe dn e cn ei s in ht e tupni mod ain. sihT makes ti a perfect exa lpm e f ro noisiced ta lb e based te ts i ,gn becau es ed c noisi ta lb es

ac n thgilhgih us ch depe dn encies. Reca ll ht at, ni hc ap et r 6, we di e itn fied uqe iv la e cn e cla ss se in t eh i tupn od am ni of ht e DtxeN ate f nu c noit . nO e fo ht e mil ti at noi s we f dnuo ni Chap et r 6 w sa ht at i dn csi r mi ni ate select noi of upni t v la seu f mor ht e e uq ivalen ec cl sa ses res lu et d in “ rts a gn ”e te ts ca ,ses us ch as fi gnidn the ne tx d eta ot J nu e 13 , 2181 . hT e borp mel ts me s f or m ht e pr se tpmu i no ht ta the

va ir ab sel are i dn epe dn ent. If ht ey a er , a Ca tr e is an udorP ct of ht e lc ssa se am sek es esn . When the er are igol c la depe dn enc ei s a gnom var ai lb es ni ht e upni t mod a ni , the es depe dn e cn ei s are tsol

rppus( e ess d si teb et )r ni a aC tr e is an udorP ct. hT e d ice si no ta lb e f mro at le st us e hpm a zis e hcus depe dn e cn ei s gnisu t eh on it no of ht e “ ssopmi i lb e ca ”noit ot de ton e im elbissop c nibmo ati sno of c dno i noit s. nI siht s ce ti no , we iw ll make rht ee t ir es at a ed c si i no ta elb f lumro a noit of the Ne taDtx e f tcnu i no .

tsriF T yr dI e tn fi gniy a irporpp ate idnoc it sno a dn ac snoit rp ese stn an nutroppo i yt f ro cr tfa ms a hsn ip. soppuS e

we ts a tr w ti h a set fo e uq ivale ecn lc a ss es c esol ot eht eno ew su ed in Cha tp er .6

ecnelaviuqE sessalC

M1 = { tnom h : tnom h ah s 03 ad ys } M2 = { tnom h : tnom h ah s 13 yad s }

3M = { htnom : htnom si }yraurbeF

D1 = d{ ay : 1 ≤ ad y ≤ 82 } D2 = d{ ay : ad y = 92 } D3 = d{ ay : ad y = 03 }

4D = yad{ : = yad 13 }

Y1 = aey{ r : aey r is a pael }raey

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2Y = raey{ : raey si a nommoc }raey If we hsiw ot hgih l hgi t im ssop ible c anibmo it ,sno we c uo ld ma ek a imiL et d nE t yr Deci nois Ta elb

htiw eht fo ll gniwo c idno t noi s a dn ac snoit . etoN( ht at eht e uq iv ela nce clas es s f ro the ey ar va ir a elb c lo la esp otni eno c noitidno .)

snoitidnoc

:1c htnom ni ?1M T

:2c htnom ni ?2M T

:3c htnom ni ?3M T

:4c yad ni ?1D

:5c yad ni ?2D

:6c yad ni ?3D

c :7 yad ni ?4D

:8c raey ?1Y ni

:1a elbissopmi

tad txen :2a e

sihT deci nois ta lb e wi ll have 652 ur el s, ma yn of ihw ch wi ll be elbissopmi . If we wa tn ed ot wohs yhw ht e es lur es we er mi ,elbissop ew hgim t re iv se ruo ac snoit ot t eh f :gniwollo

Ac it sno

:1a ooT ynam syad ni a htnom

:2a tonnaC neppah ni a nommoc raey

:3a etupmoC eht txen etad S dnoce T yr If we focus no ht e el ap y ae r a eps ct of the Ne Dtx ate f nu tc noi , ew c dluo su e the es t of e uq ivale ecn c al ess s as ht ey were ni Cha tp er 6. hT e es cla ss es have a Car et is an udorP tc ht at c tno a sni 63 t ,selpir

htiw several be gni mi ssop ibl .e

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oT i ll u ts rate a hton er deci nois ta lb e tec euqinh , siht emit we l’ l deve pol an xE et dn ed yrtnE Dec si i no Tabl ,e a dn we’ll take a clo es r kool at ht e acti no s but . nI am gnik an xE et dn ed yrtnE D ce noisi Ta ,elb we tsum e rusn e ht ta ht e e viuq ela n ec cla ss es f mro a urt e par it noit of ht e upni t od iam n. R( ecall f mor Cha tp er 3 that a pa noititr si a set of disj nio t bus es st sohw e nu i no si the ent ri e es t.) If there were a yn “ vo er al p” ma gno ht e ur el ent ir es, we dluow have a re dnud ant case in ihw ch rom e ht an no e elur c uo ld be sa sit fied. He ,er 2Y is the s te of y ae sr be ewt en 2181 a dn 2102 eve yln divi lbis e yb f uo r ex lc gnidu t eh ey ar .0091

elbairaV ecnelaviuqE sessalC

tnoM h M1 = { htnom : tnom h has 30 yad s } M2 = { htnom : tnom h has 31 yad s }

3M = { :htnom htnom si }yraurbeF

aD y D1 = ad{ y: 1 ≤ ad y ≤ 82 } D2 = d{ ay: day = 92 } D3 = d{ ay: day = 03 }

4D = :yad{ yad = 13 }

raeY Y1 = :raey{ raey = 091 0} Y2 = y{ ae :r 2181 ≤ aey r ≤ 2102 A DN aey( r

≠ 0091 ) A DN ey( ar = 0 dom 4)} 3Y = :raey{ 2181( ≤ raey ≤ 2102 DNA raey

≠ 0 dom })4 nI a se esn , we c uo ld a ugr e t ah t we have a “ rg ay box” tec nh i euq , ceb a su e ew at ke a c sol er l koo at t eh

NextDate f nu c noit . nI dro er to udorp ec the ne tx da et of a given da et , t reh e are ylno if ve ssop i elb am lupin ita :sno ni cr eme tn i gn a dn reset gnit ht e day a dn htnom , a dn ni c mer en it gn ht e y ae r, w( e t’now

el t emit og b ca wk a sdr yb rese gnitt t eh ey a .)r

noitidnoC s 1 2 3 4 5 6 7 8

1c htnom in M1 M1 M1 M1 M2 M2 M2 2M

:2c ni yad D1 D2 D3 D4 D1 D2 D3 4D

:3c ni raey - - - - - - - -

eluR tnuoC 3 3 3 3 3 3 3 3

noitca s

:1a elbissopmi X

:2a tnemercni yad X X X X X

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yad teser :3a X X

:4a tnemercni htnom X ?

:5a teser htnom ?

:6a cni tnemer raey ?

noitidnoc s 9 01 11 12 31 14 15 61

:1c htnom ni M3 M3 M3 M3 M3 M3 M3 3M

:2c ni yad D1 D1 D1 D2 D2 D2 D3 3D

:3c ni raey Y1 Y2 Y3 Y1 Y2 Y3 - -

eluR tnuoC 1 1 1 1 1 1 3 3

noitca s

:1a elbissopmi X X X X

:2a tnemercni yad X

yad teser :3a X X X

:4a tnemercni htnom X X X

:5a teser htnom

:6a tnemercni raey

sihT de noisic tab el has 63 ur el s, a dn c rro e sdnops ot the Ca tr es ai n udorP ct of the e uq ivale ecn c al ess s. We sti ll have ht e melborp iw th l igo ac yll lbissopmi e lur es, tub ht is f lumro ita no spleh su ide itn fy ht e e epx cted stuptuo of a t tse ca es . If uoy deve pol siht ta elb , uoy lliw if dn emos cumbe emosr lborp e sm htiw Dece bm er ni( elur .)8 We f xi the es ne .tx T drih yrT We can cl ae r pu eht e dn of y ae r c no is dera snoit htiw a driht s te of e uq ivalence cla ss es. hT is emit , we are ve yr ps fice ic a tuob da sy a dn shtnom , a dn we revert to the is m lp er ael p-y ae r ro c nommo ye ra c dno i noit of ht e f ri ts yrt , os the y ae r 0091 g ste on ps ec ai l a tt ent noi . W( e c dluo od a f ruo th yrt ,

wohs i gn y ae r e viuq ale ecn cla ss es as in eht es c dno t ,yr tub yb ,won uoy get eht tniop .)

eR siv ed Ne tx Da et amoD in uqE ivale ecn Cla ss es

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tnoM h M1 = { htnom : m htno h sa 03 yad s } M2 = { :htnom tnom h ah s 31 yad s tpecxe De }.c M3 = { tnom h: tnom h is }rebmeceD

4M = :htnom{ htnom si }yraurbeF

aD y D1 = {day: 1 ≤ ad y ≤ }72 D2 = :yad{ ad y = 82 } D3 = :yad{ ad y = 92 } D4 = :yad{ ad y = 03 }

5D = :yad{ yad 13 = }

raeY Y1 = :raey{ year si a pael }raey 2Y = :raey{ raey si a nommoc }raey

hT e Ca isetr an udorp ct fo ht ese c tno a sni 04 ele .stnem ehT fu ll ed c noisi at lb e si given in Fi rug e ;8.7 ti has 22 lur es, c pmo ared ot ht e 63 of the s ce dno yrt . eR call f mor Cha tp er 1 ht e uq tse noi of ehw t eh r

a la gr e es t of test cases si nece ss a ylir be tt er ht an a ams ll er set. Here ew have a 22 lur e ed cisi no ta elb that vig es a clearer ip tc ru e fo the NextDate f nu c noit ht an eod s ht e 63 ur le ed c noisi tabl .e ehT tsrif f vi e ur el s d lae iw th 03 day ;shtnom on it ce ht at ht e el ap year c disno era it sno are rri eleva tn . hT e ne tx t ow sets of lur es 6( - 01 a dn 11 - 15) d lae htiw 13 day shtnom , where ht e f tsri f vi e d ae l iw th shtnom other ht an Dece bm er, a dn the es c dno if ve ed al iw th Dece bm er. hT ere are on lbissopmi e ur el s ni this

rop t noi of the ed c noisi table, hguoht ht ere si mos e re dnud ancy ht ta an e ff icie tn tset er thgim uq e oits n. hgiE t fo eht t ne lur es ylpmis increme tn the day. W uo ld we re la ly re riuq e e hgi t separa et et ts cases f ro siht bus f nu ct noi ? borP a ylb ton , tub ton e the i hgisn st we can get f mor ht e ed c noisi at lb e. Finall ,y eht last seven ur el s foc su no Fe urb a yr a dn el ap y ae .r

hT e de ic nois ta lb e ni rugiF e 2. 11 .8 si the ba sis f ro ht e cruos e c do e f ro ht e Next aD et f nu c noit in Cha tp er 2. sA an a edis , ht is ex ma lp e swohs woh doog te gnits ac n vorpmi e rgorp a mm i gn . A ll of t eh d ce si i no ta elb ana yl is s c dluo have eb en enod rud i gn t eh ed tai el d d se i ng fo eht NextD ta e f nu cti .no

C rro e dnops i gn Te ts aC ses :

esaC DI tnoM h yaD Year detcepxE tuptuO

1 irpA l 15 991 3 lirpA ,61 3991

2 irpA l 28 991 3 lirpA ,92 3991

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3 irpA l 29 991 3 lirpA ,03 991 3

4 irpA l 30 991 3 yaM ,1 3991

5 irpA l 31 991 3 elbissopmI

6 Jan. 15 991 3 aJ n. ,61 3991

7 Jan. 28 991 3 aJ n. ,92 3991

8 Jan. 29 991 3 aJ n. ,03 3991

9 Jan. 30 991 3 aJ n. ,13 3991

10 Jan. 31 991 3 beF . ,1 3991

11 eD c. 15 991 3 eD c. ,61 3991

12 eD c. 28 991 3 eD c. ,92 3991

13 eD c. 29 991 3 eD c. ,03 3991

14 eD c. 30 991 3 eD c. ,13 3991

15 eD c. 31 991 3 aJ n. ,1 4991

16 beF . 15 991 3 beF . ,61 3991

17 beF . 28 991 2 beF . ,92 2991

18 beF . 28 991 3 aM r. ,1 3991

19 beF . 29 991 2 aM r. ,1 2991

20 beF . 29 991 3 elbissopmI

21 beF . 30 991 3 elbissopmI

22 beF . 31 991 3 elbissopmI 2. 11 .3 Test C sesa f ro eht C simmo s noi melborP

sA we iw ll ees in siht se noitc , ht e C mmo si si no borP el m si ton we ll - vres ed by a d ce si oi n ta elb an la isy s. toN rprus i gnis , beca su e there si ve yr li ltt e ed isic no la l igo c in ht e lborp em. What we can od si see woh ed c si i no ta lb es ac n help to vorpmi e an dnu er- ps ecif ei d borp el m (the C noissimmo

lborP em is a doog e ax mp el of siht lla oot co mm no is taut i )no . oT g te ts arted, cer a ll ht ta t eh Co imm noiss borP el m si a rop it no of ht e la gr er Lock, otS ck, a dn Ba rr el e ax mple, ni ihw ch te el rg a sm

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.in re rp esent gni dro e sr are sent ot a central tniop . hT e f cnu it ano l iv ew of ht e en it er borp el m is t ah t t eh salespers s’no c mmo issi no si a f nu c noit fo eht te rgel :sma

t(F ele rg a )sm = c mo im noiss

sihT d ce sopmo es na rut a yll into eerht bus f nu cti :sno

)smargelet(1F = ,skcol( ,skcots )slerrab

,skcol(2F ,skcots )slerrab = selas

)selas(3F = noissimmoc

hT e mmoC si si no borP el m si j tsu ht e c noitisopmo of 2F a dn 3F . sA we saw ni Cha tp er 6, ht e upni t mod a ni si par it noit ed yb t eh il mi st no loc ,sk ots c ,sk a dn ba .slerr

elbairaV ecnelaviuqE sessalC rof 2F

kcoL L1 = kcol{ : kcol < }1 L2 = kcol{ : 1 ≤ ol ck ≤ 70 }

3L = :kcol{ ol kc > } 07

cotS k S1 = {s :kcot ots ck < 1 } S2 = ots{ c :k 1 ≤ ots ck ≤ 80 }

3S = :kcots{ kcots > 08 }

lerraB B1 = lerrab{ : ab rr le < 1 } B2 = rrab{ el: 1 ≤ ba rr el ≤ 09 }

3B = :lerrab{ lerrab > 09 }

hT ese e uq ivale ecn lc sa ses el ad di er c ylt to a op iss ble set fo c dno iti sno f ro a d ce noisi tabl :e

:1c kcol 1<

:2c 1 07 ≤ kcol ≤

:3c kcol 07 >

:4c kcots < 1

:5c 1 ≤ kcots 08 ≤

:6c kcots > 08

:7c lerrab < 1

:8c 1 ≤ lerrab 09 ≤

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:9c lerrab > 09

oS far, os doog . woN , hw at a snoitc sha ll we lutsop a et ? hT e ro i nig la borp el m ts atement ylno a rdd e ss es ht e c sa es rehw e c ,2 c5, a dn c8 a er a ll eurt . hT e er si on ni f mro ta noi f ro hw ta s dluoh ha pp en if a yn of ht e rema gnini c dno ti snoi are eurt . hT si si an e rorr of o im s is no ta eps cifica it no .emit A te ts er thgim esu ed c si i no ta lb es as ht e ks el note of an tni e vr ei w ot ed et r im en , f mor t eh su er c/ otsu em r, j tsu tahw a snoitc uohs ld be taken ni seht e c ri cum ts a cn es. hT( e eps cifier c dluo a osl

have enod )!siht eW mi hg t e dn pu w ti h ca snoit us ch as t eh f wollo i gn :

:1a :rorrE tsum lles ta tsael eno elfir kcol .

:2a :rorrE tonnac lles erom naht 07 elfir skcol .

:3a :rorrE tsum lles ta tsael eno elfir kcots .

:4a :rorrE tonnac lles erom naht 08 elfir skcots .

:5a rrE :ro tsum lles ta tsael eno elfir lerrab .

:6a :rorrE tonnac lles erom naht 09 elfir slerrab .

nA ana sisyl us ch as t sih r ia s se fo ll wo - pu uq e noits s. roF e ax mple, hw ta fi a nega vit e un mber we er e tn ered as the va ul e of no e of ht e var ai lb es? W dluo th ta i cidn ate r te nru ed ti e sm ? If os , the c pmo a yn

uow ld l ki ely hsiw ot re ud ce the sale ps e s’nosr co mm noissi . woN , hw ta if us ch a re noitcud vom ed the c immo iss no to a le ss fa rov ab el level, us ch as orf m the %02 level ot eht %01 level? We get into s imi lar uq se ti no s hw en ht e sales imil st are exc dee ed. woH s dluoh e cx sse sales eb ert ated? rA e yeht s mi ply ac irr ed vo er to ht e ne tx htnom ? hT e c pmo a yn mi thg rp efe r this, ceb a su e ti dluow like yl re cud e t eh c ssimmo i no leve ;l ylsuoivbo , the sa psel e nosr dluow erp fer ex ec ss sales to c no tri tub e to the hgih er co imm s is no lev le s. sihT sid c su is no it es ab ck to ht e Cha tp er 1 sid cu iss no no ps ecifie ,d

rgorp a emm d, a dn et ts ed behavi sro . hT e exa lpm es in ihw ch il mi st are aloiv et d are doog exa lpm es fo et ts cases that a er “ distuo e” the s ste fo s ep cified dna rgorp emma d behavi .sro (We im thg have a dd ed

a f ruo th c ,elcri ht at refe sr to hw at eht c motsu er yllaer wants.)

eW can od a lttil e m ro e f ro eht 3F s bu f nu c noit . Since there are rht ee c mmo issi no leve sl , ht ere iw ll c el arly eb t eerh e uq iva nel ce cl ssa .se

elbairaV ecnelaviuqE sessalC rof 3F

elaS s L1 = { selas : as les < 001 0 } L2 = :selas{ 0001 ≤ las es ≤ 0081 }

3L = :selas{ selas > 0081 }

hT ese e uq ivale ecn lc sa ses el ad to eht elpmis ed ic si no ta elb in rugiF e 7. :9

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ruO al ts ts ep is to ide itn fy a tc au l te ts cas se f ro t eh Commi noiss lborP me . oN te ht at we ca tonn s mi ply vorp ide va ul es fo as les taht dluow vig e su te ts ac ses f ro eht bus f nu cti no 3F . woN uoy w li l see woh ar ti mh e it ac yll rtnoc ived this lborp em laer ly .si Te ts Cases f ro eht noissimmoC lborP em:

saC tseT e ol cks cots ks rrab e sl as les noissimmoc

DT1 5 5 5 05 0 05

DT2 51 15 15 0051 571

DT3 52 25 25 0052 063

2. 21 senilediuG dna Ob oitavres ns

sA w ti h t eh hto er et ts i gn et c seuqinh , ed c noisi ta lb e based et ts gni skrow well f ro mos e a pp licat snoi kil( e Ne taDtx e) a dn is on t row th the t buor el rof hto e sr kil( e ht e C mo im noiss lborP me ). oN t

sirprus i gn ly, t eh si ut ati sno in ihw ch ti skrow llew are soht e hw ere ht ere si a tol of ed isic no ma gnik kil( e ht e rT ai gn el lborP me ), a dn oht se in hw ci h there a er ropmi at tn l go ci al rela hsnoit ips a gnom

i tupn va ir a lb es l( i ek eht Ne Dtx a et f nu c .)noit

1. ehT noisiced elbat nhcet euqi si detacidni rof snoitacilppa deziretcarahc yb yna fo eht :gniwollof

tnenimorp fI - nehT - eslE cigol

lacigol spihsnoitaler gnoma tupni selbairav

snoitaluclac gnivlovni stesbus fo eht tupni selbairav

esuac dna tceffe spihsnoitaler neewteb stupni a dn stuptuo

hgih citamolcyc )ebaCcM( ytixelpmoc ees( retpahC )9

2. noisiceD selbat t’nod acs le pu ev ry llew a( detimil yrtne elbat htiw n snoitidnoc sah 2n .)selur erehT are lareves syaw ot laed htiw :siht esu dednetxe yrtne noisiced ,selbat al iarbeg yllac yfilpmis ,selbat ”rotcaf“ egral selbat otni rellams ,seno dna kool rof gnitaeper snrettap fo noitidnoc .seirtne

roF erom no eseht ,seuqinhcet ees reppoT[ ]39 .

3. sA htiw rehto ,seuqinhcet noitareti spleh . ehT tsrif tes fo snoitidnoc dna snoitca y uo yfitnedi yam eb yrotcafsitasnu . esU ti sa a st gnippe ,enots dna yllaudarg evorpmi no ti litnu uoy era deifsitas htiw

a noisiced elbat .

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.in EXERC SI ES

.1 Deve pol a f alumro f ro t eh bmun er fo ntsubor e ss te ts c sa es f ro a f cnu it no fo n var ai lb e .s

.2 Deve pol a f lumro a rof the bmun er of tsubor srow t ac se et st ca es s rof a f noitcnu of n va ir a lb es.

.3 Ma ek a Ve nn d ai rg ma wohs i gn the er al ti spihsno a gnom test ca es s f mro dnuob a yr v la eu

anal sy i ,s ubor ts ne ss et gnits , row st ca es set it ,gn a dn tsubor tsrow ca es t tse i gn .

.4 What ha pp e sn if ew t yr to od uo t tup ra egn subor tne ss te ts i gn ? sU e ht e co mm noissi borp el m as an exa .elpm

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UNIT 3

PATH TSET GNI , AD TA LF OW TSET NI G 3 1. htaP tseT i gn

hT e tsid i gnihsiugn char ca te itsir c of urts c larut tes it gn tem sdoh si that ht ey are a ll based no the ruos ec c do e of the rgorp am eb i gn et ts ed, a dn ton no the ed f tini i no . ceB a su e of th si a tulosb e ba sis ,

st ur ct ru la te gnits me sdoht are v re y ema na lb e to ri uorog s de if n ti snoi , ma meht a cit al an la isy s, and rp ce si e meas ru eme tn . nI siht cha tp er, we iw ll e ax im ne the owt mo ts c nommo f smro of pa ht test .gni hT e t ce ygolonh behi dn ht e es has eb en ava ali lb e is cn e the m di - s0791 , a dn ht e nigiro ta sro of the es em sdoht won have c pmo ani se that am kr et ve yr us cce ss f lu t oo ls aht t implement ht e tec nh i uq es. B hto cet uqinh es ts a tr w ti h t eh rgorp ma rg a ;hp

Def ni i noit

viG en a rgorp am irw ett n in an impe ar t vi e rgorp a imm gn al ugn age, i st pr argo m hparg is a di er c det rg a hp ni hw ci h don es are e ti her e ritn e stateme stn ro fra mg ents fo a etats em tn , a dn e gd se re rp e tnes

f wol of c lortno . If i a dn j are don es in ht e rgorp ma rg a hp , there si an e gd e f or m don e i to don e j ffi the ts ta eme tn (fra mg e )tn c rro e dnops i gn to don e j ac n be ex ce tu ed mmi e id at le y fa ter the ats teme tn (fra mg e )tn c rro e gnidnops to edon .i C sno t ur c it gn a rgorp am rg a hp f mor a given rp ogram si an ae ys orp cess. ’tI s lli rtsu ated here w ti h t eh Pascal elpmi em tn at noi of the irT a lgn e rgorp am f mor hC ap et r 2. Line un mbe sr refer to s at te em stn a dn ats et ment frag em .stn hT ere si an eleme tn of j mgdu ent her :e mos ite sem ti si c vno e in ent ot peek a fragment ( il ke a B )NIGE as a sepa tar e edon , sa ta nil e 4. Other times si s ee sm b te ter to ni cl edu this w ti h a hton er rop it no fo a ts a et em :tn the NIGEB at il ne 31 c dluo r ae lly be me gr ed iw th the

NEHT no line 21 . eW lliw es e ht at ht is lat duti e colla sp es otno a uqinu e DD -Pa ht rg a hp , so t eh differe cn es cudortni ed yb id fferi gn j mgdu e stn are m too . A( tam hem ta ician dluow make the niop t that, f ro a given rgorp am, the er thgim be seve lar di nits tc rgorp am rg a shp , lla of ihw ch re ud ec ot a

uqinu e DD -P ta h rg a hp .) eW al os en ed to deci ed hw e ht er to a oss ciate don se htiw non -e ex c tu a lb e s at teme stn such as varia elb a dn t py e de lc ar ta :snoi he er ew od .ton A rgorp am rg a hp of this rgorp ma si vig en in rugiF e 3.1. doN es 4 t hguorh 7 a er a se uq ence, don es 8 t hguorh 11 are an FI - HT NE -ELSE c sno t ur ct (that et nimr ates no an FI clau es ), a dn don es 41 hguorht 61 are an FI - NEHT c no ts ur ct. sedoN 4 a dn 22 a er ht e rgorp ma ruos ec a dn is kn ,sedon

c rro e gnidnops to the si gn el e yrtn , gnis el e tix c tir eri .a . hT ere are on pool s, os siht si a rid ec et d ca yc il c rg a hp . ehT mi trop a cn e fo eht rgorp am rg a hp si that rgorp am e ex c snoitu c dnopserro to pa sht

f or m ht e s ruo ec to ht e knis don es. niS ec test c sesa f cro e the e ex c noitu of s mo e us ch rgorp am pat ,h we won have a ve yr exp il c ti descrip it no of ht e ler ta hsnoi ip be wt ee n a te ts case a dn ht e part of t eh

rgorp am ti exer ic ses. We a osl have an ele tnag , ht e ro e cit lla y r se ep cta lb e way to d ae l htiw ht e top e itn lla y la gr e bmun er of execut noi p ta sh ni a rgorp am. Fi rug e 3.2 si a g ar hp of a lpmis e rgorp ma ;

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.in ti is t ipy lac of the ki dn fo exa lpm e su ed to wohs ht e mi ssop ibil ti y of com lp te yle te gnits even lpmis e

rgorp a sm [Sch ca h .]39 nI siht rgorp am, ht ere are 5 pa sht f mor don e B to don e F in the i tn eri ro fo the l poo . If ht e pool yam

have pu ot 81 re ep it ,snoit ht e er a er os em 4. 77 t ir l noil di tcnits rgorp ma e noitucex pa .sht 1. pr margo irt a elgn tupni( , tuo p tu ) ; 2. RAV a, ,b c : regetni ; 3. sI A lgnairT e : eloob a ;n 4. IGEB N 5. nletirw ( retnE' eerht tni ege sr ihw ch era dis es of a tr ai n lg e ': ); 6. nldaer ,b,a( )c ; 7. nletirw ( S' i ed A si ,a,' S' id e B si ,b,' s' i ed C si c,' ) ; 8. FI a( < b + )c DNA (b < a + )c DNA (c < a + )b 9. NEHT sI A airT n lg e := URT E 01 . E ESL sI A irT a elgn =: FA ESL ; 11 . FI sI A irT a lgn e 21 . EHT N 31 . EB GIN 41 . FI a( = )b XOR a( = c) ROX (b = c) DNA NO (T ( b=a ) DNA ( =a )c ) 51 . NEHT etirW ln ( elgnairT' is Is cso eles') ; 61 . FI a( = b) A DN (b = )c 71 . NEHT etirW ln ( elgnairT' is Eq etaliu ral') ; 81 . FI a( >< b) NA D a( >< c) DNA (b <> c ) 91 . NEHT etirW ln ( elgnairT' is Sc la ene') ; 02 . DNE 12 . E ESL TIRW E NL ('N to a rT lgnai e )' ; 22 . END.

ugiF re 3.1 rgorP am hparG fo t eh Pa cs al rT ai elgn rgorP am

ugiF re 3.2 lirT il sno fo P ta sh 3.2 DD- shtaP

hT e be ts nwonk f mro fo urts tc ru al tes it gn is ba des no a c urtsno ct nwonk as a de noisic -to- ed c noisi pa ht DD( -Pa )ht [ iM ell r ]77 . hT e na em refe sr ot a se uq e cn e of ats et me tn s ht at, ni Mil el s’r ,sdrow begins htiw eht “ uo tway” of a ed isic no ts a met ent a dn e sdn wi ht the wni“ ay” of ht e ne tx ed c noisi

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.in s at tement. There are on in et lanr rb anch se in hcus a se uq e ecn , os the c rro e gnidnops c do e is kil e a

wor of od im on es lined pu so ht ta hw en ht e f sri t fa ll s, a ll ht e re ts in ht e se uq e cn e fall. lliM e s’r giro inal defi in it no skrow well f ro sec dno gener ta noi la ugn ages kil e FOR NART II , beca su e ed c si i no

am gnik ts atemen st us( ch as ari ht em it c sFI a dn OD l )spoo su e ts ateme tn al b le s ot fer er ot at egr t s at teme stn . W hti olb ck urts c rut ed la ugn ages P( asc la , adA , )C , the on t noi of ts atement fra mg e stn resolves ht e fid ficul yt of a iylpp gn liM el s’r giro inal ed f noitini —othe esiwr , we e dn pu iw th rgorp ma rg a shp in hw ci h emos ats et ments a er me bm e sr fo erom than eno DD -Pa .ht

eW iw ll define DD -Pa ht s in te smr fo p ta sh fo don es in a rid ce ted rg a hp . We im thg c lla ht ese ap ths ch ia sn , hw ere a c niah is a path ni hw ci h the i in it la a dn te mr inal don es are sid it tcn , a dn eve yr in et oir r

don e has i dn e rg ee = 1 a dn uo tde rg ee = 1. citoN e ht at ht e itini al don e si 2- nnoc ce ted ot eve yr other don e in ht e cha ni , a dn ht ere are on tsni an sec of 1- ro 3-c nno ce ted ,sedon as s nwoh in Fi rug e 3. .3

hT e le htgn un( mber of gde e )s of the cha ni in rugiF e 3.3 si 6. We can have a degenerate case of a ch ia n that is fo le htgn ,0 ht at i ,s a cha ni c sno si ti gn fo e ax c ylt eno edon a dn on e gd e .s Def tini i no A DD - aP th is a cha ni ni a rgorp am rg a hp us ch t ah t

esaC :1 ti stsisnoc fo a elgnis edon htiw gedni = ,0

esaC :2 ti stsisnoc fo a elgnis edon htiw gedtuo = ,0

esaC :3 ti stsisnoc fo a elgnis edon htiw gedni ≥ 2 ro gedtuo ≥ ,2

esaC :4 ti stsisnoc fo a elgnis edon htiw dni ge = 1 dna gedtuo = ,1

esaC :5 ti si a lamixam niahc fo htgnel ≥ 1.

aC ses 1 a dn 2 se ta hsilb eht euqinu ruos ce a dn knis don es fo eht rgorp am rg a hp fo a st ur tc ru ed rgorp am as laitini a dn final DD -P ta .sh aC es 3 d ae sl w ti h c lpmo ex don e ;s it a ss ru es ht at no edon is

c no t nia ed ni erom than eno DD -Pa .ht Ca es 4 is en eded f ro “ trohs rb anches”; it al os erp es vr es t eh eno fra mg e ,tn eno DD -P ta h rp i .elpicn aC es 5 si eht “ mron al case ,” in hw i hc a DD -P ta h si a is elgn

e ,yrtn elgnis e tix se uq e ecn fo don es a( cha .)ni T eh “max mi ”la pa tr of eht ac se 5 ed fi in t noi si su ed to dete enimr eht f ni al edon fo a mron al non( - virt lai ) chai .n

elbaT 1 sepyT fo DD - shtaP ni erugiF 1.9 margorP hparG sedoN

DD - htaP emaN esaC fo noitinifeD

4 rif st 1

5 - 8 A 5

9 B 4

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01 C 4

11 D 3

21 - 41 E 5

51 F 4

61 G 3

71 H 4

81 I 3

91 J 4

02 K 3

12 L 4

22 l tsa 2

sihT si a c lpmo ex ed finiti no , os we ll’ a ylpp ti to the rgorp am rg a hp in Fi rug e 9.1. doN e 4 si a aC se 1 DD -Path, we’ll ca ll it “f tsri ”; limis arly, don e 22 is a Case 2 DD -Pa ht , a dn we ll’ call ti l“ a .”ts

doN es 5 hguorht 8 are a esaC 5 DD -Pa ht . We wonk ht at don e 8 si ht e la ts don e in iht s DD -P ta h beca su e it si the al ts don e th ta rp ese vr es ht e 2-c enno ctedne ss porp erty of the cha ni . If we went be dnoy don e 8 to inc dul e don es 9 a dn ,01 ht ese uow ld ob th eb 2-c nno e tc ed to eht re ts fo eht cha ,ni

tub ht ey are no ly 1-c nno ce ted ot e ca h hto er. If we ts ppo ed at don e 7, we dluow vio al te t eh “ma lamix ” c tir e noir . doN e 11 is a Case 3 DD - taP h, ihw ch f ro ec s don es 9 a dn 01 ot be i udividn al

DD -P ta sh by ac se 4. doN es 21 t hguorh 41 are a sac e 5 DD -Pa ht yb ht e same re nosa i gn as f ro don es 5 - .8 doN es 41 t hguorh 02 c rro e dnops to a se neuq ce of FI - NEHT ts atem .stne doN se 61 a dn 81 a er

htob aC se 3 DD -P ta sh , dna this f ro ec s don se 51 , 71 , a dn 91 ot be Case 4 DD -Pa sht . doN e 02 is a aC se 3 DD -Pa ht , a dn don e 12 si a C sa e 4 DD -P ta h. llA of ht is si s mmu a zir ed ni Ta lb e 1, hw ere t eh DD -P ta h n ma es c rro e dnops ot eht DD -Pa ht rg a hp ni ugiF re 3.4. Pa tr of the c no f noisu htiw siht

exa lpm e si t tah the t ir a gn el borp l me is igol c tni e visn e a dn c tupmo ita no ally ps a esr . sihT c nibmo ati no iy e sdl ma yn trohs DD -Pa .sht fI the NEHT a dn ESLE cla su es c tno ia ned IGEB N

. . DNE olb c sk fo c tupmo a noit al ats et ment ,s we dluow have l gno er DD -Pa sht , as we od ni t eh com noissim borp el .m We ac n won ed if en eht DD -Pa ht rg a hp fo a rgorp a .m

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ugiF re 3.4 DD -Pa ht hparG f ro t eh irT a lgn e gorP ar m Def tini i no

viG en a rp o rg ma tirw et n ni an mi perative la ugn ag ,e ti s DD - htaP arg ph si the rid etce d rg a hp in ihw ch don es are DD -Paths of i st rgorp ma g ar hp , a dn e gd es re rp e tnes c no t lor fl wo eb t neew

us cce ross DD -Pa sht . nI ef ef ct, the DD -Path rg a hp si a f ro m of c dno e sn a noit rg a hp ( ees Chap et r )4 ; in siht c dno e sn ati no , 2- nnoc ected com nop ents a er c lo al sp ed into udividni al don es ht at c rro e dnops ot

aC se 5 DD -Pa sht . hT e gnis el don e DD -Pa sht (c rro e dnops i gn ot aC s se 1 - )4 are re riuq ed to rp ese evr the c vno en it no that a ts a et em tn ro( ts at me ent frag em )tn si ni exa tc ly no e DD -P ta h. Wi uoht t t sih c vno ent noi , we e dn pu wi ht ra ht er c ysmul DD -P ta h rg a shp , in ihw ch s mo e ts at eme tn (fra mg ent )s a er in several DD -P ta .sh Teste sr t’ndluohs be mitni idated yb th si orp ec ss — there are hgih uq a ytil co emm rcial t sloo t tah generate ht e DD -Pa ht rg a hp of a given rp o rg am. hT e ve srodn make rus e aht t ht ie r udorp c st krow f ro a wide vari yte of rgorp a imm gn al ugn ages. nI arp tc ice, ’ti s r ae nos a lb e to make DD -Path rg a shp f ro

rgorp a sm pu to a ob tu 001 ecruos nil e .s Be dnoy tha ,t tsom et ts e sr l koo f ro a .loot 3.3 T tse C egarevo scirteM

hT e nosiar ’d ê rt e of DD -Pa sht si ht ta ht ey ena elb ve yr rp ce si e d cse ri tp i sno of et ts c vo era eg . eR ac ll (f mor hC a tp er )8 th ta no e of ht e f dnu ame tn al lim ti ta snoi of f nu cti no al te gnits si ht at ti is mi ssop i elb to wonk e ti her ht e exte tn of re dnud ancy ro ht e ssop ib li yti of ga sp c rro e gnidnops ot ht e way a es t fo f tcnu i ano l te ts cases e rex esic s a rgorp ma . aB ck ni hC ap et r 1, ew had a Ve nn d ai rg am gniwohs re al spihsnoit ma gno eps cifie ,d rgorp emma d, a dn et ts ed beha roiv s. T se t c vo erage em t ir cs a er a dev ci e ot em a erus t eh e tx e tn to ihw ch a set fo test ac ses c vo ers ro( exer ic es s) a rgorp a .m

hT e er a er seve lar wide yl ac ec tp ed t se t c vo era eg m te ir c ;s tsom fo t esoh ni Table 2 a er eud ot eht ea ylr krow fo .E F. relliM [ iM ller .]77 Ha gniv an gro anized v ei w fo eht e tx e tn ot ihw ch a rgorp ma si te ts ed ma sek it elbissop ot sen is bly mana eg t eh te gnits orp ec .ss tsoM auq il yt gro aniza snoit won

e epx ct eht C1 me cirt DD( -Pa ht c vo erage) as t eh muminim acce tp a elb level fo te ts c vo era .eg ehT s at teme tn c vo era eg me cirt C( 0) si its ll wide yl a pecc et :d ti is ma dn ated yb ISNA tS a dn a dr ,B781 a dn has eb en su ed cus c sse f yllu t tuohguorh I MB ecnis eht dim - .s0791

elbaT 2 larutcurtS tseT egarevoC scirteM Me cirt

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noitpircseD fo egarevoC

C0 yrevE tnemetats

C1 yrevE DD - htaP etaciderp( )emoctuo

C1p yrevE etaciderp ot hcae emoctuo

C2 C1 egarevoc pool + egarevoc

Cd C1 egarevoc + yreve eped tnedn riap DD fo - shtaP

C CM C elpitluM noitidnoc egarevoc

Cik yrevE margorp htap taht sniatnoc pu ot k snoititeper fo a pool yllausu( k =

2)

Cs tat yllacitsitatS“ ”tnacifingis noitcarf fo shtap

C

∞ llA elbissop noitucexe shtap

hT e es c vo erage em tr ci s mrof a l ta it ec s( ee Cha retp )01 in hw ci h mos e are e uq ivale tn , a dn os em are mi plied yb othe sr . ehT tropmi a cn e of ht e al t it ec si that ht e er are always fa tlu t py es t tah can eb

rev ae led at no e level, a dn ac n escape de et cti no yb ni fe roir leve sl of et ts i gn . E. F. iM ll er sbo e vr es that hw en DD -P ta h c vo erage si tta nia ed yb a es t fo te ts ca es s, ylhguor %58 of lla af lu st are reve la ed [ lliM er .]19 3.3.1 eM tr ci saB ed Te gnits

hT e te ts c vo erage em t cir s ni Ta lb e 2 te ll us hw at ot te ts , ub t on t woh to te ts ti . nI siht s ce ti no , ew at ek a c ol es r kool ta te uqinhc es that exerc si e ruos ec c edo in te smr of the m te ri sc ni Ta lb e 2. eW

m su t k ee p an mi trop ant di cnits it no ni mi :dn Mille s’r test c vo erage m te cir s a er based no rgorp am rg a shp in ihw ch don es ra e fu ll ts atements, hw e er sa ruo f lumro ita no la l swo ts ate em nt frag em stn to eb don e .s roF eht amer i dn er fo this es c ,noit eht s tat eme tn frag em tn f ro mul noita is “in fe fec .”t

S tnemetat and etaciderP T gnitse Beca su e ruo f lumro ita no la swol ts ta me e tn fra mg e stn to be i udividn la don es, the s at teme tn a dn

rp e cid ate lev le s C( 0 a dn C1) ot c llo a sp e otni no e c sno idera noit . nI ruo rt i gna el exa lpm e es( e ugiF re 3. )1 , don es 8, 9, a dn 01 are a c pmo lete Pa cs al FI - NEHT -E SL E ts ateme .tn If we re riuq ed don es to c rro e dnops to full ts ta eme stn , ew c dluo ex ce u et uj ts no e of the d ce si i no a tl e nr a vit es a dn satisfy t eh s at teme tn c vo erage c tir e noir . Beca su e we la wol ts a et em tn fra mg ents, it is n“ ta ru al” ot vid ide us ch a s at teme tn into t erh e don es. gnioD os re stlus in rp edicate tuo c emo c vo erage. Whe ht er ro on t ruo c vno ent noi is f wollo ed, ht ese c vo erage m te cir s re riuq e ht at we fi dn a tes of te ts cases us ch ht a ,t

hw en ex ce tu e ,d eve yr edon fo t eh rgorp am rg a hp si trave sr ed at el a ts no c .e

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DD - htaP T gnitse When eve yr DD -P ta h si traver es d ht( e C1 m rte ci ), we wonk ht at each iderp ca et tuo c mo e has eb en e ex c tu e ;d siht ma stnuo ot rt ave gnisr eve yr e gd e in t eh DD -Pa ht rg a hp ro( rgorp ma rg a )hp , as

soppo ed to ju ts eve yr no ed . roF FI - HT EN a dn IF- NEHT -E ESL etats ments, siht m ae sn ht at ob th t eh t ur e a dn the fa sl e rb anch se are c vo ered (C1p c vo erage). roF ESAC s at t eme stn , e ca h cla su e si c vo ered. eB dnoy siht , ti si su eful to a ks hw at e sl e we im thg od ot test a DD -Path. oL nger DD -P ta sh genera yll re rp ese tn c lpmo ex com tup a snoit , hw i hc we ac n ri thg ly c sno i red as i udividn al f nu itc sno .

roF us ch DD -P ta sh , ti may be a irporpp a et ot a ylpp a bmun er of f nu c noit al t tse s, espe ic a yll t soh e f ro dnuob a yr a dn ps ecial value .s

Dep ne d tne sriaP of DD - shtaP

hT e Cd em t ir c f hsero a wod s ht e da at f wol tes it .gn hT e tsom co mm no depe dn ency ma gno pa ri s fo DD -P ta sh si the d fe ni /e reference rel ta noi ship, ni hw ci h a var ai lb e is de nif ed er( ceives a v la ue) ni no e DD -P ta h a dn si refer e cn ed in a hton er DD -Pa ht . hT e ropmi at cn e of ht ese depe dn encies si aht t

they are c ol es yl re al et d ot ht e borp el m of fni ae lbis e shtap . We have doog ex ma p el s of depe dn e tn pa ri s of DD -Pa :sht in Fi rug e 9.4, B a dn D are us ch a p ia r, os are DD -Pa sht C a dn L. lpmiS e DD - Pa ht c vo erage im thg ton exer ic se ht ese depe nedn ic se , suht a ed eper alc ss of fa stlu uow ld ton eb rev ae led .

itluM p el C noitidno C egarevo

kooL c sol e yl ta ht e com dnuop c idno it sno in DD - aP sht A a dn E. taR her t nah mis ply traver is gn us ch rp e cid ates ot t rieh EURT a dn FA SL E tuo co em s, we s dluoh i vn e gits eta the ffid ere tn wa sy th ta e hca

tuo c mo e ac n co c .ru nO e ssop ib li yti si ot am ke a hturt ta lb e; a com dnuop dnoc i noit of erht e s mi elp c dno i snoit uow ld have ei thg wor s, y ei gnidl e gi ht et st cases. onA ther sop is b li yti si ot re rgorp ma com dnuop rp e id ca et s otni nes et d si lpm e FI - HT EN-E ESL log ci , ihw ch will r se u tl ni m ro e DD -P ta sh to c vo er. We ees an retni es it gn rt ade-of :f ts ateme tn c lpmo exi yt ve susr pa ht com lp e ix t .y elpitluM c dno i noit c vo erage a russ es ht at siht com lp e ytix i t’ns swe tp dnu er eht DD -Pa ht c vo erage .gur L poo C egarevo

hT e c dno e tasn i no rg a hp s vorp ide su wi ht an e gel ant r se olut noi to ht e lborp e sm of t gnitse .spool pooL et s it gn has b ee n iduts ed e tx en is ve yl , a dn w ti h doog r ae nos — l poo s are a ylhgih fa lu t enorp

rop t noi of cruos e c edo . oT sta tr , ht ere si an a gnisum at onox my fo l spoo in [B ie zer :]38 c cno atenate ,d ne ts e ,d a dn rroh ibl ,e nwohs in Fi erug 3. .5

ugiF re 3.5 C no ac ten ta ,de Nes et ,d a dn onK ett d spooL

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.in C cno atenated spool are ylpmis a se uq e cn e of disjoint l spoo , hw ile ne ts ed spool are us ch t ah t no e si c no t nia ed disni e a hton er. lbirroH e l spoo ca tonn o cc ru hw en ht e urts c derut rgorp a gnimm rp ece stp are fo ll wo ed. When ti si lbissop e to rb anch i otn ro( tuo f )mor t eh ddim el of a pool , a dn the es

rb anches are i tn e nr al to other spool , ht e re us lt is B ie ze s’r lbirroh e pool . htO( er ruos ces ed f ni e t sih as a onk t— woh a irporpp a et .) hT e s mi lp e v ei w of pool et gnits is t tah eve yr l poo ovni lv se a ed c isi ,no a dn we n ee d to test ob th uo ct mo se of ht e dec isi :no no e is ot trave sr e the pool , a dn ht e hto er si ot e ix t

ro( ton e tn e )r ht e pool . sihT is ac er f yllu rp oved ni [ uH a gn 97 ]. We can osla take a dom ified dnuob a yr va ul e a orpp a hc , hw ere the pool dni ex si given ti s m mumini , nimon al, a dn ma mumix

v la ues ( ees Cha tp er )5 . eW ac n hsup ht is f htru e ,r ot fu ll dnuob a yr v la ue gnitset a dn even subor tne ss et ts i gn . If the ydob fo a s mi ple pool si a DD -Pa ht ht at perf mro s a c xelpmo calcu al noit , this dluohs

a osl be t se te ,d as disc ssu ed a vob e. cnO e a pool has b ee n te ts ed, ht e te ts er c dno e sn es it into a is elgn edon . If pool s are ne dets , ht si corp e ss si re ep ated ts ar it gn htiw the i nn e somr t pool a dn gnikrow wtuo ard. hT is re us stl ni ht e mas e mu itl ilp tic y of te ts ac ses we f dnuo htiw dnuob a yr v la ue anal sy i ,s ihw ch makes se esn , ceb a su e each pool i dn ex var ai lb e acts il ke an upni t va ir able. If pool s a er

ttonk ed, it liw l be cen se sa yr to car fe u ll y ana zyl e ht me ni et rms of ht e da wolfat m te sdoh disc ssu ed ni Cha tp er 01 . sA a rp e iv we , c disno er ht e ni if nite pool ht at c uo ld co c ru if no e l poo at pm e sr wi ht ht e v la ue fo t eh other l s’poo dni e .x 3.3. .2 Test evoC r ega anA l zy re s C vo erage ana zyl e sr are a cl ssa of te ts t sloo th ta fo fer aut mo ated troppus f ro th si a aorpp ch ot te gnits

am nagement. htiW a c vo erage a an l zy er, ht e et ets r snur a es t of test cases no a rgorp ma that has eb en “ murtsni e tn ed” yb ht e voc erage an la zy er. hT e na la zy er then su es ni f ro itam no cudorp ed yb ht e in ts mur e tn a noit c do e ot generate a c vo erage re trop . nI the c nommo c sa e of DD -P ta h c vo era eg , f ro ex ma ,elp eht i sn t mur e tn ta noi ide fitn ei s a dn labe sl la l DD -Pa sht ni an nigiro la rgorp ma . When t eh in ts mur e tn ed rgorp am is e ex c tu ed htiw te ts case ,s ht e a zylan er at lub ates the DD -P ta sh rt ave sr ed yb each te ts c esa . nI this way, ht e te ts er can e px e ir em tn w ti h ffid erent s te s of et ts ac ses to deter im en t eh c vo era eg fo e ca h se .t 3.4 B sisa htaP T gnitse

hT e m ta he am it cal noiton fo a “basis” has a rtt act vi e bissop ili it es f ro st ur c rut la te ts i .gn Ce tr a ni stes can have a ba sis , a dn hw en they od , the ba sis has v re y ropmi at tn porp ert sei htiw re eps ct ot the ent eri s te . Ma ht e am it ic a sn ausu l yl define a bas si in te smr of a rutcurts e called a v“ tce ro aps c ”e , ihw ch is a set of ele em nts c( la led v tce )sro a dn ihw ch sah po era snoit taht c rro e nops d to multipli ac noit a dn a dd i noit ed if ned f ro eht v tce .sro If a half zod en hto er c ir et ir a a ylpp , the st ur tc ru e si s ia d ot eb a v ce rot ps a ec , a dn a ll ev c rot spaces have a bas si (in fact they may have es veral base )s . hT e ba is s of a v ce rot aps ce si a set of v ce srot us ch aht t ht e v ce srot are dni epe dn ent of e ca h hto er a dn ht ey s“ pa ”n the e tn ire v tce ro aps ec ni ht e es sn e ht at a yn other v tce ro ni ht e aps ce ac n be e rpx e ss ed in et r sm fo the ba sis v ce srot . suhT a set fo sab si ve rotc s s mo e woh re rp ese stn “t eh e ss ence” of ht e f llu v tce ro ps ace: eve gnihtyr e sl e in ht e space can be e rpx e ss ed in te smr of ht e basi ,s a dn if no e sisab e el em tn si de el ted, this ps a gninn porp erty si lo ts . hT e top e itn al f ro t itse gn si that, fi we ac n iv ew a rp o rg ma

as a ve rotc ps a ec , then the ba is s f ro us ch a caps e uow ld eb a ve yr tni ere gnits s te of le me e tn s ot te .ts If t eh basis is “ KO ”, ew c dluo poh e that eve yr th gni ht at can be e rpx e dess ni et smr fo t eh ba sis si

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.in a osl “ KO ”. nI th si es tc noi , we ex ma ni e the ae ylr krow of ohT am s McCa eb , ohw r ce ngo ized siht

ibissop il yt in t eh mid- .s0791 3.4.1 cM baC e s’ B sa is htaP Me doht Figure 3.6 is taken f mor [Mc aC be 28 ]; ti si a di er cted rg a hp hw ci h ew hgim t take to be the rgorp am rg a hp ro( the DD -P ta h rg a )hp of mos e rgorp am. roF the c vno e in en ec of r ae de sr ohw ha ev

enc tnuo ered siht exa lpm e e sl e hw e er ([McCa eb ]78 , [Pe yrr 78 ] ,) t eh giro inal on at noit f ro sedon a dn e gd es si re ep ated he .er toN( ci e that siht si ton a rg a hp de vir ed f mor a urts c rut ed rgorp ma : don es B a dn C are a l poo iw th owt e stix , a dn ht e e gd e f or m B ot E si a rb anch into the FI - NEHT etats ment ni

don es D, E, a dn F. hT e rgorp am od se have a si lgn e ent yr A( ) a dn a gnis le ex ti )G( .) cM Cabe ba es d h si v ei w fo te gnits no a am j ro er su tl f mor rg a hp the yro , ihw ch ts ates that eht cyc mol ta ic bmun er s( ee hC ap et r )4 of a gnorts ly c nno ce ted rg a hp is ht e bmun er of li en a ylr dni epe dn ent ric c iu st in t eh rg aph. A( ic rcuit si is m li ar to a cha :ni on in et nr al l spoo ro ed c snoisi , tub the ini it al don e si t eh et rm ni al edon . A ric cu ti si a s te of 3-c nno ce ted don es.) eW can a wl a sy c er ate a st gnor ly c nno tce de rg a hp by a gnidd an e gd e f mor eht (eve )yr si kn node ot ht e (eve )yr ruos ec edon . oN( t ci e that, if t eh

si lgn e e yrtn , lgnis e e ix t rp ecept si viol ta ed, we rg ae ylt ni c er ase ht e cy lc mo a it c un mber, ceb a su e ew n ee d ot a dd e gd es f mor each knis don e ot ae ch ruos ce edon .) rugiF e 9.7 swohs the luser t of gniod thi ;s it sla o c atno i sn e egd la eb ls that a er su ed in eht disc noissu that f .swollo

hT ere si s mo e c no f su noi ni the l ti erat ru e a tuob t eh c rro ect f lumro a f ro cyc ol tam ic c pmo el ytix . moS e ruos ec s vig e the f ro m lu a as )G(V = e - n + p, hw eli hto e sr su e ht e f ro m lu a )G(V = e - n + 2p;

eve noyr e a rg ees ht at e si eht un mber fo e gd e ,s n si the un mber of ,sedon a dn p is ht e un mber of c enno cted re snoig . hT e noc fu is no a pp are tn ly co em s f mor the tra sn f ro am noit of an a rtibr a yr dir etce d rg a hp us( ch as ht e no e in Figure 9. )6 ot a st gnor ly c enno cted di er cted rg a hp bo at ni ed yb a gnidd no e

e gd e f mor ht e knis to ht e cruos e edon (as ni Fi rug e 3. )7 . ddA i gn an gde e lc ea ylr affe tc s v la eu com tup ed yb ht e f ro mula, tub it uohs l t’nd fa fe tc ht e bmun er of ic rc iu ts. Here’s a way ot r se olve eht a pp are tn inc tsisno ency: hT e bmun er of nil ae rly i dn epe dn ent p ta sh f or m ht e ruos ec don e to ht e nis k

edon ni Fi rug e 3.6 is

a dn eht un mber fo nil ae ylr i dn epe dn e tn ic rc iu ts ni eht rg a hp ni rugiF e 3.7 si

ugiF re 3.6 CcM abe s’ noC t lor rG a hp

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.in

ugiF re 3.7 CcM abe s’ Derived St gnor ly nnoC ce det rG a hp

ehT cy lc mo a cit c pmo el ix ty fo eht ylgnorts c enno cted rg a hp ni rugiF e 3.7 is ,5 suht the er a er fi ev il en a ylr i dn epe dn e tn .stiucric fI ew won de el te t eh a dd ed e gd e f mro edon G ot edon ,A ht e es f evi

c ri c stiu eb c emo five li en a ylr i dn epe dn e tn ap t sh f mor edon A ot edon .G nI ms all rg a ,shp ew ac n usiv la yl ide itn fy dni epe dn e tn .shtap He er ew ide itn fy pa sht as es uq e cn es fo don e :s

:1p ,A G ,C ,B

:2p ,A ,B ,C G ,C ,B

:3p ,A ,B G ,F ,E

:4p ,A ,D G ,F ,E

:5p G ,F ,D ,A

eW can force siht be gninnig ot ool k il ke a v ce rot ps eca yb d fe gnini on t snoi of a tidd i no a dn sc la ar mu tl ilpi c :noita p ta h a dd i noit si s mi ply no e pa ht fol wol ed yb a on ther pa ht , a dn um l cilpit ati no c rro e sdnops ot repe it it sno of a pat .h W hti ht si lumrof a noit , McCa eb ar vir es ta a ev c rot caps e fo

rgorp am p ta sh . siH rtsulli ita no of eht ba sis pa tr of this frame krow is t tah ht e pa ht A, B, C, ,B E, ,F G si the basis s mu 2p + 3p - 1p , a dn t eh p ta h A, B, ,C B, C, ,B ,C G si ht e li en ar combin ta noi 2p2 - p1. tI is ea is er to es e siht a dd ti noi htiw an in ic de cn e m ta xir es( e Chap et r )4 ni hw ci h swor c rro e dnops to pat sh , a dn columns c rro e dnops ot e gd es, as in Ta lb e 3. hT e e irtn es ni siht ta lb e a er

tbo a ni ed yb f gniwollo a pa ht a dn gniton hw ci h e gd es are traver es d. Pa ht 1p , f ro ex ma elp , rt avers se e gd es 1, 4, a dn 9; lihw e pa ht 2p traver es s ht e f gniwollo edge es uq enc :e 1, 4, 3, 4, 9. Sin ec e gd e 4 si trave sr ed eciwt yb p ta h ,2p ht at is eht ent yr f ro eht e gd e 4 c .nmulo

elbaT 3 egdE/htaP lasrevarT htap \ desevart segde

1 2 3 4 5 6 7 8 9 01

:1p ,A ,B ,C G 1 0 0 1 0 0 0 0 1 0

:2p ,A ,B ,C ,B ,C G 1 0 1 2 0 0 0 0 1 0

:3p ,A ,B ,E ,F G 1 0 0 0 1 0 0 1 0 1

:4p ,A ,D ,E G ,F 0 1 0 0 0 1 0 1 0 1

:5p ,A ,D G ,F 0 1 0 0 0 0 1 0 0 1

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:1xe ,A ,B ,C ,B ,E G ,F 1 0 1 1 1 0 0 1 0 1

:2xe ,A ,B ,C ,B ,C ,B ,C G 1 0 2 3 0 0 0 0 1 0

eW ac n ch ce k ht e dni e dnep e cn e of pat sh 1p - 5p yb examini gn the fi tsr vif e swor of th si i cn idence am xirt . hT e dlob e irtn es s woh e gd es that a epp ar ni exa yltc no e p ta h, os paths 2p - 5p mu ts be

i dn epe dn ent. Pa ht 1p si i dn epe dn e tn of la l of ht e es , beca su e a yn a tt e pm t ot e rpx e ss 1p in te smr fo the others cudortni es wnu an et d e gd es. noN e can be deleted, a dn these five pa sht span the s te of all pa sht f or m don e A ot don e G. At siht op i tn , uoy uohs ld ch ce k ht e li en ar c bmo in ta snoi fo t eh t ow exa elpm p ta .sh ehT a dd noiti a dn mul ilpit c noita a er perf mro ed no eht c ulo mn ent ir e .s McCabe ne tx deve spol na a mhtirogl ci corp e rud e ( ac ll ed the “ba les ine me doht ”) to dete nimr e a set of b isa s pat sh . ehT me doht be snig htiw t eh es lecti no fo a “bas ile ne” pa ht , ihw ch uohs ld c erro sp dno to some “ mron al c ”esa rgorp am e ex c noitu . sihT can be mos e hw at a ibr tra yr ; McCabe a sesivd c ooh is gn a pa ht iw th as ma yn d isice no don es as elbissop . Ne tx the base nil e p ta h si r te car ed, a dn ni t nru each ed c si i no si “ lf i pp e ,”d th ta is hw en a don e of uo tde rg ee 2≥ is reache ,d a id ffere tn e egd m su t be taken. Here ew f wollo cM aC be s’ exa elpm , in ihw ch he fi sr t op ts u al tes the ap th hguorht

don es A, B, C, B, E, F, G as the baseli en . hT( is saw e rpx e ss ed in te smr of pa sht 1p - 5p ae ilr er.) ehT f tsri de sic i no don e tuo( rged ee )2≥ ni siht pa ht is don e A, so f ro the next sisab pa ,ht we rt ave sr e e egd 2 i tsn ead of e gd e 1. We g te the ap th A, D, E, F, ,G hw ere we r te race don es E, F, G ni pa ht 1 ot be as

im n mi la ly differe tn as elbissop . roF ht e next p ta h, we can f wollo ht e es c dno path, a dn at ke ht e hto er d ce si i no tuo c mo e fo don e D, ihw ch vig es su ht e p ta h A, D, ,F .G woN ylno d ce noisi don es B a dn C have on t b ee n fli pp e ;d gniod so iy le sd the tsal t ow ba sis p ta sh , A, B, E, F, G a dn A, B, C, G. oN t eci that t sih set of bas si pat sh si d itsi nct f or m the eno in Ta lb e :3 this si ton lborp em ta ci , eb ac su e ht e er si on re riuq eme tn ht at a ba sis eb .euqinu

3.4.2 O esb r snoitav no cM baC e s’ B sisa htaP eM doht If uoy had t lbuor e f wollo i gn os em of the sid cus nois no sisab p ta sh a dn smus a dn udorp tc s fo the ,es

uoy may have lef t a ha nu t gni ks e itp c msi , mos e gniht a gnol ht e nil se of H“ ere s’ a hton er ca ade cim vo e ilpmisr fic noita of a real- row ld lborp e ”m . Ri ylthg so, beca su e ht ere are t ow maj ro os ft s stop ni

the cM aC be v ei :w no e si that tes it gn the tes of ba sis p ta sh si us ff ci ie tn s’ti( on t), a dn ht e hto er has ot od htiw the yoga- il ke c no t snoitro we went hguorht ot make rgorp am pa sht l koo like a v ce rot s .ecap

McCabe s’ exa lpm e t tah ht e pa ht A, B, C, B, C, B, C, G si the nil ae r c bmo in ita no 2p2 - 1p si v re y snu a fsit act ro y. What seod the 2p2 pa tr em an? exE cu et pa ht 2p eciwt ? Y( es, acc gnidro to ht e ma ht .)

vE en wor es , hw at eod s ht e - 1p part aem n? xE ec tu e pa ht 1p ab c wk a sdr ? odnU the mo ts er ec nt e ex cut noi of 1p ? ’noD t od 1p next it em ? M ta eh tam ical hpos i irts es l ki e ht is are a r ae l t nru -off ot rp ca noitit e sr l koo i gn f ro tulos i sno ot t rieh ve yr real lborp e sm . oT get a be tt er dnu ersta dn i gn of ht e es

borp el ,sm we’ll og ab ck ot eht t ir a gn el rgorp am ex ma .elp S at tr w ti h the DD -Pa ht rg a hp fo eht irt a lgn e rgorp ma in rugiF e 9. .4 We begin htiw a baseline pa ht that c rro e dnops s to a cs alene t ir a lgn e, say htiw dis es 3, 4, 5. sihT et ts case liw l trave sr e ht e ap th .1p

woN fi we pilf ht e ed c si i no at don e A, we g te pa ht 2p . C tno i gniun ht e orp ec erud , we ilf p t eh d ce si i no ta don e D, hcihw iy e sdl the path 3p . woN we c no t uni e to ilf p deci nois don es in ht e bas ile en

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.in pa ht 1p ; ht e next don e iw th dtuo e rg ee = 2 si don e E. When we f pil don e E, we get ht e ap th 4p . Ne tx we f il p don e G ot g te 5p . Finally, w( e wonk we r’ e enod , eb ca su e there are no ly 6 ba sis pa )sht we flip edon I ot g te .6p hT is corp e erud iy le sd eht f gniwollo ba is s p ta :sh

:1p A-B-D-E-G-I-J-K- tsaL

:2p A-C-D-E-G-I-J-K- tsaL

:3p A-B-D-L- tsaL

:4p A-B-D-E-F-G-I-J-K- tsaL

:5p A-B-D-E-F-G-H-I-J-K- tsaL

:6p A-B-D-E-F-G-H-I-K- tsaL

miT e f ro a er la ity che :kc if uoy fol wol pa sht 2p , 3p , 4p , 5p , a dn 6p , oy u f dni that they are all i fn ea lbis e. Pa ht 2p si i fn ae elbis , beca su e pa ss i gn hguorht don e C aem sn ht e dis es are ton a irt a ,elgn os non e of ht e se uq el ed c si i sno can eb taken. imiS al rly, in 3p , pa ss i gn hguorht don e B em a sn t eh

sides od f mro a t ir a gn l ,e so don e L ac onn t be t var e sr ed. hT e hto ers are a ll infeasi lb e ceb a su e ht ey i ovn lve cases hw ere a t ir a gn el si fo t ow pyt es g.e( ,. i csos eles a dn e liuq a ret a .)l ehT lborp em here si that ht ere are several hni ere tn depe dn en eic s ni ht e rt ai lgn e lborp em. nO e is that if t erh e i tn ege sr c tutitsno e dis es of a gnairt el , ht ey tsum be no e of the t rh ee ssop ibi itil e :s e liuq ateral, sosi c le es, ro sca el ne. A s ce dno depe dn ency is that ht e t erh e bissop ili it se are mutua yll ex lc u is ve: if no e is t ,eur t eh other t ow must eb fa .esl Reca ll ht ta depe dn e icn es in ht e i tupn da at niamod ac su ed fid ficu tl ies f ro dnuob a yr value tes it ,gn a dn that ew re vlos ed seht e yb iog ng to d isice no table based f cnu it lano te gnits , hw ere we a rdd es es d data depe dn encies in ht e ed c si i no ta elb . Here we are dea gnil wi ht c do e el vel depe dn e cn ei s, a dn these are a osb l etu ly inc pmo ita lb e htiw the la tnet a noitpmuss that ba sis paths are dni epe dn e .tn McCabe s’ corp e rud e cus ce ss f lu ly ide tn i if es bas si pa sht that are t cigolopo a yll dni epe dn e tn , ub t hw en these c rtno a id ct sema itn c depe dn e cn ei s, t opo l igo ac lly lbissop e pa sht ra e seen to be gol ica yll i fn ea lbis e. nO e os l noitu to t sih lborp me si ot a wl a sy re riuq e ht at f il pp i gn a d ice nois er su tl s ni a sema itn cally feasi lb e pa ht . htonA er si to re nosa tuoba l go aci l depe dn e .seicn If we kniht a uob t siht

borp el m ew can di e tn fi y es veral lur e :s

fI edon B si ,desrevart neht ew tsum esrevart sedon D dna E.

fI edon C si ,desrevart neht ew tsum esrevart sedon D dna L.

fI edon E si ,desrevart neht ew tsum esrevart eno fo sedon ,F ,H a dn J.

fI edon F si ,desrevart neht ew tonnac esrevart sedon H dna J .

fI edon H si ,desrevart neht ew tonnac esrevart sedon F dna J .

fI edon J si ,desrevart neht ew tonnac esrevart sedon F dna I.

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.in Taken t go te he ,r the es lur e ,s in c no j tcnu i no iw th McCabe s’ baseli en me ,doht lliw y lei d t eh f gniwollo fea elbis ba sis pa ht se :t

:1pf A-C-D-L- tsaL toN( a )elgnairt

:2pf A-B-D-E-F-G-I-K- tsaL ( osI s ec l se )

:3pf A-B-D-E-G-H-I-K- tsaL )laretaliuqE(

:4pf A-B-D-E-G-I-J-K- tsaL )enelacS(

ugiF re 3.8 urtS c rut ed rgorP imma gn urtsnoC c st

hT e rt ai gn el borp l me si a ipyt ac l in that there a er on l spoo . hT e rgorp am has ylno 81 golopot aci yll lbissop e shtap , a dn of t eseh , ylno ht e f ruo b isa s shtap sil et d a vob e are fe elbisa . suhT f ro th si s ep cial

ca es , we a virr e at the as em t tse ac ses as we did iw th eps c ai l value tes it gn a dn uo t tup ra gn e test .gni roF a rom e tisop ive bo se vr a noit , ba sis ap th c vo erage ug ara tn ees DD -Pa ht c vo erage: ht e orp ssec fo

fli gnipp deci snois ug a nar et es that eve yr d ce noisi uo ct mo e si traver es d, hw ci h si the same sa DD - Pa ht c vo era eg . We see t sih yb ex ma lp e f mor the ni cide cn e tam xir descript noi of ba sis pa sht , a dn ni

ruo rt ai gn le rp o rg ma fe sa ib el basis p ta sh . eW c dluo hsup siht a ts ep f htru er a dn sbo e vr e that the set of DD -P ta sh a stc l ki e a ba sis , beca su e a yn rgorp am pa ht ac n be e rpx e ss ed as a enil ar c nibmo ati no fo

DD -P ta hs . 3.4.3 E ss e laitn lpmoC e ytix Part of McCabe s’ krow no cy lc mo a it c c lpmo ex ti y od se m ro e to i vorpm e rgorp a gnimm ht an t tse i gn . nI siht sect noi we ta ek a iuq ck l koo ta t sih ele ga tn lb e dn of rg a hp ht e yro , st ur c rut ed rgorp a mm i gn ,

a dn the cilpmi a snoit ht se e have f ro et gnits . hT is ohw el package ec tn e sr no ht e ton i no of es es aitn l com lp e ix ty [McCabe ]28 , hw ci h is j tsu ht e cy lc o am it c comp el ytix of y te a on ther f mro fo c dno e sn ita no rg a hp . ceR all ht at c dno e asn it no rg a shp are a way of mis lp i iyf ng an exi gnits rg a hp ; os af r ruo s mi plific ta i sno have been ba es d no r me ivo ng tie her ts gnor c nopmo e stn ro DD -P ta sh . He ,er

we c dno e esn a dnuor eht ts ur c rut ed rgorp a gnimm c sno t ur ct ,s ihw ch a er rep ae ted as rugiF e 3. .8

hT e bas ci edi a si ot kool f ro the rg a hp of no e of ht e st rutcur ed rgorp mma gni c urtsno c st , c llo a sp e it into a gnis el ,edon a dn re ep at litnu on m ro e curts t ru ed rgorp a gnimm c urtsno c st can be f dnuo . sihT

orp ec ss is f lo l wo ed ni Figure 9. ,9 hw ci h ts a str wi ht ht e DD -Path rg a hp of t eh P sa cal t ir a elgn rgorp ma . hT e FI - NEHT - ESLE c urtsno ct ovni l gniv don es A, ,B C, a dn D si c dno e sn ed into don e ,a

a dn ht en the t rh ee FI - NEHT c urtsno stc are c dno e sn ed otno sedon b, c, a dn d. hT e r me gninia IF- NEHT -E ESL ihw( ch c rro se sdnop to ht e FI rTAsI ai gn el ats et ment) si c dno e sn ed into don e e,

resu tl gni ni a c dno e sn ed rg a hp w ti h cyc mol a it c c pmo el ytix )G(V = 1. nI general, hw en a rgorp am si

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.in we ll st ur tc ru ed i( . ,.e is com sop ed os el yl fo ht e ts ur tc ru ed rgorp mma gni c urtsno c )st , ti can a wl ays eb re cud ed to a rg a hp iw th eno p ta .h

hT e rg a hp ni Fi rug e 3.6 ca tonn be re cud ed ni siht way ( rt y i )!t . hT e pool iw th don es B a dn C ca tonn be c dno e sn ed beca su e of e gd e f or m B ot .E imiS al rly, don es D, ,E a dn F kool li ek an FI - HT EN c urtsno c ,t ub t ht e e gd e f mor B to E loiv ates the ts cur t eru . McCa eb new t no to f dni element la “ urtsnu c rut e ”s aht t vio al te ht e erp ce stp of urts c rut ed rgorp a gnimm [ cM baC e ]67 . hT ese are nwohs in Fi rug e 3. .01

ugiF re 3.9 C dno e gnisn w ti h Re ps e tc ot t eh St rutcur ed rgorP a gnimm C urtsno c st E ca h of ht ese “ snu t ur tc ru es” c tno a sni erht e tsid i cn t pa sht , as soppo ed to eht owt pa sht rp e es tn ni t eh c rro e gnidnops urts c rut ed rgorp a imm gn c ,stcurtsno os no e c cno l noisu si ht at us ch vio al ti sno incre esa cyc mol a it c com lp e ytix . The piece ’d er ecnatsis of McCabe s’ ana sisyl si that these urtsnu c rut es ac tonn occ ru yb ht e sm vle e :s if ht ere is no e ni a rp o rg ma , there tsum eb at el a ts no e erom , so a

rgorp am ca tonn eb just ls hgi lt y urtsnu tc ru ed. Sin ec ht ese ni c er ase cy lc mo a it c com lp e ix t ,y t eh im n mi um un mber fo t tse cases si ht ere yb ni cr ae sed. nI ht e next cha tp er, we wi ll es e ht ta t eh

urtsnu c rut es have in et re gnits mi pli ac snoit f ro data wolf te ts .gni

hT e mottob l ni e f ro te srets is this: rgorp a sm w ti h hgih cyc mol a it c c lpmo ex ti y re riuq e m ro e test .gni Of ht e gro a zin ati sno that su e t eh cyc mol at ci oc mp el ytix m te ri ,c most set mos e diug eli en f ro

am mumix a cc e tp ab el c lpmo e ;ytix )G(V = 01 si a c nommo c ecioh . What ha pp e sn if a nu ti has a hi hg er com lp e ix ty? owT ssop ib li iti e :s ie ht er is m ilp fy ht e tinu ro lp an to od m ro e test gni . If ht e tinu si we ll urts tc ru ed, ti s e ss enti la c pmo el ytix si 1, os ti ac n be mis pli eif d ae si yl . If the tinu has na

e ss e tn i la com lp e ytix th ta excee sd ht e ug ide nil es, often ht e be ts c oh eci si ot le i im tan e t eh urtsnu c rut es.

ugiF re 9. 01 loiV a snoit fo urtS tc ru ed rgorP mma gni 3.5 Guid ile n se and snoitavresbO nI uo r ts ydu of f nu itc ano l et ts i gn , we sbo e vr ed that ga sp a dn re dnud ancies ac n ob th exi ts , a dn at t eh

same t emi , ca onn t be er c zingo ed. hT e lborp em was that f nu c noit la test gni removes su t“ oo fa ”r

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.in f or m ht e c edo . hT e pa ht et ts i gn a orpp ca h se ot urts c rut al tes it gn re rp ese tn ht e case hw ere t eh pe udn lum h sa gnuws oot af r ht e hto er wa :y vom ing f mor c do e to d ri ce ted rg a hp re rp esen at snoit a dn

rgorp am pa ht f ro mul ta i sno sbo c ru es ropmi at tn ni f ro am ti no aht t si rp ese tn ni ht e c edo , in pa itr c alu r the cnitsid t noi be ewt en fea elbis a dn fni e lbisa e shtap . nI the ne tx chap et r, we l koo at datafl wo based et ts i gn . hT e es tec nh i uq es m vo e lc o es r to the c edo , os ht e pe muludn lliw ws i gn b ca k f mor ht e pa ht

an la sisy e rtx e .em McCabe was part yl right hw en he bo es vr ed: “ tI si ropmi at tn to dnu e tsr a dn ht at ht ese are rup e yl cr eti ir a that em a rus e ht e uq a ytil of et ts i gn , a dn on t a orp ce erud to di e itn fy et st cases” [ CcM abe .]28 He w sa refe rr i gn to the DD -Path c vo erage em irt c hw( ci h si e uq ivale tn ot the rp edicate tuo c emo

em irt c) a dn ht e cyc mol ta ic c lpmo e ytix em irt c ht ta re riuq es ta el a ts ht e cy lc mo a it c un mber of d itsi tcn rgorp ma p ta hs tsum be rt ave sr ed. Ba sis pa ht te gnits theref ro e giv se su a l wo er dnuob no

woh um ch tes it gn si ne ssec a yr . Pa ht based test gni a osl divorp se su wi ht a es t of m te ir cs ht at act as c ssor ch ce ks no f nu itc ano l et ts i gn . We can su e t eseh m te r ci s ot re vlos e the ga sp a dn re dnud ancies noitseuq . When we fi dn t tah

the same rgorp ma pa ht si rt ave sr ed yb several f nu tc noi al set t cases, we s psu ect ht ta siht re dnud ancy si ton rev ae gnil new fa stlu . When we fail ot ta ta ni DD -P ta h c vo erage, ew wonk ht ta there are ga sp

in the f nu cti lano et ts cas se . As an exa elpm , s soppu e we have a rgorp ma ht at c no t snia e tx e sn ive e rorr ha dn l gni , a dn we te ts it htiw dnuob a yr va ul e te ts ac ses r( ia ,n mi +n , ,mon max-, a dn am )x . ceB a esu these are all perm elbissi va ul es, DD -Pa sht c dnopserro i gn to ht e e rorr ldnah i gn c do e will ton eb trave sr ed. If we a dd te ts c sa es de vir ed f mor ntsubor sse te gnits ro tra noitid la e uq ivale cn e lc a ss et ts i gn , ht e DD -Pa ht c vo erage wi ll mi vorp e. eB dnoy siht rather suoivbo su e of c vo erage tem ir c ,s

there si an roppo t nu ti y rof r ae l te gnits c ar ft ms a hsn ip. hT e c vo erage m te ir cs ni Ta lb e 2 ac n po erate in t ow wa :sy as a lb a kn te ma dn ated ts a dn a dr g.e( ,. a ll nu i st hs all be te ts ed to a tt a ni fu ll DD -Pa ht c vo erage) ro as a m ce msinah ot selective yl et ts rop it sno of c do e m ro e orogir su ly than othe sr . eW

im thg c esooh multiple c dno ti noi c vo erage f ro m udo el s htiw com lp ex gol ci , elihw t soh e htiw e tx e visn e iter ita no im thg be et ts ed in et rms of ht e l poo c vo erage tec nh i uq es. hT is si borp ably ht e be ts v ei w of st ur tc aru l set t gni : su e the rp po erti se of the cruos e c do e to tnedi fi y a rporpp ai te c vo era eg

em irt cs, a dn then su e t seh e as a c or ss c eh ck no f nu itc no al te ts ac ses. W neh the desired c vo erage si ton ta ta ni e ,d fo ll wo i tn ere ts i gn pa sht to di e itn fy tidda i no al ps( ec ai l va ul e) te ts ac se .s

T sih si a oog d lp eca ot revi tis ht e Ve nn d ai rg ma v ei w of te gnits ht ta we u es d ni hC a tp er 1. rugiF e 9. 11 s swoh ht e re al t hsnoi ip be ewt en eps cified beha roiv s s( te )S , rgorp a mm ed beha sroiv s( te )P , a dn t igolopo ac lly ef asi lb e ap ths in a rgorp am s( et )T . sA usu a ,l regi no I is ht e tsom de ris a elb — it c no t snia spe ic fied behavi sro that are pmi el me detn yb fea lbis e pa sht . yB defi noitin , eve yr fea lbis e pa ht si t opo l cigo a yll elbissop , so the hs aded noitrop r( e snoig 2 a dn )6 of the set P mu ts be me pt .y

eR noig 3 c tno a sni ef a lbis e pa sht that c rro e dnops ot psnu ce ified beha .sroiv uS ch extra f tcnu i ano l yti n ee sd to be exa nim ed: if su fe lu , ht e s ep c cifi a noit uohs ld eb cha gn e ,d hto e siwr e these fea lbis e pa sht

dluohs be r me vo ed. Re snoig 4 a dn 7 c no t nia the ni fea lbis e p ta hs; fo the ,es re noig 4 si lborp em .cita eR noig 4 r efe sr to sp ce ified behavi sro ht ta have a tsoml b ee n pmi el me tn e :d golopot ci al yl ssop i elb

y te infea lbis e rgorp ma pa sht . hT is re noig v re y l ki le y c rro se sdnop to c gnido e rr o sr , hw ere cha gn es are needed to make the paths fea elbis . Regi no 5 its ll c rro e dnops s to ps e ic fied behavi sro ht ta have

ton eb en pmi le em tn e .d Pa ht based te ts i gn wi ll never r ce ngo ize t sih regi .no niF al ,yl regi no 7 is a c ru i so ti y: nu s ep cified, ni fea is elb , yet t opo l yllacigo lbissop e shtap . rtS ictly speaki gn , ht ere si on

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borp el m he er , eb ac su e ni fea lbis e p ta sh ca tonn xe ce etu . If eht c rro e dnops i gn c do e si inc rro e yltc cha gn ed yb a m tnia ena cn e acti no m( a by e yb a rgorp emma r ohw od se t’n f lu ly dnu e sr ta dn ht e c do e ,) the es c dluo eb c emo f ae elbis ,shtap as ni re noig .3

ugiF re 3. 11 F ae elbis a dn opoT l go ic lla y elbissoP Paths EXERC SI ES

1. dniF eht citamolcyc ytixelpmoc fo eht hparg ni erugiF 2.9 .

2. yfitnedI a tes fo sisab shtap rof eht hparg ni F erugi 2.9 .

3. ssucsiD s’ebaCcM tpecnoc fo ”gnippilf“ rof sedon htiw eergedtuo ≥ 3.

4. esoppuS ew ekat erugiF 2.9 sa eht DD - htaP hparg fo emos margorp . poleveD stes fo shtap hcihw( uow ld eb t se t sesac ) of r t eh C0, C1, dna C2 em tri .sc

5. poleveD elpitlum noitidnoc egarevoc tset sesac rof eht lacsaP elgnairt margorp . yaP noitnetta ot

eht ycnedneped neewteb tnemetats stnemgarf 41 dna 61 htiw eht noisserpxe a( = )b DNA c( = )d . etirweR eht margorp tnemges 41 - 12 hcus taht eht dnuopmoc snoitidnoc era decalper yb detsen FI -

NEHT - ESLE stnemetats . erapmoC eht citamolcyc ytixelpmoc fo ruoy margorp htiw taht fo eht gnitsixe noisrev .

6. roF a tes V ot eb a rotcev ,ecaps owt snoitarepo noitidda( dna ralacs )noitacilpitlum tsum eb

denifed rof stnemele ni eht tes . nI ,noitidda eht gniwollof airetirc tsum dloh rof lla srotcev ,x ,y dna z �

iv. ereht si 0 rotcev a

v. na rof y x � ,V ereht si a rotcev -x hcus taht x + (- )x 0.

,V eht rotcev xk � V .

vii. x(k + yk + xk )y .

viii. k( xl + xk x)l + .

ix. x)lk( = )xl(k .

x. lx = .x

woH ma yn fo the es ten rc eti air oh ld f ro t eh “ve rotc caps e” fo pa sht in a rgorp am?

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,V dna rof lla sralacs ,k ,1 ,0 dna :1 i. fi ,x y � ,V eht rotcev y + x � V. ii . x + y = y + .x

iii. (x + )y + z = x + (y + z).

V hcus taht x + 0 x = . �

� vi. of r yna X

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6.3 D ta a olF w seT t ni g Da at wolf te gnits si an nu f ro t nu ate te mr , ceb a su e mo ts os f wt are deve pol e sr mmi e id ta yle kniht a uob t

mos e c enno c noit wi ht atad f wol dia rg a sm . taD a f wol te ts gni refe sr to f mro s of ts cur t ru la tes it gn t tah focus no the op ints at ihw ch va ir a lb es cer e vi e va ul es a dn the op i tn s at ihw ch these va ul es are u es d

ro( r efe ren ec )d . We will see that data wolf t se ti gn res ves as a “ er al ti y c eh ck” no pa ht te ;gnits i dn ee d, ma yn of the da at fl wo et ts i gn noporp ents (a dn r se earchers) see ht is a aorpp ch as a f mro fo pa ht te ts i gn . eW w li l l koo ta owt m nilnia e f smro of data fl wo et ts i :gn no e divorp es a set of cisab defin ti snoi a dn a inu fyi gn st ur c rut e of et st c vo e gar e tem ir cs, hw ile the dnoces si ba es d no a c cno ept cal el d a “ rgorp am ls ci e .” B hto of seht e f mro ali ez intui vit e beha sroiv (a dn an la sy e )s of te ts e sr , a dn a hguohtl ht ey ob th ts a tr htiw a rgorp am rg a ,hp htob m evo b ca k ni t eh rid ect noi fo f cnu t lanoi t tse i .gn

tsoM rgorp a sm de vil er f nu itc no la yti in te smr of ad ta. Va ir a lb es that re rp e nes t d ata s mo e woh receive v la ues, a dn these v la ues are u es d to c upmo et va eul s f ro hto er var ai lb es. cniS e ht e early ,s0691

rgorp a emm sr have ana zyl ed ruos ce c do e ni te smr of the stniop ts( a et em )stn at ihw ch va ir a lb es rec ie ve v la ues a dn op ints at ihw ch ht e es va ul es are su ed. Ma yn mit es, the ri a an l sy es were based no c no c dro a cn es t tah il st ts atement bmun e sr in ihw ch va ir a lb e names co c ru . C no c dro an ec s we er

lupop ar feat ru es of sec dno gener ita no la ugn age c lipmo e sr (they are ts li l lupop ar w ti h COB LO rgorp a emm sr ). Ea ylr “ tad a fl wo ” an la sy es tfo en cen et red no a es t of fa stlu th ta are won nwonk sa

define r/ eference a mon la ie :s

• a elbairav taht si denifed tub reven desu ecnerefer( )d

• a elbairav taht si desu tub reven denifed

• a elbairav taht si denifed eciwt erofeb ti si desu E ca h fo t eseh a mon la ies ac n eb er c ngo ized orf m t eh c no c dro ance of a rgorp ma . cniS e t eh c no c dro a cn e inf ro am noit si compiler generated, ht e es a mon alies ac n be sid c vo ered yb hw at si

nwonk as “ ts a cit ana yl is s”: f gnidni fa lu ts ni ecruos c edo w ti tuoh execut gni .ti 3.6.1 Def /eni U es T gnitse

uM ch of ht e f mro a zil a noit of ed f eni / su e te gnits was nod e in ht e ea ylr s0891 [Ra pp s 58 ]; t eh defin ti snoi ni siht es c it no a er c apmo t lbi e wi ht t soh e in C[ la kr e ,]98 an ra ticle ihw ch mmus a sezir m so t of ed f ni /e su e et ts i gn theory. hT is ydob of r aese rch si ve yr c pmo ta lbi e wi ht ht e f alumro t noi we deve pol ed ni cha tp e sr 4 dna 9. It rp e mus es a rgorp am rg a hp ni ihw ch don es are statement fra mg e stn (a frag em tn may be an en it re s at teme )tn , a dn rgorp a sm that fol wol ht e urts c rut ed rgorp mma gni

rp ece tp s .

hT e f gniwollo d fe noitini s refer to a rgorp am P ht ta sah a rgorp am rg a hp )P(G , a dn a set of rgorp ma va ir ab sel V. hT e rgorp ma rg a hp G( )P is c rtsno ucted as in Cha tp er 4, wi ht ts ateme tn fra mg en st as

don es, a dn e gd es ht ta rper e es tn don e se uq enc .se G( )P has a is ng el ent yr don e, a dn a gnis el exit edon . We sla o sid lla wo gde es f or m a don e ot i st e fl . Pa ,sht apbus t sh , a dn yc cl se are as they were in

Cha tp er .4

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V, rw ti ten as ED F v( , )n , iff ht e value of t eh va ir ab el v is ed if ned ta eht ts at eme tn fragm ne t c rro e gnidnops ot don e n. tupnI ts at me e stn , a ngiss em tn st ta eme ,stn pool c no t lor ts ta me e stn , dna orp ec rud e c slla a er lla exa lpm es fo ts a met e stn that are defini gn don es. When the c do e c rro e dnops i gn ot us ch ts ateme stn ex ce tu es, ht e c tno e stn of t eh m me yro l co ita )s(no ossa ciated iw th eht va ir a lb es a er cha gn e .d Def tini i no

V, rw itten sa v(ESU , )n , fi f the va ul e of t eh va ir ab el v si su ed at ht e ts atement frag em tn c rro e gnidnops ot don e n. uptuO t ts ta eme stn , issa mng ent s at teme stn , c tidno i no al ts a et em stn , pool c lortno s at et em stn , a dn orp ce rud e ac sll are la l ex ma lp es fo s at teme stn th ta are su age don es. When ht e c edo c rro e dnops i gn ot us ch ats teme stn ex ce tu es, t eh c no et stn fo t eh me yrom ol ac ti )s(no a coss i ta ed wi ht eht var ai lb es rem ia n nu cha gn e .d Def tini i no A su age don e v(ESU , )n si a rp e id c ta e su e toned( ed as P- su e) iff t eh ts ateme tn n is a rp e id c ta e s at teme ;tn hto e siwr e v(ESU , )n is a com tatup i no su e , d( e ton ed C- su e). hT e don es c rro e dnops i gn to

rp e cid ate su se la ways vah e an dtuo e rg ee ≥ ,2 a dn don es c rro e gnidnops ot c tupmo ita no su es la wa sy have uo tde rg ee ≤ .1 Def tini i no A de nif iti no -u es )bus( p hta iw th r se ep tc to a var ai lb e v d( e ton ed ud -p ta )h si a p)bus( a ht ni

,V ht ere are define a dn su age don es FED (v, )m a dn ESU (v, n) us ch ht at m a dn n a er eht tini i la a dn final don es fo eht p)bus( a .ht

Def tini i no A de inif it no -cle ra htap)bus( htiw re ps ect ot a va ir ab el v d( e ton ed dc-pa )ht is a ed f noitini - esu

p)bus( ath ni HTAP S(P) wi ht ini tial a dn final don es FED (v, )m a dn ESU v( , )n such ht ta on hto er don e ni the p)bus( ta h si a de gninif don e of v. tseT e sr uohs ld on ti ec woh seht e ed f snoitini ac tp ru e t eh

e ss e cn e of com tup i gn wi ht ts ro ed data v la ues. uD -pa sht a dn dc- shtap d cse ribe the fl wo of atad ac ssor cruos e stateme stn f mor stniop at ihw ch ht e va ul es are de nif ed to stniop at ihw ch the v la ues a er su e .d uD -p ta sh ht at ra e ton ed f noitini -clear a er top e itn al elbuort .stops 3.6.2 pmaxE le

eW will su e the noissimmoC lborP me a dn ti s rgorp ma rg a hp to li l su trate these de snoitinif . ehT bmun ered ruos ec c edo si given ne tx , f wollo ed yb a rp o rg am rg a hp c urtsno cted a cc gnidro ot t eh

orp ec rud es we sid cu ess d ni Cha tp er 4. sihT gorp mar c tupmo es ht e c ssimmo i no no ht e s la se of f ruo salespers sno , he cn e t eh tuo er roF -l poo ht at re ep sta f ruo t emi s. gniruD cae h re ep tit noi , a salespers s’no man e si daer f mor t eh tupni de iv ce, a dn eht i upn t f or m taht pe nosr si er ad ot com tup e

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Def tini i no

� � doN e n G( )P si a defi gnin don e of the var ai lb e v

� � doN e n )P(G si a gasu e don e of the var ai lb e v

� TAP HS( )P us ch ht at, f ro os me v

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.in the tot al bmun e sr of l kco s, ots c ,sk a dn ba rr e sl os ld yb ht e pers .no hT e While- pool is a cla iss c la se nitn le c ortno lled l poo ni hw ci h a v la ue of -1 f ro loc sk si fing ei s the e dn of ht ta pers s’no da at . ehT to at sl are ca cum lu ated sa ht e data il nes are r ae d ni the W lih e- pool . tfA er rp int gni siht rp le nimi a yr inf ro m noita , ht e sales va ul e is c tupmo ed, gnisu ht e c tsno a tn ti em cirp es ed f ni ed at the begi nn i gn fo the rgorp ma . hT e sales v la eu is ht en su ed ot c upmo et ht e co mm noissi in ht e c dno i noit al rop t noi fo t eh rgorp a .m

1 rgorp am lock- ots c _k a rrab_dn el 2 snoc t 3 ecirp_kcol = 0.54 ; 4 ots c ecirp_k = 30.0; 5 ecirp_lerrab 25.0; 6 pyt e 7 IRTS NG_ 03 = ts r gni [30]; mselaS{ a s'n Name} 8 v ra 9 ol ck ,s stoc ,sk ,slerrab _mun loc ,sk un s_m toc sk ,

10 ,slerrab_mun mselas a dni_n e ,x edro r_i dn ex : I TN EGER; 11 ,selas immoc ssion : ER AL; 12 namselas : ST IR GN _3 ;0 13 14 NIGEB p{ r rgo am loc s_k toc _k and ab_ rr le } 15 FOR las se am n_i dn ex =: 1 TO 4 DO 16 IGEB N 17 LDAER elas(N ms na ); 18 TIRW E NL laS'( e ms an si ,' las se )nam ; 19 _mun loc sk =: 0; 20 s_mun toc sk =: 0; 21 slerrab_mun =: 0 ; 22 ol(DAER c sk ); 23 ELIHW ol c sk >< -1 D O 24 NIGEB 25 AER D NL s( toc ,sk errab l ;)s 26 _mun loc sk =: _mun loc sk + loc sk ; 27 ots_mun c sk := _mun ots c sk + stoc sk ; 28 slerrab_mun := ab_mun err sl + ab r sler ; 29 ;)skcol(DAER 30 ;DNE ( HW I EL loc sk ) 31 AER DLN; 32 TIRW E NL ( S' a sel f ro mselas,' an); 33 TIRW E NL ( oL' c sk s :dlo ,' num_loc sk ); 34 TIRW E NL ( otS' c sk os l :d ,' s_mun toc )sk ; 35 TIRW E NL ( slerraB' os l :d ,' num_ rab r )sle ; 36 selas =: loc cirp_k e _mun* loc sk + stock_price n* um_stocks

+ p_lerrab r ci e*n _mu ab rr sle ; 37 TIRW E NL ( latoT' ,' :selas selas : )2:8 ; 38 TIRW ELN; 39 FI > selas( 81 00.0) HT E N 40 IGEB N 41 mmoc i ss ion =: 0. 01 * 01 0 0.0 ; 42 mmoc i ss i no =: c immo ssi no + 51.0 * 008 .0; 43 immoc ssi no =: c mmo i ss i no + 0. 02 * selas( - 81 00 . )0 ; 44 ;DNE 45 E ESL FI selas( > 0001 .0) T EH N 46 IGEB N 47 ssimmoc i no =: 0. 01 * 0001 .0; 48 immoc ssi no =: c mmo i ss ion + 0. las(*51 es - 01 00 . )0 ; 49 DNE

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50 ESLE c ssimmo i no := .0 10 * sa sel ; 51 TIRW E NL ( mmoC' i ss i no si ,'$ c ssimmo i no :6:2); 52 ;DNE F( OR las e ms a )n 53 DNE . p{ r rgo am loc s_k toc dna_k - ab r er l}

hT e DD -P ta hs ni siht rgorp ma are vig en ni Ta lb e 1, a dn the DD -Pa ht rg a hp is sh nwo in rugiF e .2.01

Tab el s 2 a dn 3 sil t ht e ed f ni e a dn su age don se f ro if ve va ir ab el s in ht e c mmo issi no lborp em. We esu this inf mro a noit in c no j nu tc noi htiw ht e rgorp ma rg a hp in Fi rug e 01 .1 ot di e itn fy vari uo s d ife niti no -

su e a dn ed f ni i noit -c el ar pa sht . It s’ a j mgdu ent call hw te her ro on t non -e tucex a lb e ats et men st us ch as c tsno a tn (C TSNO ) a dn va ir ab el AV( R) d ce larati no ts a et em stn uohs ld be c no is dered as ed f gnini

don es. eT c nh ically, ht ese ylno d ife ne m me yro ps ace ht( e C TSNO declar ita no crea et s a c lipmo er- udorp ec d itini al v la ue). uS ch don es are ’n t ve yr in et re gnits hw en we fol wol hw ta ha pp ens al gno

the ri ud -p ta sh , ub t if ht ere is mos te hi gn orw ng, ti s’ usu la yl he pl ful to inc dul e meht . Take ruoy p ci .k eW w li l refer ot the vari suo pa sht as se uq e cn es of don e un mbe sr . tsriF , le s’t l koo ta ht e ud -p ta hs f ro

the var ai lb e ots c .sk We have ots(FED c ,sk )52 a dn ots(ESU cks, )72 , os the htap 52< , >72 si a ud - pa ht trw htiw( re ps ect t )o ots c .sk Si cn e the er are on hto er ed f gnini don es f ro ots c ,sk this pa ht is a osl defin ti noi -clear .

owT d fe gnini a dn t ow su age don es make ht e skcol va ir ab el m ro e tni ere ts i :gn we have DE (F loc sk , ,)22 (FED loc ,sk ,)92 ol(ESU c ,sk ,)32 a dn ol(ESU c ,sk .)62 hT e es yield f ruo ud - ap t :sh

1p = ,22< >32

2p = ,22< ,32 ,42 ,52 >62

3p = ,92< ,03 32 >

4p = ,92< ,03 ,32 ,42 ,52 >62

ugiF re 3.1 rgorP am hparG fo t eh ssimmoC i no rgorP ma

elbaT 1 DD - shtaP ni erugiF 1.01 DD - htaP

oN sed

1 41

2 51 - 22

3 32

4 42 - 03

egaP 81

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5 13 - 93

6 04 - 44

7 54

8 64 - 94

9 05

10 25,15

11 35

uD -p ta sh 1p a dn 2p refer to the rp imi gn value of ol c sk ihw ch si er ad at don e 22 : loc sk has a rp e cid ate esu ni the Wh eli s at teme tn don( e ,)32 a dn if ht e c tidno i no is urt e (as ni pa ht )2p , a

com tup ati no su e at sta met e tn 62 . hT e other owt ud -paths ts art n ae r ht e dne of ht e While l poo a dn o cc ru hw en eht pool re ep a .st fI ew “e tx e dn e ”d pa sht 1p a dn 3p to ni c edul edon ,13

’1p = ,22< ,32 >13

’3p = ,92< ,03 ,32 >13 then the shtap ’1p , 2p , ’3p , a dn 4p f mro a v re y comp el te es t of te ts cases rof ht e W lih e- :pool pyb ass t eh l ,poo be nig t eh l ,poo re ep at eht ,pool a dn exit eht l .poo llA fo the es ud -pa sht a er ed if in it no -c el a .r

ugiF re 3.2 DD -Pa ht hparG fo eht noissimmoC rgorP ma

elbaT 2 esU/enifeD noitamrofnI rof ,skcol ,skcots dna skcol_mun elbairaV

denifeD ta desU ta mmoC e tn

l kco s 9 ot( )relipmoc

l kco s 22 AER D

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l kco s 23 etaciderp su e

l kco s 26 noitatupmoc esu

l kco s 29 AER D

ots cks 9 ot( )relipmoc

ots cks 25 AER D

ots cks 27 noitatupmoc esu

kcol_mun s 9 ot( )relipmoc

kcol_mun s 19 tnemngissa

kcol_mun s 26 tnemngissa

kcol_mun s 26 noitatupmoc esu

kcol_mun s 33 W IR T E

kcol_mun s 36 noitatupmoc esu

elbaT 3 esU/enifeD noitamrofnI rof selaS dna noissimmoC elbairaV

denifeD ta desU ta mmoC e tn

sa el s 11 ot( )relipmoc

sa el s 36 tnemngissa

sa el s 37 W IR T E

sa el s 39 su etaciderp e

sa el s 43 noitatupmoc esu

sa el s 45 su etaciderp e

sa el s 48 c noitatupmo esu

sa el s 50 noitatupmoc esu

oissimmoc n 11 ot( )relipmoc

oissimmoc n 41 gissa tnemn

egaP 83

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oissimmoc n 42 tnemngissa

oissimmoc n 42 noitatupmoc esu







oissimmoc n 51 W IR T E

hT e ud -p ta sh f ro un m_loc sk wi ll lead su to pyt aci l et ts ac ses f ro c atupmo t snoi . W hti t ow ed f gnini don es ol_mun(FED( cks, )91 a dn un(FED m_locks, ))62 a dn t erh e su age don se mun(ESU( ol_ c ,sk )62 , ol_mun(ESU cks, )33 , _mun(ESU loc sk , ,))63 we hgim t e px ce t six ud -pat sh . Let s’ at ke a c sol er

l koo . Pa ht 5p = 91< , 02 , ,12 22 , 32 , 42 , 52 , >62 si a ud -p ta h ni ihw ch the ini tial value )0( has a com tup ati no .esu sihT htap si ed f noitini -cl ae .r ehT ne tx ap th si borp l me ati :c

6p = ,91< ,02 ,12 ,22 ,32 ,42 ,52 ,62 ,72 ,82 ,92 ,03 ,13 ,23 >33

eW have i rong ed t eh lbissop e re tep ti noi of the W lih e- pool . We c dluo hgih li thg this yb gniton ht at the pbus a ht 62< , 72 , 82 , 92 , 03 , 22 , 32 , 42 , >52 thgim be rt ave sr ed sever la t mi es. irongI ng th si for

won , we ts lli have a ud -pa ht that fai sl to be ed f noitini -cl ae r. If there is a borp el m w ti h the v la ue fo ol_mun cks at don e 33 ht( e WR ETI ts ta eme )tn , we uohs ld kool ta the tni e vr eni gn DE ol_mun(F c ,sk

)62 .edon

hT e ne tx pa ht c sniatno ;6p we ac n wohs th si yb u is gn a pa ht n ema in p al ce of ti s c rro se gnidnop edon se uq en ec :

7p = ,91< ,02 ,12 ,22 ,32 ,42 ,52 ,62 ,72 ,82 ,92 ,03 ,13 ,23 ,33 ,43 ,53 >63

7p = < ,6p ,43 63 ,53 >

uD -p ta h 7p si ton ed f noitini -c el ar eb ca esu ti inc dul es edon .62

pbuS ta sh that be nig w ti h don e 62 (an a ngiss em tn ts ate em nt) are i tn ere gnits . hT e f ri st, 62< , ,>62

se sme degenera et . If ew e“ px a dn ed” ti otni m ca nih e c ,edo we dluow eb a lb e to sepa etar t eh ed fi en a dn su age trop i sno . We lliw sid a ll wo these sa ud -pa sht . eT c inh c ylla , eht su age no ht e ri thg -ha dn

egaP 84

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.in side of ht e a ss i mng e tn refe sr to a va ul e ed f ni ed at don e 91 , s( ee htap )5p . hT e rema gnini t ow ud - pa sht a er htob pbus a sht fo :7p

8p = ,62< ,72 ,82 ,92 ,03 ,13 ,23 >33

9p = ,62< ,72 ,82 ,92 ,03 ,13 ,23 ,33 ,43 ,53 >63 Both fo ht e es a er ed f noitini - lc ea ,r a dn ob th have eht l poo iterati no lborp em ew sid c ssu ed bef .ero Sin ec ht ere is ylno no e defini gn don e f ro s ela s, all the ud -pa sht rw t sales tsum be ed f tini i no -cle .ra

hT ey are tni ere its gn eb c sua e ht ey li rtsul a et rp e cid a et a dn c atupmo it no su es. hT e f tsri erht e ud - shtap a er ae :ys

01p = ,63< >73

11p = ,63< ,73 ,83 93 >

21p = ,63< ,73 ,83 ,93 ,04 ,14 ,24 >34

itoN ce ht ta 21p is a de nif i noit -cl ae r p ta h htiw erht e su age don es; it a osl c no iat sn pat sh 01p a dn 11p . If ew we er t tse gni iw th 21p , we wonk we uow ld also ha ev c vo ered t eh other owt pa .sht eW wi ll re siv ti t sih wot a dr t eh e dn fo eht cha tp e .r

hT e FI , SLE E IF lo ig c in tats e em tn s 93 hguorht 50 hi lhg i sthg an a ytiugibm in ht e giro in la re aes rc .h hT ere are t ow c cioh es f ro ud -pa sht that begin wi ht pa ht :11p ht e sta it c c ioh ce is ht e p ta h ,63< 73 , 83 , 93 , 04 , 14 , 24 , >34 , ht e nyd cima c ioh ec si eht p ta h 63< , 73 , 83 , 93 , >54 . Here we wi ll su e t eh nyd amic iv e ,w so t eh mer a gnini ud -pa sht f ro sa sel a er

31p = ,63< ,73 ,83 ,93 ,54 ,64 ,74 >84

41p = ,63< ,73 ,83 ,93 ,54 >05

toN e that ht e nyd amic v ei w is ve yr compa lbit e htiw ht e dnik fo thi kn i gn we su ed f ro DD -P ta sh . fI uoy have foll wo ed this sid cu iss no ac ref lu ly, uoy are borp ably rd ae di gn the ts fu f no ud -pa sht rw t

com oissim n. r’uoY e thgir -- i ’t s mit e f ro a cha gn e of ap ce. nI ats et ments 93 hguorht 15 , t eh calc lu ita no fo c noissimmo is c no t llor ed yb ra gn es of ht e va ir a elb sale .s Stat eme stn 14 ot 34 ub i dl pu the va ul e of c mo m noissi yb isu ng the mem yro locat noi to dloh tni e mr e aid et v la ues. hT is is a com nom rgorp mma gni rp ca ecit , a dn ti si de ris ab el eb ca su e ti swohs woh ht e nif al va ul e si com tup ed. W( e c dluo per al ec these nil es wi ht the stateme tn “c mo m si is no := 220 + 0 02. * las( es -1 08 0)”,

hw ere 022 si the val eu of 0.10* 01 00 + 0.15 8* 00, tub th si dluow be drah f ro a ma atni iner to dnu e tsr a dn .) hT e “ tliub - pu ” ve noisr su es tni e demr ai te va ul es, a dn these wi ll a epp ar as defi en a dn

su age n do es in the ud -pa ht ana yl is s. cniS e ew decided to sid la wol ud -p ta sh f mor a ss i mng e tn s at teme stn il ke 14 a dn 24 , we’ ll j tsu c no is der ud -pa sht that be nig htiw the rht ee “ er al” ed if gnin

don e :s (FED co imm ,noiss )34 , (FED c noissimmo , )84 , a dn (FED c mmo issi no , )05 . hT ere si no ly eno su age ,edon (ESU co mm ,noissi .)15

egaP 85

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.in We have a hton er ma bi ytiug . ehT sta cit iv ew r se ults in eno ud -pa :ht

,34< ,44 ,54 ,84 ,05 >15

sihT pa ht c no t snia t eh t rh ee ed f ini it sno of co mm ,noissi os ti si ton ed if n ti noi -clea .r ehT nyd a cim iv ew re stlus in eerht :shtap

51p = ,34< >15

61p = ,84< >15

71p = ,05< >15

gA a ,ni eht nyd a cim iv ew is rp efera lb e; ti al os re stlus in ed f tini i no -c el ar pa .sht A( hs a pr te ts er m hgi t

a ks woh ew uow ld ever xe ec etu eht “p ta ”h ,34< ,44 ,54 ,84 ,05 .>15 eW tonnac .) ehT f llu set fo ud - pa sht in eht borp l me is vig en ni Ta elb 4 ht( ey a er re bmun ere .)d 3.6.3 uD - ap th Te ts voC re ega Me irt cs

ehT elohw tniop fo a gnizylan a rgorp am as in eht rp evi suo es c oit n si ot enifed a set fo te ts c vo erage em irt cs nwonk as eht sppaR - eW kuy er da at wolf met ir cs [ aR spp .]58 ehT if tsr t eerh fo th se e a er

e uq ivale tn ot t eerh fo .E F. lliM e ’r s m te ir cs in Cha tp er :9 lA l-Pa ,sht A ll - gdE e ,s a dn A ll - doN e .s ehT othe sr rp e emus that ed if en a dn su a eg don es have b ee n di entified f ro a ll rgorp am var ai ,selb a dn that

ud -pa sht ha ev eb en ide tn ified htiw r se ep ct ot each var ai .elb nI eht f lo l gniwo de tinif i ,sno T is a set fo p)bus( aths ni t eh rgorp am rg a hp )P(G fo a rgorp am ,P iw th eht tes V of irav ab el .s

Def tini i no

ehT set T s ta si fies t eh A ll -Defs c tir eri no f ro eht rgorp am P iff f ro eve yr var ai elb v defin ti noi clear p)bus( at sh f mor eve yr ed fi gnin edon fo v to a esu fo .v Def tini i no

ehT set T s ta si fies t eh A ll - sU es criteri no f ro t eh rgorp am P iff f ro eve yr va ir a elb v ,V T c tno a sni defin ti noi -clear p)bus( ta sh f mor eve yr ed f gnini edon fo v to eve yr esu fo ,v a dn ot t eh s ccu e ss ro

edon fo e ca h v(ESU , .)n Def tini i no

ehT set T s ta si fies t eh A ll -P-U es s oS/ me C- sU es tirc eri no f ro eht rp rgo ma P ffi f ro eve yr va ir a elb v � v, a dn fi a defini noit fo v has on P-u es ,s ht e er si a de tinif i no - elc ar path to at le tsa eno com tup a noit

esu .

elbaT 4 uD - shtaP ni erugiF 1.01 uD -Pa ht

lbairaV e feD edoN doN esU e

1 kcol s 22 32

egaP 86

� ,V T c tno a sni

�

,V T c no t snia ed fi in t noi - lc ear p)bus( at sh f mor ve ery ed fi gnin edon fo v ot eve yr rp e cid a et esu fo

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2 kcol s 22 62

3 kcol s 92 32

4 kcol s 92 62

5 s ot cks 52 72

6 rrab e sl 52 82

7 ol_mun kc s 91 62

8 kcol_mun s 91 33

9 kcol_mun s 91 63

01 kcol_mun s 62 33

11 kcol_mun s 62 63

21 skcots_mun 02 72

31 skcots_mun 02 43

41 ots_mun skc 02 63

51 skcots_mun 72 43

61 skcots_mun 72 63

71 lerrab_mun s 12 82

81 skcots_mun 12 53

91 skcots_mun 12 63

02 skcots_mun 82 53

12 skcots_mun 82 63

22 sa el s 63 73

32 sa el s 63 93

42 sa el s 63 34

52 sa el s 63 54

egaP 87

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62 sa el s 63 84

72 sa el s 63 05

82 oissimmoc n 14 24






Def tini i no

hT e tes T s ta si fies the A ll -C- sU es oS/ em P- sU es c tir e noir f ro the rgorp ma P iff f ro eve yr va ir ab el v � of ,v a dn fi a def ni i noit of v has on C- su es, ht e er si a de inif it no -c el ar pa ht to at el a ts no e rp e id c eta

esu . Def tini i no

hT e s te T tas isfies the A ll - UD - ap ths crite noir rof ht e rgorp am P fi f f ro eve yr var ai lb e v � c no t snia defi in it no -clear p)bus( a sht f mor eve yr defini gn don e fo v to ve re y su e of ,v a dn ot the us cce ross don e of ae ch ESU (v, )n , a dn that ht ese pa sht are tie her si lgn e pool trave sr a sl , ro ht ey a er

cyc el f .eer

hT e es test c vo erage me irt cs have several set-t yroeh based rela ,spihsnoit hw ci h are fer e rr ed to as “ usbus mp it no ” in [Ra spp 58 ]. When no e test c revo age em irt c musbus es a hton er, a es t of te ts ac ses that a tt ai sn c vo erage in et rms of ht e f ri st m cirte en ec ss a ylir ta t ia sn c revo age htiw re ps e tc to t eh

musbus ed m te .cir hT e es relati no shi sp a er nwohs ni rugiF e 3. .3

eW won have a m ro e r fe ni ed v ei w of st ur tc ru al te gnits ibissop il it se b te w ee n the ext er em s of eht pyt( aci yll anu t at ina lb e) A ll -Pa sht met ,cir a dn t eh genera yll ac ec tp ed inim mum, lA l- gdE e .s

ugiF re 3.3 Ra /spp eW kuy er iH erarc yh fo Da at wolF voC era eg Met ir cs

egaP 88

V, T c tno ia sn defin ti noi -clear (s p)bu a sht f mor ve re y d fe gnini don e of v to eve yr com tup a noit esu

,V T

Vtusolution.in

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lution

.in Sin ec sever la ud -pa sht ra e rp esent in a f lu l gorp mar e ex cut noi pa ht (tra srev ed yb a et st case), t eh hi hg er f ro ms of c vo erage em irt cs t’nod la ways mi ply si ing f ci ant yl hgih re un mbers of et st ac ses. nI

ruo c gniunitno exa ,elpm ht e t rh ee ed isic no at ble f cnu t lanoi te ts ac ses c vo er all the DD -P ta sh es( e Tab el )5 a dn som t of the ud -p ta hs s( ee Ta lb e )6 . hT e im ss i gn ud -pa sht 8( , 9, 41 , 81 , )91 are la l trave sr ed yb a te ts c esa ni ihw ch on thi gn si s dlo ,.e.i( t eh f tsri val eu fo ol c sk si - .)1 3.7 ecilS -B desa T itse ng

rP o rg ma sli ec s have s ru f ca ed a dn s mbu e gr ed ni os ftware e gn in ee gnir til era rut e is cn e the ae ylr 0891 s. hT ey were nigiro lla y oporp es d ni [W ie ser ]58 , su ed as an a aorpp ch ot fos tware main et nance

in [Ga ll a hg er ]19 , a dn tsom rece yltn su ed to tnauq fi y f cnu it lano c ho esi no in [ iB eman ]49 . Part fo this ve sr a it til y si ud e ot ht e nat laru , vitiutni ely c el ar in et tn of the rgorp ma cils e c no ce tp . nI f ro am yll , a rgorp ma ils ec si a s te of rgorp am ts ateme stn ht ta c rtno i tub e ot , ro fa ef ct a va ul e f ro a var ai lb e at

mos e op int in the rgorp am. hT si ton i no of ls ice rroc e sdnops to hto er d si c lpi ines as we ll . We thgim st ydu yrotsih ni et r sm of cils es: SU yrotsih , poruE ae n h tsi yro , Ru ss ian hi yrots , Far Ea ts ,yrotsih Ro am n h tsi yro , a dn os no . hT e way us ch hi cirots la cils es tni er ca t t snru tuo ot be ve yr an la suogo to t eh way rgorp ma ils ec s ni et ar c .t

elbaT 5 DD - htaP egarevoC fo noisiceD elbaT lanoitcnuF tseT sesaC saC e

kcol s cots sk lerrab s as les oissimmoc n DD - shtaP

1 5 5 5 005 50 1- ,5 ,7 ,9 11 ,01

2 15 15 51 051 0 571 1- ,5 ,7 ,8 11 ,01

3 25 25 52 052 0 063 1- ,5 ,9 ,01 11

elbaT 6 uD - htaP egarevoC fo noisiceD elbaT lanoitcnuF tseT sesaC uD - htaP

saC e 1 aC se 2 aC se 3

1 X X X

2 X X X

3 X X X

4 X X X

5 X X X

6 X X X

7 X X X

egaP 89

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.in

8

9

10 X X X

11 X X X

12 X X X

13 X X X

41

15 X X X

16 X X X

17 X X X

81

91

20 X X X

21 X X X

22 X X X

23 X X X

24 X

25 X X

26 X

27 X

28 X

29 X

30 X

31 X

egaP 90

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.in

32 X

33 X

eW ll’ ts a tr yb iworg ng ruo krow i gn d ife n ti noi fo a rgorp ma ils c .e We c unitno e wi ht the ton ati no ew su ed f ro ed if ne- su e shtap : a rgorp ma P ht ta has a rgorp am rg a hp G( )P , a dn a s te of rgorp ma

va ir ab sel V. hT e f ri ts yrt r fe ni es ht e ed if in it no in [Ga ll a hg er 19 ] to a woll don es in )G(P ot r fe er ot s at teme tn frag em .stn Def tini i no

viG en a rp o rg am P, a dn a set V of va ir a lb se in P, a ls ice no ht e v lbaira e es t V at ts ateme tn ,n rw ti ten V(S , ,)n si t eh set fo all ts a et em stn in P that c no t etubir ot t eh values fo va ir a lb es in .V

tsiL i gn ele me tn s of a cils e V(S , )n wi ll eb c bmu e osr me, beca su e ht e eleme stn are rgorp am st ta eme tn

frag em nts. niS ec it si much mis lp er to l tsi fragm tne un mbers in (P G), we make ht e f wollo i gn rt i iv al cha egn (it k ee sp eht set ht e yro sirup ts ha :)ypp Def tini i no

viG en a rgorp am P, a dn a rgorp am rg a hp )P(G in hw ci h ts atements a dn ats teme tn fra mg ents are un bm ered, a dn a set V of va ir ab el s ni P, the cils e no ht e va ir ba el s te V at ats teme tn garf ment ,n rw ti ten V(S , )n , is the set don e un mbe sr of a ll ts ateme tn frag em stn in P rp i ro ot n that c tubirtno e ot

t eh v la ues fo va ir a lb es in V ta ts at me e tn fra mg e tn .n

hT e di ea fo ils ec s is ot separa et a rgorp ma otni oc m nop ents ht at have mos e esu ful em ani gn . Fi ,tsr we en ed to e lpx a ni t ow par st fo t eh ed if in it no . Here ew mean “ roirp ”ot ni the nyd a im c se esn , os a s il ce capt ru es ht e e ex c noitu t mi e behavi ro of a rgorp am w ti h r se p tce ot ht e va ir ab el )s( ni ht e ls .eci

vE e utn a yll , we wi ll deve pol a l tta ice a( erid cted, ca yc il c rg a )hp of cils es, ni hw ci h don es are slice ,s a dn e gd es c rro e dnops to eht tesbus relati .pihsno

hT e c“ no tri etub ” pa tr si rom e comp el .x nI a nes se, dec al rative ts ate stnem us( ch as C TSNO and EPYT ) have an eff ce t no ht e v la ue of a va ir able. A C NO ST defini noit se st a va ul e that ac n never be

cha gn ed yb a defi in t noi edon , a dn the di ff ere cn e b te ew en EGETNI R a dn R LAE va ir ab el s can be a ruos ec of elbuort . nO e re tulos i no im hg t be ot s mi ply ex lc du e all non -e ucex at lb e ts atements. We w li l

inc dul e TSNOC de lc a snoitar ni cils es. hT e ton i no of c rtno i ub it no si pa aitr ll y cla ir fied yb t eh rp e cid ate P( - su e) a dn com up at ti no (C- su e) su a eg tsid in tc noi fo aR[ spp ]58 , tub ew en ed ot er fi en

the es f smro fo va ir a elb su age. epS cifi ac ll ,y eht ESU re al t hsnoi ip pertai sn ot fi ev f ro ms fo su age:

P- su e desu ni a etaciderp )noisiced(

C- su e desu ni noitatupmoc

O- su e desu rof tuptuo

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L- su e desu rof noitacol ,sretniop( )stpircsbus

I-u es noitareti lanretni( ,sretnuoc pool )secidni Whi el we er’ at ,ti ew i ned it fy owt f ro ms fo ed f noitini don es:

I-def denifed yb tupni

A-def denifed yb tnemngissa

roF won , rp e us em aht t ht e ils ec V(S , )n is a sl ci e no no e va ir a elb , that si , ht e set V c no is sts of a si lgn e variable, .v If ts a met ent fra mg ent n si a de gninif don e f ro v, ht en n si inc dul ed in the ils ce. If s at teme tn frag em tn n is a su age don e f ro v, then n si ton in lc du ed ni ht e ils ce. P- su se a dn C- su es fo other variab el s ton( ht e v ni ht e cils e set )V are ni cl du ed ot ht e e tx e tn that ht e ri ex ce ut noi fa fe tc s t eh v la ue of the var ai lb e v. sA a diug le eni , if ht e va ul e of v si the same htehw er a stateme tn fra mg e tn is inc dul ed ro e cx l du e ,d xe lc edu ht e ts ateme tn fra mg e tn . L- su e a dn I- su e va ir a lb es a er pyt ci ylla i sivn ib el distuo e rieht udom les, tub th si ha dr ly rp ce dul es ht e lborp me s us ch va ir abl se often cre .eta

onA ther j gdu em tn ca :ll here iw( th s mo e pe )lir we c sooh e ot exc dul e ht ese f mor ht e tni ent fo c“ no t ubir t ”e . suhT O- ,esu L- su e, a dn I- esu don es a er exc dul ed f mor ils ec ..s

E lpmax e

hT e co imm ss i no elborp m si su ed ni th si koob beca su e ti c atno ins i tn e ser it gn d ta a wolf porp erties, a dn these are ton rp ese tn ni ht e irT a lgn e p lbor me ro( in Ne Dtx ate). wolloF these exa lpm se lihw e l gnikoo at ht e cruos e c do e f ro ht e c mo im noiss lborp em ht at we su ed to ana zyl e ni te smr of ed f ni e-

esu ap t .sh

S1: S( as l se m ,na 71 ) = }71{

S2: S( as l se m ,na 81 ) = }71{

S3: S( as l se m ,na 23 ) = }71{

hT e sale ms an var ai lb e si ht e lpmis e ts case ni the rgorp am . tI has no e ed f gnini don e (an I-def at edon )71 . tI sla o o cc ru s at don es 81 a dn 22 , ob th times as an tuptuo va ir able O( - su e), he cn e ti si ton

inc dul ed in cils es S2 a dn S3. Both ,71< >81 a dn ,71< >23 are ed f noitini -cle ra ud -pat .sh Slices no the loc sk va ir ab el s woh yhw it si top entially af lu t- enorp . tI has a P- su e at don e 32 a dn a C-

esu ta edon ,62 a dn has owt ed if in it ,sno eht I- ed fs at sedon 22 a dn .42

S4: (S l ,skco 22 ) = }22{

S5: (S l ,skco 32 ) = { ,22 ,32 ,42 ,92 }03

S6: (S l ,skco 62 ) = ,22{ ,32 ,42 ,92 }03

S7: (S l ,skco 92 ) = }92{

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hT e ils ces f ro ots c sk a dn ba rr e sl are gnirob . htoB are s troh , defin ti noi - lc ear pa sht c no t nia ed en it re yl iw niht a pool , so they are ton a ff ected yb eti tar i sno of ht e l poo . knihT( fo the pool ydob as a DD -

Pa ht .)

S8: ,skcots(S }03 ,92 ,52 ,42 ,32 ,22{ = )52

S9: ,skcots(S }03 ,92 ,52 ,42 ,32 ,22{ = )72

S10: S( ab rr el ,s )52 = ,22{ ,32 ,42 ,52 ,92 }03

S11: S( ab rr el ,s )82 = ,22{ ,32 ,42 ,52 ,92 }03

hT e ne tx f ruo slic se li l su trate woh repe noitit a epp a sr in ils ce .s edoN 91 si an A- ed f f ro un m ol_ c ,sk a dn don e 62 c tno a sni htob an A-d fe a dn a C- .esu hT e r iame gnin don se ni S 31 22( , 32 , 42 , 92 , a dn

)03 per at ni ot eht W ih el -l poo c no t llor ed yb ol c .sk ilS c se S 31 , S 41 , dna S15 are e uq al beca su e don es 33 a dn 63 a ,er resp ce vit le y, an O- esu a dn a C- esu fo _mun loc .sk

S12: S( _mun l ,skco 91 ) = }91{

S13: S( _mun l ,skco 62 ) = ,91{ ,22 ,32 ,42 ,62 ,92 }03

S14: S( _mun l ,skco 33 ) = ,91{ ,22 ,32 ,42 ,62 ,92 }03

S15: S( _mun l ,skco 63 ) = ,91{ ,22 ,32 ,42 ,62 ,92 }03

hT e ils c se no s_mun toc sk a dn b_mun a rr e sl a er tiuq e s imi lar. hT ey a er ini it alized yb A- ed fs ta sedon 02 a dn 12 , a dn ht en are re ed f ni ed yb A-d fe s at don es 72 a dn 82 . gA a ni , ht e rem ia ni gn don se ,22( ,32 ,42 ,92 a dn )03 pert nia to eht While- pool c ortno ell d yb ol c .sk

S16: S( s_mun t ,skco 02 ) = }02{

S17: S( s_mun t ,skco 72 ) = ,02{ ,22 ,32 ,42 ,52 ,72 ,92 }03

S18: S( s_mun t ,skco 43 ) = ,02{ ,22 ,32 ,42 ,52 ,72 ,92 }03

S19: S( s_mun t ,skco 63 ) = ,02{ ,22 ,32 ,42 ,52 ,72 ,92 }03

S20: S( rab_mun rel ,s 12 ) = }12{

S21: S( rab_mun rel ,s 82 ) = ,12{ ,22 ,32 ,42 ,52 ,82 ,92 }03

S22: S( rab_mun rel ,s 53 ) = ,12{ ,22 ,32 ,42 ,52 ,82 ,92 }03

S23: S( rab_mun rel ,s 63 ) = ,12{ ,22 ,32 ,42 2 ,5 ,82 ,92 }03

ehT ne tx t rh ee sli ec s de rtsnom a et ruo c vno e noitn rega gnidr compiler- ed f deni v la .seu

S24: S( p_kcol ir ,ec 63 ) = }3{

S25: S(st rp_kco i ,ec 63 ) = }4{

S26: S( ab rr el rp_ i ,ec 63 ) = }5{

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hT e ils ces no sales a dn com noissim are eht i tn ere gnits no es. hT ere si ylno no e d fe gnini don e f ro sales, the A- ed f at don e 63 . hT e mer a ni i gn ils ces no sales wohs the P- su es, C- su es, a dn ht e O-u es ni defin ti noi -clear pa .sht

S 72 : ,selas(S )63 = ,3{ 5 ,4 }63 ,03 ,92 ,82 ,72 ,62 ,52 ,42 ,32 ,22 ,12 ,02 ,91 ,

S 82 : ,selas(S )73 = ,3{ }63 ,03 ,92 ,82 ,72 ,62 ,52 ,42 ,32 ,22 ,12 ,02 ,91 ,5 ,4

S 92 : ,selas(S )93 = ,3{ }63 ,03 ,92 ,82 ,72 ,62 ,52 ,42 ,32 ,22 ,12 ,02 ,91 ,5 ,4

S 03 : ,selas(S )34 = ,3{ 2 ,91 ,5 ,4 }63 ,03 ,92 ,82 ,72 ,62 ,52 ,42 ,32 ,22 ,12 ,0

S 13 : ,selas(S )54 = ,3{ }63 ,03 ,92 ,82 ,72 ,62 ,52 ,42 ,32 ,22 ,12 ,02 ,91 ,5 ,4

S 23 : ,selas(S )84 = ,3{ }63 ,03 ,92 ,82 ,72 ,62 ,52 ,42 ,32 ,22 ,12 ,02 ,91 ,5 ,4

S 33 : ,selas(S )05 = ,3{ ,12 ,02 ,91 ,5 ,4 }63 ,03 ,92 ,82 ,72 ,62 ,52 ,42 ,32 ,22

knihT a tuob s il ce 72S ni te smr of sti “c nopmo e ”stn , ht e ls ci es no the C- esu var ai b el s. We can rw i et S 72 = S 42 S25 S26 S13 S17 S 12 , hw ere the values of ht e six C-u es var ai lb es at don e 63 era defined yb eht six cils es j nio ed t go ether yb the noinu po erati no . citoN e woh ht e f mro ali ms c rro e sdnops to ruo :noitiutni if ht e va eul fo sales si gnorw , we fi sr t kool ta woh ti si c upmo et ,d a dn if siht si KO , ew check woh t eh com nop ents a er oc m up et .d

vE re ythi gn co em s ot ge ht er (l ti erall )y iw th ht e ecils s no co imm ss i no . hT e er are is x A-def don es f ro com noissim (c rro e dnops i gn ot ht e six ud -pa sht we di e itn fied ea ilr e .)r rhT ee c tupmo ita no s of com noissim are c no t dellor yb P- su es of sa el s ni ht e FI , ESLE IF lo ig c. sihT y ei sdl t rh ee “ ap th ”s fo s il ces ht at c etupmo c mmo issi .no S( ee Fi rug e 4.01 .)

S34: S( mmoc i ss i ,no 14 ) = }14{

S35: S( mmoc i ss i ,no 24 ) = ,14{ }24

S 63 : ,noissimmoc(S )34 = ,3{ ,4 ,5 ,91 }34 ,24 ,14 ,63 ,03 ,92 ,82 ,72 ,62 ,52 ,42 ,32 ,22 ,12 ,02

S37: S( mmoc i ss i ,no 74 ) = }74{

S 83 : ,noissimmoc(S )84 = ,3{ ,4 ,5 ,91 ,02 }84 ,74 ,63 ,03 ,92 ,82 ,72 ,62 ,52 ,42 ,32 ,22 ,12

S 93 : ,noissimmoc(S )05 = ,3{ }05 ,63 ,03 ,92 ,82 ,72 ,62 ,52 ,42 ,32 ,22 ,12 ,02 ,91 ,5 ,4 Whichever c tupmo ita no is take ,n a ll c emo got e reht in eht la ts .ecils

S 04 : ,noissimmoc(S )15 = ,3{ ,4 ,5 ,91 ,02 ,12 ,22 ,32 ,84 ,74 ,34 ,24 ,14 ,63 ,03 ,92 ,82 ,72 ,62 ,52 ,42 }05

hT e ils ce ni f mro ita no vorpmi es ruo hgisni t. kooL ta the la citt e ni rugiF e 01 .4; ti si a rid e tc ed acy lc ci rg a hp in hw ci h ls ices a er don e ,s a dn an e egd re serp ents eht porp er s sbu et ler a hsnoit i .p

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ugiF re 3.4 La ecitt fo Sli ec s no mmoC si si no

sihT la citt e si rd a nw os ht ta the op is noit of ht e sli ec don se ylhguor c rro e sdnops htiw eht ir op is noit in eht cruos e c edo . hT e de inif it no - elc ar ap t sh 34< , >15 , 84< , >15 , a dn 05< , >15 c erro s dnop ot t eh

segde taht wohs secils S 63 , S 83 , dna S39 era sbus te o s f ls i ec S40. rugiF e 3.5 s woh s a al tt ice of ils sec f ro the en it er gorp r ma . oS em sl ci es esoht( t tah are di e itn ac l to othe )sr have eb en ed el ted f ro lc ar ti .y All a er il s et d in Ta elb ,7 gnola w ti h ls ice- eps cif ci tset jbo e vitc es taht are a rporpp ai .et

ugiF re 3.5 La ecitt fo Sli ec s ni t eh Co imm ss i no rgorP am

hT ere is a n ta ur la rbyh id be ewt en ils ec -based te gnits a dn f nu c lanoit et .gnits niS ec s il c se are ed f ni ed htiw re eps tc to var ai lb e ,s sl ci es c rro e dnops to a f nu tc noi al d ce om isop it no of a rgorp am. eB ac su e t eh

bus f snoitcnu a er ms la le ,r c rro se gnidnop s te s fo f nu c noit al te ts cases iw ll eb m ero re nosa a .elb 01 .2.2 elytS dna T ce uqinh e

When ew anal zy e a gorp ar m ni terms fo “i tn e gnitser ” sli ec ,s ew can foc su no pa str fo ni et re ts elihw d si rega gnidr rnu elated .strap We c uo l t’nd od t sih wi ht ud - shtap — they a er se uq e cn es t tah in edulc s at teme stn a dn va ir ab el s ht at may ton eb fo tni ere .ts eB f ero disc gnissu emos an la y it c et c nh i uq e ,s we ll’ fi tsr l koo at “ doog yts el .” eW c dluo ha ev tliub eseht st ly tsi ci cerp e stp otni eht ed fi ,snoitin tub then t eh ed f snoitini eb c emo even erom cumber os m .e

elbaT 7 tseT sevitcejbO rof secilS ni erugiF 5.01 tnelaviuqE secilS

tseT evitcejbO

,1 ,2 3 namselas daer dna nettirw yltcerroc

4 skcol daer yltcerroc

,5 6 skcol lenitnes tcerroc

7 lanoitidda skcol daer yltcerroc

,8 9 ( ,01 )11 evitareti daer fo skcots tcerroc

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,8( )9 ,01 11 evitareti daer fo slerrab tcerroc

12 skcol_mun dezilaitini yltcerroc

,31 ,41 51 skcol_mun detupmoc yltcerroc

16 skcots_mun dezilaitini yltcerroc

,71 ,81 91 skcots_mun detupmoc yltcerroc

20 slerrab_mun dezilaitini yltcerroc

,12 ,22 32 slerrab_mun detupmoc yltcerroc

24 ecirp_kcol tnatsnoc noitinifed tcerroc

25 ecirp_kcots tnatsnoc noitinifed tcerroc

26 ecirp_lerrab tnatsnoc noitinifed tcerroc

,72 ,82 ,92 ,03 ,13 selas pmoc detu yltcerroc

,23 33

34 noissimmoc no tsrif 0001 tcerroc rof selas > 0081

35 noissimmoc no txen 008 tcerroc rof selas > 0081

36 noissimmoc no ssecxe revo 0081 erroc tc rof selas > 0081

37 noissimmoc no tsrif 0001 tcerroc rof 0001 selas< 0081<

38 noissimmoc no ssecxe revo 0001 tcerroc rof 0001 selas< 0081<

39 noissimmoc no selas < 0001 tcerroc

40 noissimmoc nettirw yltcerroc

1. reveN ekam a ecils ,V(S )n rof hcihw selbairav v fo V od ton raeppa ni tnemetats tnemgarf n. sihT ytilibissop si dettimrep yb eht noitinifed fo a ecils , tub ti si dab ecitcarp . sA na ,elpmaxe

esoppus ew denifed a ecils no eht skcol elbairav ta edon 72 . gninifeD hcus secils setatissecen gnikcart eht seulav fo lla selbairav ta lla stniop ni eht margorp .

2. ekaM secils no eno elbairav . ehT tes V ni cils e )n,V(S nac niatnoc lareves ,selbairav dna

semitemos hcus secils era lufesu . ehT ecils ,V(S )63 erehw

=V { ,skcol_mun ,skcots_mun slerrab_mun }

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c atno i sn lla eht eleme stn fo t eh ils ce s{(S ale ,}s )63 e cx e tp t eh TSNOC ed clarati sno a dn ats teme tn .63 Si ecn ht e es owt cils es a er os is mila ,r yhw ed fi en t eh eno in te smr fo C- su es?

3. ekaM secils rof lla A- fed sedon . nehW a elbairav si detupmoc yb na tnemngissa ,tnemetats a ecils

no eht elbairav ta taht tnemetats lliw edulcni snoitrop( )fo lla ud - shtap fo eht selbairav desu ni eht oitatupmoc n. ecilS ,}selas{(S )63 si a doog elpmaxe fo na A- fed ecils .

4. ekaM secils rof P- esu sedon . nehW a elbairav si desu ni a ,etaciderp eht ecils no taht irav elba ta

eht noisiced tnemetats swohs woh eht etaciderp elbairav tog sti eulav . sihT si yrev lufesu ni noisiced - evisnetni smargorp ekil eht elgnairT margorp dna etaDtxeN .

5. secilS no non -P- esu egasu sedon t’nera yrev gnitseretni . eW dessucsid C- esu ils sec ni tniop ,2

erehw ew was yeht erew yrev tnadnuder htiw eht A- fed ecils . secilS no O- esu selbairav nac syawla eb desserpxe sa snoinu fo secils no lla eht A- sfed dna( I- )sfed fo eht O- esu elbairav . secilS I no - esu dna O- esu selbairav era lufesu gnirud ,gniggubed tub fi yeht era detadnam rof lla ,gnitset eht tset

troffe si yllacitamard desaercni .

6. redisnoC gnikam secils elbalipmoc . gnihtoN ni eht noitinifed fo a ecils seriuqer taht eht tes fo stnemetats si ,elbalipmoc tub fi ew ekam siht ,eciohc it snaem taht a tes fo relipmoc evitcerid dna evitaralced stnemetats si a tesbus fo yreve ecils . sA na ,elpmaxe eht ecils S5, = )32 ,skcol(S si hcihw

,22{ ,32 ,42 ,92 ,}03 sniatnoc eht stnemetats

22 (DAER loc )sk ; 23 WH ELI ol c sk <> -1 D O 24 EB IG N 29 R (DAE loc )sk ; 30 ;DNE W{ H ELI locks}

If ew a dd ats et ments 1- 41 a dn ,35 ew have eht c lipmo a elb sli ec nwohs here:

1 pr rgo am loc ots_k c errab_dna_k l 2 noc ts 3 ol c ecirp_k = 45.0; 4 ots ck_ ecirp = 03 .0; 5 ecirp_lerrab = 25.0; 6 t epy 7 NIRTS G_30 = gnirts [ 03 ]; S{ a namsel ’s Na }em 8 av r 9 ol c ,sk stoc ,sk ,slerrab _mun loc ,sk un m_stoc ,sk

10 nu ,slerrab_m elas sma _n i ,xedn edro r dni_ ex : I TN EGER; 11 ,selas c ssimmo i no : ER AL; 12 namselas : TS R NI _G 03 ; 13

41 EB G NI p{ r rgo am loc s_k toc b_dna_k erra l} 22 (DAER loc )sk ; 23 ELIHW ol c sk >< -1 DO 24 NIGEB 29 (DAER loc )sk ; 30 ;DNE {W ELIH lock }s

35 NE .D p{ r rgo am loc s_k toc _k a rab_dn er }l If ew a dd ed this sa em set fo ts at me e stn ot all t eh ils c se ew ma ed f ro t eh co mm noissi rgorp a ,m ruo al tt ci es rema ni dnu i bruts e ,d tub ae ch ls ice si separ ta yle compila elb (a dn reht ef ero ex ce tu a lb e .) nI

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.in t eh f ri st cha tp e ,r ew ggus e ts ed taht doog te ts i gn rp ca cit es lead to bet et r rgorp mma gni rp actic .se He er ew have a doog e ax mpl .e hT i kn a tuob deve pol i gn rgorp ma s in te smr fo compila elb cils e .s fI we did ht i ,s ew c dluo c edo a ecils a dn mmi e id a et ly test .ti eW can then c edo a dn t tse other cils e ,s a dn meht me egr them G( a ll a hg er ac sll this “sl eci ps icil gn ”) into a rp e ytt dilos rgorp am. yrT co gnid t eh co mm noissi rgorp am this wa .y

senilediuG dna oitavresbO ns Dataf wol te ts i gn si c el a ylr i dn ica et d f ro rgorp ma s ht at a er c tupmo ta noi a yll in et .evisn sA a c lloro a yr , in c lortno i tn e evisn gorp mar ,s fi c no t lor var ai selb a er c tupmo ed P( -u es ,)s da at fl wo t tse gni si osla i dn ic eta d. ehT d fe snoitini ew am ed f ro ed f eni /use pa sht a dn ils ec s vig e su v re y rp ce esi wa sy ot desc ebir parts fo a rgorp ma that ew dluow li ek ot te .ts hT e er a er a ac de cim oot ls ht at s troppu ht e es defin ti ,snoi tub they have t’n rgim ated ot eht c mmo ercial ma kr e alpt c .e emoS ip eces a er there; uoy ac n if dn rgorp a imm gn ugnal age c lipmo e sr ht at vorp ide no -scr ee n hgih l hgi it gn fo cils e ,s a dn tsom

de iggub ng t sloo let uoy w“ cta ”h ce tr ia n varia lb es as uoy ts ep hguorht a rgorp ma ex ce u it .no He er are s emo ti stibd ht at may vorp e h le pf lu to ,uoy pa itr c lu a ylr hw en uoy have a id ffic tlu eludom ot et st.

1. secilS t’nod pam ylecin otni tset sesac esuaceb( eht ,rehto non - detaler edoc si llits ni na

elbatucexe )htap . nO eht rehto ,dnah yeht era a ydnah aw y ot etanimile noitcaretni gnoma selbairav . esU eht ecils noitisopmoc hcaorppa ot er - poleved tluciffid snoitces fo ,edoc dna eseht secils erofeb uoy ecilps )esopmoc( meht htiw rehto secils .

2. evitaleR stnemelpmoc fo secils dleiy a ”citsongaid“ libapac yti . ehT evitaler tnemelpmoc fo a tes B htiw tcepser ot rehtona tes A si eht tes fo lla stnemele fo A taht era ton stnemele fo B. tI detoned si sa A - .B redisnoC eht evitaler tnemelpmoc tes ,noissimmoc(S )84 - ,selas(S :)53

,noissimmoc(S )84 = ,3{ ,4 5 ,91,81,63, ,02 ,32 ,42 ,52 ,62 ,72 ,43 ,83 ,93 ,04 }74,54,44

,selas(S )53 = ,3{ ,4 ,5 ,63 ,81 ,91 ,02 ,32 ,42 ,52 ,62 }72

,noissimmoc(S )84 - ,selas(S )53 = ,43{ ,83 ,93 ,04 }74,54,44

If ht e er si a lborp em iw th c noissimmo at il en ,84 we can di ediv eht rgorp am ni to owt pa ,str t eh com up t noita fo sales at enil ,43 a dn eht c upmo at ti no fo co mm noissi be wt een il nes 53 a dn .84 fI sales si KO at enil ,43 t eh borp el m tsum l ei ni t eh ler a vit e c melpmo ent; fi ,ton eht

borp el m may eb in either .noitrop

3. hT ere si a ynam - ot - ynam pihsnoitaler neewteb secils dna DD - :shtaP stnemetats ni eno ecils yam eb ni lareves DD - ,shtaP dna stnemetats ni eno DD - htaP yam eb ni lareves secils . lleW - nesohc

evitaler stnemelpmoc fo secils nac eb lacitnedi ot DD - shtaP . roF pmaxe ,el redisnoc ,noissimmoc(S )04 - ,noissimmoc(S )73 .

4. fI uoy poleved a ecittal fo ,secils s’ti tneinevnoc ot etalutsop a ecils no eht yrev tsrif tnemetats .

sihT ,yaw eht ecittal fo secils syawla setanimret ni eno toor edon . wohS lauqe secils htiw a owt - yaw worra .

5. secilS tibihxe ecnerefer/enifed noitamrofni . redisnoC eht gniwollof secils no :skcol_mun

,skcol_mun(S )71 = φ

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,skcol_mun(S )42 = ,71{ ,02 }?72

,skcol_mun(S )13 = ,71{ ,02 ,42 }72

,skcol_mun(S )43 = ,71{ ,02 ,42 }72

,skcol_mun(S )71 si eht tsrif noitinifed fo skcol_mun .

,skcol_mun(S )42 - _mun(S ,skcol )71 si a noitinifed - ,raelc enifed ecnerefer htap .

When s il ces a er e uq a ,l t eh c rro e gnidnops pa sht a er ed f noitini -c el a .r EXERC SI ES

1. knihT tuoba eht citats susrev cimanyd ytiugibma fo ud - shtap ni smret fo DD - shtaP . sA a star ,t tahw DD - shtaP a er dnuof ni eht ud - shtap ,21p ,31p dna 41p rof ?selas

2. yrT ot egrem emos fo eht DD - htaP desab tset egarevoc scirtem otni eht rekuyeW/sppaR yhcrareih

nwohs ni erugiF 2.01 .

3. pxE r sse s il ec S 04 sa t eh nu i no of ot reh nenitrep t s il .sec

4. dniF eht lof gniwol margorp :secils

a. ,noissimmoc(S )84

b. )53 ,selas(S

.c ,noissimmoc(S ,)04 ,noissimmoc(S ,)93 ,noissimmoc(S )83

d. ,skcol_mun(S )43

.e ,skcots_mun(S )43

f. ,slerrab_mun(S )43

5. dniF eht noitinifed - raelc shtap htiw( tcepser ot )SELAS morf enil 53 :ot ,63 ,04 ,24 ,54 74 .

6. ekaM a ecittal fo ”gnitseretni“ secils . sA a ,muminim edulcni eht seno morf noitseuq 4 .

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UNIT 4

EL V SLE OF T TSE NI ,G NI ET GRA OIT N TSET NI G

4.1 T lanoitidar eiV w of T gnitse L sleve

hT e rt a lanoitid m ledo fo os tf ware dev le mpo ent si ht e aW te fr a ll ledom , ihw ch si rd a nw as a V ni Figure 4.1 to me hp a zis e ht e isab c el v le s of te ts i gn . nI th si v ei w, inf amro it no udorp ec d in no e of t eh deve mpol e tn hp ases c tsno ti tu es the ba sis f ro te ts ca es ide fitn ic ta noi ta that leve .l gnihtoN c no t vor e isr la here: ew rec at ylni uow ld epoh that tsys em test cas se a er s mo e woh c lerro ated htiw t eh re riuq ements s ep cifica noit , a dn ht ta tinu test ac ses are de vir ed f or m the de at i el d de ngis of ht e tinu .

owT sbo e vr ita no :s ht ere si a cl ae r rp e noitpmus of f nu cti no al tes it gn here, a dn there si an mi lp i de “ ob t mot - ”pu tes it gn .redro

ugiF re 4.1 ehT W ta erfa ll Li ef yC lc e Of the t rh ee rt a tid i no al leve sl fo te ts i gn ,tinu( tni e rg ta noi , a dn )metsys , nu it te gnits si best

dnu e ootsr d. hT e et gnits the yro a dn cet nh i uq es we krow ed t guorh h ni Pa str I a dn II are dir ce ylt a pp li ac lb e to inu t et ts i .gn syS et m et ts i gn si dnu e dootsr b te ter than tni e rg ta noi et gnits , ub t ob th en ed cla ir fic oita n. hT e mottob - pu a orpp ca h hs e sd s emo hgisni t: et ts the i dn i udiv la c nopmo e stn , a dn then in et rg ate these otni sysbus et sm nu t li ht e ent ri e sys et m si tested. tsyS me te ts i gn uohs ld be mos e gniht that ht e c motsu er ro( use )r dnu e sr at sdn , a dn ti often drob e sr no c motsu er a cc ep at cn e et s it .gn Gene ar l yl , tsys me tes it gn si f nu c anoit l r ta her t nah st ur c rut a ;l siht is m yltso ud e ot ht e a sb e cn e of a st ur ct ru al ba sis f ro sys et m test cases. nI ht e rt a noitid al v ei w, tni e rg ati no et ts gni si hw ta s’ le tf vo e :r ti s’ on t tinu et ts i gn , a dn ’ti s ton tsys me et ts i gn . oM ts of ht e usu la sid c ssu i sno no in et rg a noit et ts i gn ec tn er no t eh dro er ni ihw ch stinu a er tni e rg eta :d pot - ,nwod ob tt mo - pu , ro eht “big ba gn ”

(eve yr t ih ng ta no ce). Of ht e erht e lev le s, tni e rg a noit si ht e el a ts ew ll dnu e tsr doo ; we’ll od s mo e gniht a uob t aht t ni siht cha retp a dn ht e ne tx . hT e wate fr a ll dom le si c sol le y ossa ci eta d htiw t po - nwod deve mpol e tn a dn de ngis yb f nu c noit la d ce om sop iti no . ehT e dn resu tl fo rp e mil ina yr de ngis si a f tcnu i ano l ed c tisopmo i no of the e ritn e tsys em ni to a ert ile ke urts c rut e of f lanoitcnu co pm no e .stn Figure 4 2. c tno a ni s a pa tr ai l f nu cti no al ed c oitisopmo n of uo r TA M ys ets m. W ti h t ih s d ce oitisopmo n. pot - nwod tni e rg ita no dluow begin htiw the am ni rgorp ma , ch ce gnik ht e ac l sl ot t eh t rh ee next level orp ec rud es T( e nimr la O/I , ManageSe snoiss , a dn rTtcudnoC ansact noi s). gniwolloF the eert , ht e aM nageSe snoiss orp ec rud e dluow be et ts ed, a dn ht en the Ca nEdr t yr , IP N tnE ry, a dn Sele rTtc a sn ca noit rudecorp es. nI each ac es , the ca ut al c do e f ro l wo er vel el stinu is re lp aced yb a st ,bu ihw ch si a worht -away pi ce e of c edo taht takes the lp a ec fo t eh actual c .edo B to tom- pu in et rg a noit dluow be ht e tisoppo e se uq ence, ts ar it gn htiw ht e Ca yrtnEdr , NIP nE rt y, a dn Sele rTtc a sn ca noit corp e rud e ,s a dn krow i gn pu t wo a dr eht main rgorp a .m nI ob t ot m- pu tni e rg ati no ,

inu st at hgih er el vels a er re alp ec d yb vird ers (another f ro m of worht -a yaw c do e) that me lu ate t eh orp ec rud e c la ls. hT e big ba gn a orpp ca h ylpmis up st all the nu ti s t go e ht er at ecno , htiw on buts s ro vird ers. Wh ci hever a orpp ach si at ken, the og al of rt a id it no la tni e rg ita no et ts i gn si to in et rg ate

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.in rp e ylsuoiv te ts ed nu ti s w ti h re eps ct to the f nu c noit la d ce om tisop i no tree. W lih e siht descri seb

in et rg a noit te its gn as a orp ce ss , sid c su is no s of iht s t py e ffo er l eltti inf mro a noit a tuob eht og a sl ro cet uqinh se . Bef ero a erdd iss gn ht ese er( la ) i uss e ,s we en ed to dnu e tsr a dn ht e c sno e uq en ec s of t eh

a tl e nr ta evi l efi cyc el m do e .sl

ugiF re 4.2 Pa laitr nuF cti no al eD c sopmo i noit fo eht MTA syS et m 4.2 A evitanretl Life C elcy sledoM Sin ec ht e early s0891 , rp tca it noi e sr have de siv ed a tl e nr atives ni re nops se ot trohs co im sgn of eht tradi noit al taw erf lla dom el of wtfos a er dev le mpo ent [ rgA e ts i ]68 . moC m no ot lla of ht e es a tl e nr ta vi es si ht e s ih ft away f mor ht e f nu cti ano l ed c noitisopmo ot an hpme isa s no com isop it .no Dec isopmo t noi is a pe fr ce t f ti htob to the t po - nwod rp o rg e noiss of ht e waterf lla dom el a dn ot t eh

tob t mo - pu et ts i gn dro er. nO e of the m roja w ae nk e ss es of wate fr la l dev le mpo ent c ti ed yb [ rgA es it 68 ] is the vo er-re il an ec no this lohw e paradigm. nuF c noit la ed c noitisopmo ac n ylno be we ll enod hw en ht e ys mets si comp el te yl dnu e dootsr , a dn ti orp mo et s a an l sisy ot the near ex lc noisu fo

htnys e is s. ehT re lus t si a ve yr long sepa noitar be wt ee n re riuq ements s ep cifi ac noit a dn a c lpmo te de tsys me , a dn gnirud siht tni e vr la , ht ere si on roppo t nu ti y f ro fee bd ca k f mor t eh c su t mo e .r

Com sop iti ,no no the hto er ha ,dn si lc so er ht e way pe po le krow : ts a tr iw th os m gnihte k nwon a dn dnu e dootsr , ht en a dd ot ti rg a ud ally, a dn ma by e r me vo e dnu ise red snoitrop . hT ere is a ve yr n ci e

an ola yg iw th isop it ve a dn negative sc erutplu . nI neg ta vi e scul erutp , krow orp eec sd yb re ivom ng wnu a tn ed tam e ir al, as ni eht am t meh at ci ia s’n vi we of scul tp i gn iM chela gn le s’o David: ts a tr iw th a

p ei ce of ma br le, a dn simply chip away a ll non -David. vitisoP e sc rutplu e is tfo en nod e wi ht a em muid like wax. hT e c rtne al hs ape si a xorpp mi ta ed, a dn then wax si eit reh a dd ed ro re vom ed nu it l

the sed ired hs ape si ta t nia ed. hT i kn a tuob the c sno e uq e cn se of a tsim ake: htiw negative sculpt ,eru the lohw e krow m su t eb t nworh away, a dn re trats ed. hT( e er si a sum eum ni rolF e ecn , tI la y ht at c no t snia lah f a zod en us ch false ts a str to hT e vaD id.) W hti isop it ve sc plu t eru , eht e norr e uo s pa tr si s mi ply r me vo ed a dn re lp aced. hT e cent ar il yt of oc m op tis noi ni the a etl vitanr e m do e sl has a am j ro mi pl ci a noit f ro i tn e rg a noit te .gnits

4.2.1 retaW f lla nipS -o ff s

hT ere a er t erh e ma nilni e deriva vit es fo the aw et fr lla dom e :l ni creme tn al deve mpol e ,tn e tulov i no a yr deve mpol e ,tn a dn the pS iral dom el oB[ e mh ]88 . E ca h fo t eseh i vlovn es a se ir es of inc er em stn ro

iub l sd , as s nwoh ni Fi rug e 4.3. W nihti a ub i dl , the ron lam wate fr all sahp es f mor det ia led de gis n t guorh h te ts gni o cc ru , w ti h no e tropmi a tn fid ef re cn e: tsys em tes it gn is lps ti otni t ow ets ,sp re rg e noiss a dn rgorp e noiss te ts i .gn

ugiF re 4.3 iL fe yC c el htiw a B iu ld Se uq e ecn

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.in tI is mi trop ant ot ek ep rp el imi na yr de ngis as an tni e rg la hp sa e, ra ht er t nah ot t yr ot a rom t zi e us ch

hi hg level de ngis ca ssor a se ir es of ub li sd . oT( od so usua yll re stlus in nu f ro t nu ate c sno e uq en ec s fo de ngis c oh ices made gnirud ht e ae ylr sdliub that are re rg atte lb e ni later ub li sd .) Sin ec rp e nimil a yr de ngis re am sni a separate step, we are te tpm ed ot c no c dul e ht at in et rg a noit itset gn si fanu fected ni the s nip - fo f dom le .s oT emos ex et tn this si t ur :e the am in apmi tc of ht e series fo ub li sd si that re rg e noiss te gnits b ce mo es en c sse a yr . hT e og la of re rg isse no et ts i gn si ot a russ e ht at thi sgn ht at

krow ed c rro ce tly ni the rp e suoiv iub ld its ll krow htiw the ne ylw a dd ed edoc . rgorP e noiss et ts i gn a muss es t tah re rg e noiss te gnits was s cu ce fss lu , dna ht ta ht e new f nu tc noi al ti y ac n be tset ed. eW( like to t knih ht at the a tidd i no of new c do e re rp e stnes rgorp e ss , ton a re rg e noiss .) Re rg e noiss et gnits si an a ulosb et nece ytiss in a es ri se of sdliub beca su e of ht e we ll - nwonk “ lppir e fe fect” of cha gn es ot

an e ix ts gni tsys e .m ehT( rtsudni ial average si ht ta eno cha gn e in five i udortn ec s a new fa tlu .)

hT e di ff eren ec s a gnom the t erh e nips -off m sledo are ud e ot woh ht e sdliub are di e tn i if ed. nI increme tn al dev le mpo e tn , ht e m vito ita no f ro es rap eta ub i sdl is usu al yl ot lev le fo f the ats ff orp f .eli W hti rup e waterf lla dev le mpo e tn , there can be a guh e ub lge of pe nnosr le f ro ht e hp ases f mor de at eli d de ngis orht u hg u in t te gnits . Mo ts gro anizat noi s ca onn t troppus hcus ra dip ats ff fluc ut ita ,sno os eht tsys me si divid ed otni iub l sd ht at can be roppus et d yb exis it gn rep s nno el. nI e noitulov a yr

deve mpol e ,tn ht ere si ts lli t eh rp e noitpmus of a dliub es uq ence, tub no ly ht e fi tsr ub li d si defi .den aB sed no ti , later sdliub are di e itn fied, usu lla y ni r se snop e to rp i itiro es set yb the c su t mo er/ su e ,r os

the ys mets e ov lves ot m tee the cha gnign n ee sd of the su er. hT e spir la m do el si a c nibmo ati no fo rapid orp tot gnipy a dn e ov lu it no a yr dev le po em ,tn ni hw ci h a iub ld si ed f ni ed rif ts in terms of ra dip

orp tot py ing, a dn ht en is bus jec et d to a on/og og d ce noisi based no tec onh l ygo -rela et d ksir af c srot . morF siht we s ee ht at ek epi gn rp e il im na yr de is gn as an in et rg al ts ep is di ff ci lu t f ro the e noitulov a yr

a dn spir la m edo ls. oT ht e e tx ent that siht ac onn t be ma tni ia ned as an rgetni la ca ytivit , tni e rg ta noi et ts i gn is neg ta vi le y a eff cte .d

Beca su e a iub ld si a s te of d le vi era lb e e dn - resu f nu itc no a ytil , no e a vd a tn age of ht ese spin- ffo m do e sl si aht t all t erh e y ei dl earlier nys the sis . sihT la os re us l st ni ear eil r motsuc er fee bd ack, os wt o

fo t eh ed fi eic ncies fo wate fr la l devel po em tn a er gitim eta .d 4.2.2 pS ce ific ta i no B desa M do le s

owT hto er va ir a snoit ra e re nops s se to ht e lpmoc“ ete dnu e tsr a dn i ”gn lborp em. ceR( la l that f tcnu i ano l ed c sopmo i noit si us cce ss f lu no ly when ht e tsys me si c lpmo e et ly dnu e sr t doo .) W neh

tsys me s are ton fully dnu er ts oo d b( y tie her ht e c motsu re or ht e devel po e )r , f nu itc no al d ce noitisopmo si per li suo at b se t. hT e rapid otorp t ipy ng l efi cy lc e ugiF( er 21 . )4 ed la s wi ht siht yb rd as it c la yl re ud c gni ht e spec fi ci a noit -to-c su t mo er fee bd ca k l poo to udorp ec ve yr ae r yl nys thesi .s aR ht er than ub i dl a anif l ys ets m, a “ iuq ck a dn di ytr ” pytotorp e si ub i tl a dn then su ed to e il c ti

c motsu er f ee bd ack. Depe gnidn no the fee bd ca ,k m ro e ipytotorp ng cycles am y o cc ru . nO ce t eh deve pol er a dn eht c su t mo er a rg ee that a pytotorp e re rp ese stn eht de ris ed tsys me , the deve pol er seog a eh ad a dn ub li ds ot a c rro ect eps cifi ac noit . tA this op i tn , a yn fo t eh aw et rfa ll spin-o ff s im thg sla o eb

su ed .

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ugiF re 4.4 Ra dip ytotorP ip ng iL fe Cyc el

aR dip orp tot gnipy has in et re gnits ilpmi ac ti sno f ro s tsy me t tse gni . Where are ht e re uq irements? sI the la ts pytotorp e ht e sp ce if ci a noit ? woH a er s tsy me test cases t ar ec d kcab to the orp tot epy ? enO

doog answer to uq e ts i sno such as eseht is to su e the orp tot py i gn cycle(s) as ni f mro a it no ga ht e gnir ca tiv eiti s, a dn then udorp ce a re riuq emen st ps ec fi ic ta i no in a erom tra tid i no al ma nn er. htonA er

ibissop il yt si ot ca rutp e hw at the c su t mo er od es htiw the orp tot py e )s( , ed f ni e these as cs ena soir that are mi trop ant ot the c otsu em r, a dn then su e ht ese as s tsy em te ts ac ses. hT e ma ni c no t noitubir fo rapid orp tot py i gn si that ti rb i sgn the po era anoit l ro( beha roiv a )l iv e tniopw to the re uq ireme stn ps e ic f ci a noit hp a es . usU ally, re uq i er em stn ps e fic ci ati no et c nh i uq es e hpm asize the urts c rut e of a

tsys me , ton sti behavi .ro hT is is nu f ro t anu t ,e suaceb e m so t cu ts mo ers ’nod t ca er a tuob ht e st ur c erut , a dn ht ey od ca er a tuob eht behavi .ro

xE ec tu a lb e ps eci cif ita sno giF( ure 4. )5 a er an e tx e noisn of the rapid p pytotor ing c cno e tp . W hti siht a aorpp ch, ht e re riuq e stnem are eps c fi ei d ni an xe ce tu a lb e f ro tam us( ch sa finite ts ate m ca hines ro Pe irt ne )st . hT e c su t remo then ex ce tu es ht e eps cifi ac ti no ot sbo e vr e the ni et dn ed s sy et m beha ,roiv a dn divorp es fee bd ca k as ni t eh ra dip pytotorp i gn m do e .l

ugiF re 4.5 exE c tu able pS ecif ci a noit

nO e big d effi re cn e is ht ta ht e re uq ireme stn eps cif ci a noit od c mu ent si pxe icil t, as oppo es d to a orp tot py e. roM e ropmi t na t, ti is fo et n a em cha in ac l corp e ss ot de vir e tsys em test cases f or m na

e ex c tu a lb e ps ecif ci ita .no guohtlA h rom e row k si re uq ired ot deve pol an e ex c tu a lb e ps ecif ci ita ,no this is par it ylla of sf et yb the re cud ed fe f tro ot generate sys et m te ts case .s htonA er tropmi a tn d itsi :noitcn hw en s etsy m test gni si based no an e ex c tu ab el eps cifi ac ti no , we have a f mro fo st ur ct ru al te gnits at eht sys et m leve .l

4.2.3 nA O ejb ct-Orie tn ed Li ef yC c el M do el When s wtfo a er is deve pol ed iw th an bo cej t ro ei tn ita no , enon fo ruo fil e cy lc e m do le s f ti ve yr we ll .

hT e ma ni r sae :sno the jbo e tc iro e atn t noi si hgih ly com op tis noi al in an t eru , a dn ht ere si de esn in et ar tc noi a gnom ht e urtsnoc c noit sahp es of jbo e tc - iro e tn ed ana sisyl , bo ej ct- ro ie tn ed desi ng , a dn

jbo e tc - iro e tn ed rgorp a mm i gn . We c dluo wohs siht htiw cnuonorp ed f ee abd ck l spoo gnoma wa et r af ll hp ases, tub ht e f tnuo ain dom le [He dn e nosr -Sel el rs 09 ] si a much m ro e a etairporpp

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em ta ohp r. nI ht e f tnuo a ni dom e ,l (see rugiF e )6.21 ht e f dnuo ati no si t eh re uq ireme stn ana sisyl of real row ld ys ets .sm

ugiF re 4.6 nuoF iat n M do el fo bO ject- irO e tn ed oS tf wa er Deve mpol e tn

sA ht e bo ject- iro e tn ed paradigm orp cee sd , deta sli “ lbbub e pu ” hguorht sp ce if ci ita no , ngised , a dn c do i gn hp ases, ub t at ae ch ts a eg , mos e of the “fl wo ” spord ab ck ot t eh rp evi uo s hp ase(s). sihT m do el ac rutp es t eh er al yti fo eht way pe elpo ac ut lla y krow (even iw th eht tradi noit al a orpp ca h se .) 4.3 oitalumroF ns fo eht S TA M metsyS nI th si a dn the ne tx rht ee cha tp e ,sr ew iw ll rel ta e ruo sid cu iss no ot a hgih er level exam ,elp t eh

S mi p el motuA a it c lleT er M ca hi en TAS( M) s tsy me . hT e ve sr i no deve depol here si a re siv i no fo t tah f dnuo ni [ ppoT er 39 ;] it si iub lt a dnuor the fif et en scr ee sn nwohs in rugiF e 4. .7 ihT s si a rg e ylta re cud ed tsys me ; commerc lai MTA ys ts sme ha ev rdnuh eds fo sc er e sn a dn mun e suor it me- uo t .s

hT e MTAS et nimr la si ks etched ni Fi rug e 21 . ;8 ni a tidd i no to ht e di lps ay sc er en, ht ere are f tcnu i no tub t sno B1, 2B , a dn B3, a di ig t ke py ad htiw a can ec l key, stols f ro rp i tn er rece stpi a dn MTA ca ,sdr

and rood s f ro de sop ti s dna ac hs wi rdht awals. hT e MTAS sy mets is d se rc ibed here ni owt wa :sy htiw a urts c rut ed a an l sisy a orpp ach, a dn htiw an jbo e tc - iro e tn ed a aorpp ch. hT ese descrip it no s a er

ton c lpmo e ,et tub they c tno ia n d te lia suff ci ie tn ot i ll u ts ra et eht te ts i gn t ce uqinh es dnu er sid c ssu i .no 4.3.1 MTAS w hti tS ruc ut er d sylanA is

hT e st ur tc ru ed a an l sy si a aorpp ch to re riuq eme stn ps ecif ci ta noi si the m tso wide yl u es d tem doh ni the row ld . tI enj yo s ex et visn e C ESA t oo l ppus ort as we ll sa c mmo e cr i la tra ni i gn , a dn si described ni

mun e suor et stx . hT e tec uqinh e si based no t erh e oc mp el me tn a yr m do e :sl nuf itc no , ad ta, a dn c .lortno Here we su e d ta a fl wo dia rg a sm f ro eht f nu c noit al dom le ,s e itn t r/y le hsnoita ip m do le s f ro da ,at a dn f tini e s at te am c nih e dom e sl f ro ht e c ortno l a eps ct of ht e TAS M tsys e .m hT e f nu cti no al a dn da at m do e sl were rd a nw wi ht ht e Deft ESAC loot f mor byS ase cnI . hT ta t loo ide itn fies e tx e nr al dev ci se

us( ch as t eh et mr inal )srood wi ht l wo er case le tt e ,sr a dn elements of ht e f cnu it lano ed c sopmo i it no htiw bmun e sr us( ch as 5.1 f ro the Va il date Ca dr nuf cti )no . hT e po en a dn lif el d a ehworr ads no lf wo

a sworr ngis fi y hw ether ht e fl wo item si s mi lp e ro c dnuopmo . ehT trop i sno of the MTAS tsys me nwohs here pe tr a ni genera yll to ht e pe nosr al di e itn fic noita bmun er )NIP( ve ir f ci at noi noitrop of the

tsys me .

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ugiF re 4.7 Sc er e sn f ro eht MTAS tsyS em

hT e Deft C ESA t loo hsiugnitsid es b te ew en s mi lp e a dn c dnuopmo f ,swol hw ere c dnuopmo fl swo may be d ce opmo es d into hto er f swol , ihw ch may ht e sm e vl es be com dnuop . hT e rg a cihp a pp ae ra ecn of this c ioh ec si ht ta is m lp e fl swo have lif led a hworr ae sd , hw ile c dnuopmo fl swo have po en a hworr ae ds. sA an exa ,elpm t eh com dnuop f wol s“ c er e ”n has t eh fol wol i gn d ce :noitisopmo sc er en is com sirp ed fo

neercs 1 lew come

neercs 2 retne NIP

neercs 3 gnorw NIP

neercs 4 NIP ,deliaf c dra deniater

neercs 5 tceles snart epyt

neercs 6 tceles tnuocca epyt

neercs 7 retne am tnuo

neercs 8 iffusni c ei nt nuf ds

neercs 9 c tonna esnepsid taht tnuoma

neercs 10 tonnac ssecorp rdhtiw awa sl

neercs 11 ekat ruoy hsac

neercs 12 tonnac ssecorp stisoped

neercs 13 tup ped polevne ni tols

neercs 14 rehtona art n as c it o ?n

neercs 15 ;sknahT ekat drac dna tpiecer

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ugiF re 4.8 ehT TAS M Term ni al

ugiF re 4.9 C tno e tx iD a rg am fo eht MTAS syS et m

ugiF re 4. 01 Level 1 taD a wolf iD a rg ma fo eht MTAS yS ets m Figure 4. 11 si an (inc lpmo ete) tnE ti y R/ le a hsnoit ip d ai rg am of ht e rojam da at st ur ct ru es in the

TAS M s sy et :m sremotsuC , A cc nuo ts, Ter im na ,sl a dn rT a sn a snoitc . dooG d ata m do e gnil rp ca ecit d ci tat se lutsop ati gn an e ytitn f ro cae h rop t noi of ht e s tsy em ht ta is desc debir yb d ata ht ta si retained (a dn su ed yb f nu c it no la nopmoc e )stn . Am gno the d ata the sys et m dluow en ed f ro ae ch c otsu em r a er the c motsu e ’r s di entif ci a noit a dn pe lanosr acc tnuo bmun er ;)NAP( these are enc do ed ni to t eh

am ng e cit st pir no the c su to em s’r TA M ca .dr We uow ld al os wa tn ot wonk ni f mro ta noi a ob tu a c motsu e s’r a cc ,)s(tnuo ni lc du i gn eht acc nuo t bmun e ,sr ht e b ala cn es, the pyt e of acc tnuo s( a sgniv or c eh cki )gn , a dn ht e Pe nosr la dI e itn f ci ta noi uN m reb (P )NI of the acc tnuo . At siht tniop , we hgim t a ks

yhw ht e IP N si on t a oss c tai ed w ti h ht e c su t mo er, a dn ht e NAP htiw an cca tnuo . oS me de ngis has cre tp into eht s ep cifi ac noit at this op int: if the ad at were sa uq e noits ed, a pers s’no MTA ac dr c luo d be su ed yb a noyn ;e sa it si , the rp esent separa noit rp ed si sop es a es c ru ti y checki gn corp e erud . Pa tr fo the /E R dom el sed c bir es rela it spihsno a gnom eht en it it e :s a cu ts mo er SAH cca )s(tnuo , a c motsu er c udno c st transac it )s(no ni a SE SS I NO , a dn , i dn epe dn e tn of c motsu er inf ro am noit , rt a asn c (noit s)

UCCO R at an MTA et r im nal. hT e si lgn e a dn lbuod e a hworr ea sd ingis fy ht e ugnis al ri yt ro pl ru a ytil of these rela it no s spih : no e c otsu em r may have several a cc stnuo , a dn yam c udno ct non e ro several transa itc no s. Ma yn rt a sn acti sno am y o cc ru ta a te nimr al, tub no e rt a asn cti no never o cc sru at a mu tl ilpi c ti y fo te nimr la .s

ugiF re 4. 11 tnE ti y R/ le a hsnoit ip doM el fo t eh TAS M tsyS me

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hT e dataf wol id a rg a sm a dn ht e e r/ytitn ale pihsnoit m do el c tno ia n ni f ro itam no ht at is rp ima ylir st ur ct ru al. sihT si elborp am it c f ro te ts e sr , beca su e test cases are c cno e denr w ti h beha roiv , ton htiw st ur ct ru e. sA a s lppu me tne , ht e f nu c noit al a dn data inf mro ati no are il kn ed yb a c no t lor m do e ;l he er we su e a f tini e tats e am c enih . noC t lor dom els rper esent the op int at hcihw urts c rut e a dn beha roiv in et esr tc ; as us c ,h ht ey a er fo s ep c lai itu lity ot te ts e .sr

ugiF re 4. 21 ppU er Level TAS M Fini et tS a et Ma enihc

hT e ppu er el v le fin eti s tat e machine ni Fi rug e 4. 21 divides the tsys em otni ts a set that c rro e dnops ot s at ges of c otsu em r usa eg . htO er c ioh ec s are sop is ble, f ro instance, we im thg c sooh e ats tes ot be sc er e sn b ie gn lpsid ayed siht( snrut uo t ot eb a roop c ioh ec ). tiniF e ts ate am c nih es can eb h ei rarc cih a yll ed c sopmo ed ni um ch ht e same yaw as da at lf wo id a rg a .sm hT e d ce om sop iti no fo t eh

wA a ti NIP ats te si s nwoh ni Fi rug e 4. .31 nI htob of these f rugi se , sta et tisnart i sno a er ca su ed rehtie yb eve stn ta ht e MTA t re im n la (such as a ke kortsy e) ro by d ata c dno i snoit (such as the rec ngo ti noi

that a NIP is c rro ect). hW en a rt a tisn i no co c ru s, a c rro e gnidnops acti no may a osl o cc ru . We c sooh e to u es sc er en lpsid a sy as us ch act snoi ; siht c cioh e liw l vorp e to be ve yr ha ydn hw en we deve pol

tsys me level te ts ac se .s

hT e f nu cti ,no ad ta, a dn noc t lor dom le s are the ba sis f ro de ngis a tc vi i it es in the aw et r af ll dom el (a dn ti s nips -offs). ruD i gn ngised , mos e of the ro iginal de snoisic may be revised based no a idd it no al

in is sthg a dn rom e ated iled re uq i er em stn (f ro e ax mple, perf mro a cn e ro iler a ytilib og la )s . hT e e dn resu tl is a f nu c it no al isopmoced ti no us ch sa ht e pa tr i la no e nwohs in t eh ts ur tc ru e chart in Figu er 4.14. citoN e that the giro inal f ri st lev le ed c opmo is noit into f ruo s ysbu ets sm si c no t uni e :d t eh f tcnu i ano l yti has b ee sopmocedn ed ot l wo er levels of d te lia . C oh ices us ch as these are ht e se se cn e of de ngis , a dn d ise ng si dnoyeb the sc po e of th si koob . nI arp c it ce, te ts e sr often have to il ve w ti h eht resu tl s fo roop de ngis c ioh c .se

ugiF re 4. 31 NIP yrtnE iniF te St eta aM c enih If we ylno u es a urts tc ru e chart to diug e tni e rg ta noi te gnits , we ssim ht e fact ht ta mos e pyt( aci yll l wo er el vel) f nu c snoit ra e su ed ni m ro e than no e alp ce. He er , f ro e elpmax , ht e Sc er e virDn er f tcnu i no si su ed yb several other ludom es, ub t it no ly a pp ae sr cno e in the nuf itc no al ed com tisop i no . nI the ne tx cha tp er, we wi ll es e that a “ca ll rg a hp ” is a much b te ter ba sis f ro tni e rg ita no te ts ac es

ide itn ficat oi n. We can deve pol ht e be gninnig s of such a ca ll rg a hp f mor a rom e det ia led iv ew fo

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rop t snoi of the sys et m. oT troppus siht , we deen a bmun ered ed c sopmo ti noi , a dn a m ro e det ia del iv ew fo owt fo eht com nop ent .s

Here is eht f nu c noit al d ce sopmo i noit ca rr ei d htruf er ni eniltuo f :mro t eh bmun e gnir cs heme

rp ese vr se eht leve sl fo eht c nopmo e stn in iF rug e 21 . .41

ugiF re 4. 41 A D ce om sop ti noi eerT f ro eht MTAS yS ets m

1 MTAS metsyS

1.1 eciveD esneS & lortnoC

.1.1 1 rooD esneS & lortnoC

.1.1.1 1 teG rooD sutatS

.1.1.1 2 lortnoC rooD

.1.1.1 3 esnepsiD hsaC

.1.1 2 tolS esneS & lortnoC

.2.1.1 1 aW tolSdraChct

.2.1.1 2 teG tisopeD tolS sutatS

.2.1.1 3 lortnoC draC relloR

.2.1.1 3 lortnoC epolevnE relloR

.2.1.1 5 pirtS draC daeR

1.2 lartneC knaB mmoC .

.2.1 1 teG NIP NAP rof

.2.1 2 teG tnuoccA sutatS

.2.1 3 tsoP yliaD snoitcasnarT

1.3 lanimreT esneS & lortnoC

.3.1 1 neercS revirD

.3.1 2 yeK rosneS

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1.4 eganaM noisseS

.4.1 1 etadilaV draC

.4.1 2 etadilaV NIP

.2.4.1 1 NIPteG

.4.1 3 esolC noisseS

.3.4.1 1 weN noitcasnarT tseuqeR

.3.4.1 2 P tnir tpieceR

.3.4.1 3 tsoP noitcasnarT lacoL

.4.1 4 eganaM noitcasnarT

.4.4.1 1 teG noitcasnarT epyT

.4.4.1 2 teG tnuoccA epyT

.4.4.1 3 tropeR ecnalaB

.4.4.1 4 ssecorP tisopeD

.4.4.1 5 ssecorP lawardhtiW

sA pa tr of the ps ec fi ci ta noi a dn desi ng orp sec s, each f lanoitcnu com nop e tn si ron llam y e px a dn ed ot wohs i st stupni , uo t stup , a dn em chani ms . eW od siht here w ti h sp e odu -c do e ro( PDL, f ro rp o rg ma

de ngis la ugn age) f ro t erh e m ludo e .s hT si pa itr cul ra PDL si ylesool based no Pa cs la ; t eh op i tn of a yn PDL is to c cinummo a et , on t to deve pol os em gniht th ta can be c lipmo ed. hT e m nia rgorp ma desc noitpir f wollo s the f ni ite ts ate am chi en desc noitpir vig en ni giF ure 4. 21 . atS et s ni that id a rg am a er “ lpmi emen et ”d w ti h a Ca es ts ateme .tn iaM n P rgor ma atS et = wA tia draC SAC E S etat FO awA Cti :dra S erc Dne evir 1(r , un )ll

W cta aCh lSdr (to draC olS atSt sut ) W LIH E C Sdra tol tatS su si I eld OD

W hcta raC olSd C(t Sdra tol atS sut ) C tno lor draC loR (rel cca )tpe V ila tad raCe C(d Odra ,K )NAP IF C dra KO T NEH St eta = A iaw IPt N

E ESL oC ortn aCl Rdr lo l e(re cej )t

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tS eta = A iaw aCt dr awA Pti :NI V ila tad NIPe IP( ,koN AP )N

IF P oNI k T NEH cS neer irD rev ,2( lun l) tS eta = A iaw rTt sna

E ESL cS neer irD rev ,4( lun l) tS eta = A iaw aCt dr

awA Tti snar : M ana Teg snar a tc noi S tat e = olC Ses isse no

olC Ses isse no : IF N Twe snar tca Rnoi uqe tse T NEH tS eta = A tiaw rT a sn E ESL rP Rtni e ec tpi

oP rTts sna itca Lno aco l lC Seso sse noi oC ortn aCl oRdr ell e(r cej )t tS eta = A tiaw raC d

dnE , ( ESAC tS )eta NE D. ( niaM rp rgo ma S MTA )

hT e Val di ate NIP orp ec rud e is based no a on ther f tini e ts eta m ca hine nwohs ni Fi rug e 4. 31 , ni ihw ch

s at tes er fer ot t eh bmun er fo NIP e yrtn atte .stpm orP dec eru laV adi IPet P(N oNI ,k P )NA teG NIP Prof (NA ,NAP xE cep det )NIP yrT = sriF t SAC E T yr OF riF ts : rcS Dnee vir (re ,2 n llu )

PteG (NI tnE ere NIPd ) FI E etn der NIP = E epx detc NIP

T NEH P koNI = EURT R RUTE N

E ESL S eerc rDn revi ,3( lun l) T yr = eS dnoc

ceS dno : rcS Dnee vir (re ,2 n llu ) PteG (NI tnE ere NIPd )

FI E etn der NIP = E epx detc NIP T NEH P koNI = EURT

R RUTE N E ESL S eerc rDn revi ,3( lun l)

T yr = hT dri ihT dr : rcS Dnee vir (re ,2 n llu )

PteG (NI tnE ere NIPd ) FI E etn der NIP = E epx detc NIP

T NEH P koNI = EURT R RUTE N

E ESL S eerc rDn revi ,4( lun l) P koNI = SLAF E

DNE , ( ESAC rT )y NE D. orP( dec eru laV adi Pet )NI

hT e eG t NIP orp ec rud e is based no a on ther f tini e s at te am c enih in ihw ch s at tes fer er ot eht bmun er

of gid i st rec ie ved, a dn ni a yn ts ,eta e ti her a on ther tigid key ac n be uot che ,d ro the ac ncel key ac n eb

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.in t uo ch de . Ra ht er ht an htona er ESAC ts ateme tn lpmi eme atn it no , eht “s at te ”s are c llo a sp ed otni

eti tar i sno fo a W ELIH l .poo

orP dec eru teG (NIP tnE ere IPd ,N C cna iHle )t coL la ataD : D igi yeKt s = { ,0 1, 2, ,3 ,4 ,5 ,6 ,7 ,8 }9 GEB NI naC lec tiH F = ESLA tnE ere NIPd = lun l ts nir g giD sti dvcR 0= IHW EL (TON giD Rsti dvc 4= RO cnaC Hle )ti OD

EB NIG eK eSy osn eK(r iHy )t FI eK tiHy NI iD tig syeK HT NE IGEB N

tnE ere IPd N = tnE ere NIPd + HyeK ti NI MERC TNE iD( stig vcR )d FI giD sti vcR 1=d EHT N cS eer irDn rev ,2( X' - )' FI giD sti vcR 2=d EHT N cS eer irDn rev ,2( X' X- )' FI giD sti vcR 3=d EHT N cS eer irDn rev ,2( X' XX -') FI giD sti vcR 4=d EHT N cS eer irDn rev ,2( X' 'XXX ) DNE

NE D HW{ }ELI NE D. rP( eco rud e eG IPt )N

If we f wollo ht e sp e docodu e ni t seh e rht ee m ludo es, we can ide itn fy the su“ e ”s r le pihsnoita ma gno t eh m ludo es ni t eh f nu c noit al ed c tisopmo i .no

ludoM e sesU seludoM

MTAS Main tolSdraChctaW

lortnoC draC relloR

neercS revirD

etadilaV raC d

etadilaV NIP

eganaM asnarT c oit n

weN noitcasnarT eR uq tse

IPetadilaV N PteG I fN APro N

NIPteG

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neercS revirD

IPteG N yeK S sne ro

neercS revirD

itoN ce t tah the “ su es” ni f mro ati no si ton er a ylid a pp are tn ni the f nu c noit la d ce om isop it no . sihT inf ro m noita is dev le po ed (a dn e tx e sn ive yl re siv e )d rud ing the rom e detai el d hp ases of the desi ng

orp ec ss . We wi ll re tisiv ht is ni Cha tp er .31 4.4 gnitarapeS In oitarget n and Sy mets T itse gn

eW are alm tso ni a noitisop to make a elc ar di nits c it no b te ween i tn e rg at noi a dn s tsy em et s it gn . eW n ee d this cnitsid it no to diova ga sp a dn re dnud a icn es ac sor s lev le s of test gni , ot cla ir fy a rporpp ai et og sla f ro ht e es el v le ,s dna ot dnu e tsr a dn woh ot ide itn fy et ts cases at fid ef re tn leve sl . hT is elohw

d csi noissu si af lic ti ated yb a c no ec tp e ss e itn al ot lla lev le s of et :gnits ht e on it no of a “ erht a ”d . A t rh ead is a c sno t ur tc that refe sr ot ex ce tu i no time beha roiv ; hw en we te ts a ys ets m, we su e te ts ca ses to sel tce (a dn ex ce u et ) rht ae sd . We ac n ps eak fo leve sl of rht ae :sd s tsy em rht ae sd desc bir e sys t me el v le beha ,roiv i tn e rg ta noi erht a sd c rro es dnop ot i tn e rg a noit level behavi ro , a dn nu it erht a sd

c rro e dnops ot nu ti level beha roiv . aM yn aut sroh su e ht e te mr , ub t ef w ed f ni e ti , a dn of soht e t tah ,od the fo ef red ed fini snoit are ’n t very he pl f lu . roF now, we take “ erht ad” ot be a timirp ive et mr , hcum il ke f nu c noit a dn da at . nI the next owt cha tp e sr , we hs all es e taht t erh a sd are mo ts o etf n rec zingo ed

in terms of ht e way s sy te sm a er desc bir ed a dn ved e pol e .d roF ex ma elp , we im hg t ht i kn of a rht e da as a path t hguorh a f tini e ts ate am c nih e descript noi of a s sy et m, ro we thgim kniht of a rht ae d as

mos e gniht that si dete nimr ed yb a data c tno e tx dna a se uq e cn e of rop t vel el upni t eve stn , us ch sa t soh e ni ht e c tno e tx d ai rg am of ht e MTAS ys ets .m eW c uo ld osla kniht of a t rh ead as a se uq e cn e fo

ruos ec ts atements, ro as a es uq en ec of m ca enih urtsni c snoit . hT e tniop is, rht ea sd a er a gene cir c cno e ,tp a dn ht ey e tsix i dn epe dn e yltn fo woh a tsys em si described a dn devel po e .d

eW have a rl ae yd sbo e vr ed ht e st ur tc ru la ve susr behavi ro al id c oh tomy; here we hs all f dni th ta htob of these v ei sw h le p su pes arate tni e rg ati no a dn s tsy em te gnits . hT e urts tc ru al v ei w r fe le stc htob t eh

orp ec ss yb hw ci h a etsys m si ub li t a dn ht e et c uqinh se su ed to ub i dl ti . We ec tr ia nly e px ce t aht t te ts ac ses at vari suo leve sl c na be t ar ec d b ca k ot deve mpol e tn al ni f ro am ti no . While siht si en ec ss a yr , it

fa li s to eb fus ficient: ew wi ll if na yll ma ek ruo de ris ed separati no ni te smr fo behavi ro al c sno t ur tc .s

ugiF re 4. 51 MTAS Cla ss iH erarc yh

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vE re noy e a rg ee s that ht ere m su t be so em nitsid itc no , a dn taht tni e rg ati no et ts i gn si ta a m ro e de at eli d el vel than etsys m te gnits . hT ere si a osl general a rg ee me tn ht at tni e rg a noit t se ti gn ac n s fa e yl a muss e ht ta t eh nu i st vah e b ee n es parate yl te ts ed, a dn ht a ,t taken yb the sm le ves, the inu st f tcnu i no c rro ce ylt . nO e co mm no iv ew, theref ero , si taht ni te rg ati no te ts i gn is c no ec nr ed iw th ht e tni e afr c se a gnom ht e nu i st . nO e ilibissop ty is to fa ll b ca k no the mmys e rt i se in ht e water af ll il ef cy elc m ,ledo a dn as y that tni e rg at noi te gnits is c no ce nr ed htiw rp e nimil a yr de gis n inf ro am ti no , w lih e s tsy em et ts i gn is at the lev le of ht e re riuq eme stn sp ce if ci ta noi . sihT si a lupop ar acade im c v ei w, tub it be sg

an tropmi a tn uq e noits : woh od we sid criminate b te ew en eps ci cif ati no a dn rp e nimil a yr de ngis ? ehT pat acad me ic answer ot siht is eht hw at vs. woh id c oh tom :y the re riuq eme stn s ep cif ci a noit ed f seni

hw at, a dn the milerp ina yr de ngis describes woh . W lih e siht s sdnuo doog ta f ri st, it od e ’ns t s at dn pu we ll in arp tc eci . oS em lohcs a sr a ugr e taht j tsu eht c cioh e of a re riuq eme stn ps ecific ita no et c nh euqi si a de ngis c oh i ec

hT e fil e cycle a aorpp ch is ce oh ed yb de ngis e sr ohw often take a “ t’noD rT ae d nO Me” v ei w of a

re riuq ements s ep cifi ac :noit a re riuq eme stn eps c fi ci a it no dluohs neither rp e sopsid e ron rp ce dul e a de ngis noitpo . Wi ht this iv ew, hw en ni f ro am ti no ni a s ep cif ci ati no si so deta li ed aht t ti “ ts eps no t eh de ngis e s’r toe ”s , the eps ci if c ita no si oot deta li ed. sihT s sdnuo doog , tub it its ll ’nseod t y ei dl an po era noit al way ot separa et tni e rg ita no a dn metsys t se ti .gn

hT e sledom u es d ni the deve mpol ent orp ec ss vorp ide s mo e clues. If we f lo l wo the ed if in it no of t eh

TAS M tsys em, ew c dluo f tsri lutsop ta e that s etsy m te gnits uohs ld make rus e aht t all fift ee n di yalps sc er e sn have b ee n generated. nA( tuptuo mod a ni based, f nu cti no al v ei w of sys tem t tse gni .) ehT e r/ytitn ela pihsnoit m edo l a osl he :spl eht no e- ot - no e a dn no e-to-ma yn relati spihsno pleh su

dnu e tsr a dn woh um ch t gnitse sum t be nod .e ehT c ortno l dom le ni( siht ca es , a h ei rarc yh of f ni i et s at te am c nih es) is the m tso h le pf .lu We can utsop l ta e ys ets m test cases ni t re sm of pa sht hguorht t eh f tini e ats te am c nih e )s( ; gniod siht iy e sdl a sys et m level ana gol fo st ur ct laru t gnitse . hT e f nu itc no al m do e sl d( a at f wol id a rg a sm a dn urts c rut e chart )s vom e in ht e erid c noit of leve sl ceb a su e ob th e rpx e ss a f anoitcnu l ed c tisopmo i no . vE en iw th t sih , we ca tonn kool at a urts c rut e cha tr a dn di e itn fy

hw ere ys ets m tes it gn sdne a dn tni e rg ita no t se ti gn ts a str . ehT tseb ew can od htiw urts tc ru al inf ro m noita si di e itn fy the e rtx emes. roF insta ,ecn ht e f wollo ing rht ae ds are a ll clea ylr at eht s tsy em el v le :

1. noitresnI fo na dilavni drac . siht( si ylbaborp eht ”tsetrohs“ metsys )daerht

2. noitresnI fo a ,drac dilav dewollof yb eerht deliaf NIP yrtne stpmetta .

3. noitresnI fo a dilav ,drac a NIP tcerroc rtne y ,tpmetta dewollof yb a ecnalab yriuqni .

4. noitresnI fo a dilav ,drac a NIP tcerroc yrtne ,tpmetta dewollof yb a tisoped .

5. noitresnI fo a dilav ,drac a NIP tcerroc yrtne ,tpmetta dewollof yb a lawardhtiw .

6. noitresnI fo a dilav ,drac a tcerroc NIP yrtne ,tpmetta dewollof yb na tpmetta ot wardhtiw rom e hsac naht eht tnuocca nalab ce.

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.in We can al os di e itn fy mos e in et rg a noit el vel t rh ae .sd oG b ca k ot ht e LDP desc tpir i sno fo Va il tad e NIP a dn G te NIP . Va dil ate NIP ac sll eG t NIP , a dn Ge NIPt wa sti f ro Ke Sy e rosn to re op rt

hw en a key si uot ched. If a tigid is t uo ched, NIPteG ce oh es an “X” ot the d si p al y scr ee n, tub if t eh canc le key si uot ched, Get NIP et r im nates, a dn V ila tad e NIP c no is de sr htona er NIP e tn ry a mett .tp We c dluo hsup ts lli rewol , a dn c disno er ke sy t kor e se uq e cn es us ch as t ow ro t rh ee di ig st fol wol ed yb ac nc le ke sy t .ekor

4.4.2 Be oivah r la sthgisnI Here is a rp ag tam ci , e lpx tici di ts inc noit ht ta sah krow ed well in i irtsudn la ppa il ca snoit . knihT a tuob a s tsy em in te smr of i st rop t dnuob a yr , ihw ch si the col ta noi of tsys em level i stupn a dn uo t up t .s

vE re y s sy et m has a trop dnuob a ;yr ht e rop t bo dnu a yr of ht e MTAS s tsy em ni lc edu s the gid it ke py ad, the f nu c noit snottub , ht e sc er en, the de isop t a dn rdhtiw awal srood , ht e ca dr a dn er ec pi t slots, a dn os no . aE ch of ht ese dev eci s can be hguoht t of as a “ ,”trop dna events co c ru ta s etsy m

rop ts. hT e trop i tupn a dn tuptuo events a er v isi ble to the c motsu er, a dn eht c su to em r ve yr o tf ne dnu e tsr a dn s s tsy em beha roiv ni et smr of es neuq ec s of trop eve stn . viG en siht , we am dn ate t tah tsys me trop events are ht e “ imirp it ves” of a tsys em t tse c esa , ht at i ,s a tsys me te ts case ro(

e uq ivale tn ly, a tsys me rht ae )d si e rpx e ss ed as an tni erleaved se uq ence fo trop i upn t a dn trop tuptuo eve tn s. hT is fits ruo dnu e tsr a dn i gn fo a test esac , in ihw ch we eps c yfi rp e-c dno ti snoi , i stupn ,

stuptuo , a dn op ts -c idno t snoi . W ti h iht s ma dn ate we ac n alwa sy rec zingo e a el v le vio ital no : if a test case rht( ea )d ever re riuq es an in tup ro( an uo t )tup ht at si ton v isi lb e at ht e trop dnuob a yr , eht test case ca onn t be a ys ts me el vel te ts case (t rh ea )d . itoN ec that this si c el ar, er c zingo a elb , a dn enf ro cea lb e. eW lliw re enif siht ni hC a tp er 41 hw en ew disc ssu rht ae sd fo tsys me behavi ro . I etn gra it on Te nits g

Cra pstf e snosr are rec zingo ed yb wt o e ss e itn al cha ar cteris cit :s they have a d ee p lwonk e gd e of t eh t oo sl of ht ie r tra ed , a dn they have a is lim ar onk w el gd e of ht e em uid m ni ihw ch ht ey krow , os ht at they dnu e tsr a dn eht ir sloot ni et r sm of woh ht ey “ krow ” htiw ht e medi mu . nI Pa str II a dn III , we foc su ed no the oot ls t( ce uqinh e )s ava li ab el ot the et gnits cr fa pst e .nosr ruO og al there was to

dnu e tsr a dn te its gn et c uqinh es in terms of th ie r a vd a tn ages a dn l mi i at snoit htiw r se ep tc ot pa tr ic lu ar t py se of af u tl s. Here we ihs ft ruo em hp asis ot ht e me muid , htiw ht e og al th ta a bet et r dnu e tsr a dn i gn

fo t eh medi mu w li l im vorp e t eh et gnits cra pstf e s’nosr j gdu em .tn 4.5 A C resol L oo k ta eht AS TM Sy mets we described the TAS M ys mets ni et smr of its tuptuo sc er ens ugiF( er 4. )7 , ht e te nimr al it fles F( i rug e 4. )8 , ti s c tno ext a dn pa itr la atad fl wo rugiF( es 4.9 a dn 4. )01 , an e tn ti y/r itale no s pih m ledo fo ti s da at F( i rug e 4. ,)11 f tini e ats te am c nih es desc bir i gn mos e of its beha roiv F( i rug es 4. 21 a dn 4.1 ,)3

a dn a part ai l f cnu it lano d ce sopmo i noit rugiF( e )41.4 . We osla deve pol ed a LDP desc tpir i no fo the am ni rgorp am a dn t ow ,stinu V ila date NIP a dn eG t NIP .

eW begin here yb e dnapx i gn the f nu c noit la ed c noitisopmo t tah was ts ar et d in Fi rug e 4. ;21 the

bmun e gnir cs heme rp e vres es ht e leve sl of ht e c nopmo e stn ni t tah if rug e. roF eisae r referenc ,e ea hc

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.in com nop ent taht a pp ae sr ni ruo an la y is s is given a new rohs( et )r bmun e ;r ht e es un mbe sr are vig en in Tab el 1. hT( e ylno r ae nos f ro this si ot am ke ht e rugif es a dn rps ea ehsd et rom e readable.) If uoy kool c sol le y at the inu st that a er de ngis ated yb l te et sr , uoy es e that ht ey a er ap ckagi gn leve sl ni t eh d ce ;noitisopmo ht ey are never c la el d as corp e rud es. hT e ed com isop it no ni Ta lb e 1 is ip c rut ed as a d ce noitisopmo tr ee ni rugiF e 31 .1. hT is d ce om isop it no si ht e ba sis f ro the usu la v ei w of i tn e rg a noit et ts i gn . It si ropmi at tn to rem me ber ht ta us ch a dec tisopmo i no is rp ima ylir a p ca ka gnig par it noit fo

the s sy tem. As sof wt a er desi ng vom es otni om re detail, the a dd ed ni f mro ta noi l ste su er f ni e t eh f tcnu i ano l d ce om op is noit rt ee into a nu it ca ll gni rg a hp . hT e nu it lac gnil rg a hp si ht e rid ected rg a hp in ihw ch don se are gorp ram stinu a dn e gd se c dnopserro to rp ogr ma ac sll ; ht at si , if inu t A ca sll tinu B, there si a erid cted e gd e f mor don e A to don e B. We began the deve pol em tn of ht e ca ll rg a hp f ro the MTAS sys et m ni Chap et r 21 hw en we e nimax ed the c la sl made yb ht e m ia n rgorp am a dn t eh Va il tad e NIP a dn Ge NIPt m udo el s. hT at inf ro am ti no si capt ru ed in ht e adja ec ncy tam xir given be wol in Ta elb 2. hT is am rt ix aw s created w ti h a s erp ads eh te ; this snrut uo t ot be a ha ydn loot f ro et ts ers .

lbaT e 1 MTAS stinU dna detaiverbbA semaN tinU muN eb r

leveL rebmuN tinU emaN

1 1 MTAS metsyS

A .1 1 eciveD esneS & lortnoC

D 1.1.1 rooD esneS & lortnoC

2 1.1 .1.1 teG rooD sutatS

3 1.1 .1.2 lortnoC Door

4 1.1 .1.3 esnepsiD hsaC

E 1.1.2 tolS esneS & lortnoC

5 1.1 .2.1 tolSdraChctaW

6 1.1 .2.2 teG tisopeD tolS sutatS

7 1.1 .2.3 lortnoC draC relloR

8 1.1 .2.3 ortnoC l epolevnE Ro rell

9 1.1 .2.5 daeR draC pirtS

10 .1 2 lartneC naB k C mmo .

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.in 11 1.2.1 teG NIP rof NAP

12 1.2.2 teG tnuoccA sutatS

13 1.2.3 tsoP yliaD asnarT c snoit

B .1 3 lanimreT esneS & oC lortn

14 1.3.1 neercS revirD

15 1.3.2 yeK rosneS

C .1 4 eganaM Se iss on

16 1.4.1 etadilaV draC

17 1.4.2 etadilaV NIP

18 4.1 .2.1 G NIPte

F 1.4.3 esolC noisseS

19 4.1 .3.1 weN asnarT c noit tseuqeR

20 4.1 .3.2 tnirP eR c tpie

21 4.1 .3.3 tsoP noitcasnarT oL c la

22 1.4.4 eganaM asnarT c it no

23 4.1 .4.1 teG noitcasnarT epyT

24 4.1 .4.2 teG tnuoccA epyT

25 4.1 .4.3 tropeR nalaB ce

26 4.1 .4.4 ssecorP De op tis

27 4.1 .4.5 ssecorP dhtiW rawal

hT e TAS M ca ll rg a hp si nwohs ni Fi rug e 4.5.2 moS e fo the ih erarc yh si sbo c ru ed to re cud e t eh c no f noisu ni ht e rd a gniw . nO e gniht uohs ld be uq eti vbo i uo s: rd a sgniw of ac ll rg a shp od on t s ac le pu well. htoB ht e rd a sgniw a dn the ad aj cency ma xirt rp o div e i hgisn st to the et ts er. doN es htiw gih h

de rg ee iw ll eb mi trop a tn ot i tn e rg ta noi te ,gnits dna pat sh f mor eht m ia n rgorp ma don( e )1 to t eh si kn don es ac n eb su ed to di e itn fy c no et stn fo sdliub f ro an increme tn al deve mpol e .tn

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.in 4.5.2 D oitisopmoce n B desa In oitarget n

tsoM te koobtx sid cu iss sno of in et rg a noit tes it gn ylno c no is der in et rg a noit te gnits based no t eh f tcnu i ano l ed c noitisopmo of eht tsys em b ie gn te ts ed. hT ese a orpp a hc es are a ll based no eht f tcnu i ano l ed com op is ti no , e rpx e ss ed ei ht er as a t er e giF( ure 4.5. )1 ro in textual f mro . hT e es d csi snoissu ine tiv ably c tne er no ht e dro er in ihw ch udom les are to be ni et rg ated. hT ere are f ruo c oh ice :s f mor t eh pot of ht e ert e wnwod a dr pot( )nwod , f or m the ob t ot m fo t eh ert e wpu a dr tob( tom

)pu , os em combina noit of these s( a iwdn c )h , ro mo ts rg a hp aci yll , non e of these ht( e gib ba )gn . A ll of these tni e rg ati no dro e sr rp se mu e th ta eht stinu have b ee n separate yl te ts ed, t suh eht og al fo d ce noitisopmo based int rge a it no is ot te ts t eh i tn erf ca es a gnom separate yl te ts ed nu i .st

ugiF re 4.5.1 MTAS nuF itc no al eD com isop it no rT ee

lbaT e 2 ycnecajdA xirtaM of r eht MTAS llaC hparG

ugiF re 4.5.2 MTAS Call rG a hp

eW ac n psid e sn e w ti h eht big bang a orpp ca h m so t ae si yl : ni siht iv ew of tni e rg a noit , lla the stinu a er com lip ed t go te her a dn te ts ed at no c .e ehT bward ack ot siht si aht t hw en on( t fi )! a fai rul e si

sbo e vr ed, there are few cl seu to h le p i los ate ht e ol c (noita s) of the fa tlu . (Recall ht e nitsid tc noi ew am ed in hC ap et r 1 b te ew en fa lu ts a dn fa li ru es.)

4.5.2 T po - oD wn tnI egr ta ion

poT - nwod in et rg a noit be snig w ti h the m ia n gorp mar ht( e oor t of the ert e). ynA l wo er level tinu ht at si cal el d yb ht e ma ni rgorp am a pp ae sr as a “ ts ”bu , hw ere ts sbu are ip eces of t worh -away c do e aht t

e lum eta a ac lled tinu . If we perf mro ed t po - nwod in et rg a noit te ts i gn f ro the MTAS tsys me , the rif ts s et p uow ld be ot deve pol sbuts f ro la l the stinu c la el d yb ht e ma ni rgorp a :m aW tc Ch a olSdr t, C lortno

aC dr loR el r, Sc er en virD er, Va dil ate Ca dr , V la tadi e NIP , aM na eg rT an tcas noi , a dn eN w

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.in

rT a asn c noit Re uq e ts . neG er lla y, te ts e sr have to ved e pol the ts sbu , a dn mos e mi a nig a noit si re uq ire .d He er a er owt exam lp es fo .sbuts orP ec d eru Get fNIP orP NA P( A ,N Expe detc PIN) ST BU FI NAP = ' 211 '3 T NEH IP N := 8' 678 ;' FI NAP = ' 321 '4 NEHT IP N := 8' 567 ;' FI NAP = ' 478 '6 HT NE IP N := 1' 352 ;' dnE ,

orP dec eru yeK neS ros eK( iHy )t S BUT tad :a SyeK ort sek ATS KC FO ' 8 ' . ' 8 ' , ' 7 ' , ' c ecna l ' yeK tiH = P PO eK( rtSy eko )s dnE ,

nI ht e st bu f ro G te NIP f NAPro , the te ts er re ilp cates a ta lb e kool - pu w ti h ju ts a few va ul se t tah will

a epp ar ni te ts cases. nI ht e ts bu f ro Ke Sy e rosn , the te ts er tsum de siv e a uqes e cn e of rop t eve stn aht t can co c ru cno e each t mi e ht e Ke Sy ens ro orp ce rud e si ac ll ed. H( e er , ew divorp ed the ke kortsy es ot par it ylla en et r the NIP ‘8 ’678 , tub the su er hit ht e ac nc le ub t not eb f ro e the f ruo th dig ti .) nI rp act ,eci the eff tro ot deve pol buts s is usua yll uq ite ngis if ci a tn . hT ere si doog r ae nos to c disno er ts bu c edo sa pa tr fo eht os f wt a er deve pol em ,tn a dn m atnia in ti dnu er c no fi rug ati no am nag eme .tn

nO ec la l the sbuts f ro MTAS ma ni have b ee n divorp ed, we test ht e ma ni rp o rg ma as if it were a s at dn -a enol tinu . We c uo ld a ylpp a yn of eht a irporpp a et f nu cti no al a dn urts c rut al t ce uqinh e ,s a dn l koo f ro af stlu . When ew are c nivno ec d taht ht e m ia n rgorp am lo ig c si c ro r ce t, we rg a ud a yll replace st sbu iw th t eh a tc ual c edo . vE en t sih ac n eb lborp e am it .c W uo ld we repl ca e la l eht st sbu at no ce? fI we did, we dluow have a “ ms all ba gn ” f ro nu i st iw th a hgih dtuo e rg ee. fI we re lp ace no e st bu at a

mit e, we ret tse ht e ma ni rp rgo am no ec f ro each lper aced buts . sihT em a sn ht at, f ro the MTAS m nia rgorp am exa lpm e he er , we uow ld rep tae sti tni e rg ta noi et ts ie thg mit es no( ec f ro each re lp aced ts ,bu

a dn ecno wi ht la l t eh .)sbuts 4.5.2 B motto - pu tnI egr oita n Bo tt om- pu i tn e rg a noit is a rorrim“ i am g ”e ot ht e pot - nwod dro er, iw th ht e fid ef re cn e ht ta sbuts a er repla ec d yb rd iver om d lu se that em lu ate stinu at the ne tx level pu in the eert . nI ob tt om- pu in et rg a noit , we ts a tr htiw ht e l ae ves of ht e d ce om sop ti noi tr ee nu( i st il ke noC t or rooDl a dn

psiD e esn Cas )h , a dn test ht me wi ht eps cia yll c dedo vird e sr . hT ere si baborp ly el ss t worh -away c do e in rd ive sr than there si in sbuts . R ce a ll we had no e buts f ro each c dlih don e ni the d ce tisopmo i no tree. M tso tsys me s have a fa ri ly hgih fan- uo t en ar at ht e el av se , os ni ht e tob tom- pu in et rg a noit

dro er, we t’now have as ma yn vird e sr . hT si si par llait y fo fset yb the fa tc that the vird er udom el s will eb erom compl ci ate .d

4.5.3 dnaS w ci h tnI egr noita Sa wdn ci h i tn e rg ati no si a c nibmo ta noi of t po -d nwo a dn ob tt om- pu in et grati no . If we kniht a uob t it in te smr of the ed c sopmo i noit t eer , we are r ae lly tsuj gniod big ba gn tni eg ar noit no a s bu -t eer . hT e er

liw l be le ss st bu a dn vird er deve pol em tn fe f tro , tub th si iw ll be fo fset to mos e e tx e tn yb the a edd d

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.in diff ci ytlu of af lu t si lo a noit th ta si a c sno e uq e cn e of gib ba gn in et rg a noit . W( e c dluo borp a ylb d csi ssu ht e zis e of a sa iwdn ch, f or m da ytni f gni er sa iwdn ches ot Da doowg - ts y el sa iwdn ches, tub

ton won .) 4.6 C lla arG ph B esa d oitargetnI n

nO e fo the rd a bw ca sk of d ce opmo is noit based tni e rg ati no si ht at eht b isa s is t eh f nu cti no al d ce noitisopmo t eer . fI we su e the c lla rg a hp i tsn ae ,d we mi git ate this fed ci ienc ;y ew sla o m vo e ni the dir ce noit of beha roiv al test gni . We are in a noitisop ot enj yo ht e i vn e ts me tn we made ni t eh d csi noissu of rg a hp ht e yro . Sin ec ht e c lla rg a hp si a rid ected rg a ,hp yhw ton su e it ht e way we su ed

rgorp am rg a shp ? sihT el a sd su to t ow new a orpp ca hes ot tni e rg a noit test :gni we ll’ er fer to them sa pa ri -wi es tni e rg a noit a dn n ie doohrobhg tni e rg ita .no 4.6.1 iaP r-w esi tnI egr ta i no

hT e edi a be dnih riap -wise tni e rg ita no si ot el imi nate the ts bu / vird er deve mpol ent fe f tro . aR ht er naht deve pol ts sbu a dn / ro rd ivers, yhw on t use ht e tca ual c edo ? tA tsrif , siht sdnuos kil e gib bang in et rg a noit , tub we re ts ir tc a se noiss ot ju ts a pa ri of stinu ni ht e ca ll rg a .hp hT e e dn re tlus si ht ta ew have no e tni e rg a it no t se t se noiss f ro each e gd e ni ht e ca ll rg a hp 04( f ro the MTAS c lla rg a hp ni Figure 4. )2 . hT is si ton much of a re tcud i no in se noiss s f or m either pot - nwod ro ob tt om- pu ( 24 se ,)snoiss tub it si a rd astic re ud c noit ni st bu / vird er deve mpol e .tn 4.6.2 eN i obhg r dooh nI tegr noita

eW ac n let the m ta h me ita cs ca yrr su its ll f htru er yb ob r iwor ng ht e on it no of a “ doohrobhgien ” f or m t golopo y. sihT( si t’n t oo much of a ts retch — rg a hp the yro si a rb anch of t ygolopo .) eW

ni( f mro all )y def ni e ht e n ie doohrobhg of a don e ni a rg a hp ot eb the set of don es that are no e e egd away f mor ht e given edon . nI a erid etc d rg a hp , siht aem ns a ll the mmi e id ate rp e ced e ross don es a dn a ll ht e i mm e id ate s cu ec ss ro don es on( ti ec ht at t eh es c rro e dnops ot the s te of st sbu a dn rd ive sr of ht e

don e). hT e e el ven n ie doohrobhg s f ro ht e TAS M exam lp e b( a es d no ht e ca ll rg a hp in Fi rug e 4. )2 a er given ni Table .3

lbaT e 3 MTAS sdoohrobhgieN edoN

rossecederP s seccuS so sr

16 1 ,9 ,01 21

17 1 ,11 41 , 81

18 17 ,41 51

19 1 ,41 51

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.in 23 22 ,41 51

24 22 ,41 51

26 22 ,41 ,51 ,6 ,8 ,2 3

27 22 ,41 ,51 ,2 ,3 ,4 31

25 22 51

22 1 ,32 ,42 ,62 72 , 52

1 /n a ,5 ,7 ,2 ,12 ,61 ,71 91 , 22

We can a wl ays com up te the bmun er of n ie doohrobhg s f ro a vig en ca ll rg a .hp hT e er wi ll be eno ne doohrobhgi f ro cae h in et ri ro edon , plus no e e rtx a ni ac se there are leaf don es c nno ce ted erid c ylt to eht toor .edon nA( roiretni edon has a non -ze or i dn e rg ee a dn a non -ze or dtuo e rg ee.) eW ha ev

I etn roir on sed = n edo s - os( ecru on sed + knis on )sed eN hgi rob ooh sd = ni iret ro n edo s + os cru e on sed

ihw ch c enibmo ot

ieN bhg ohro sdo = n edo s -s kni on sed

Nei doohrobhg in et rg a noit iy e sdl a rd a ts ic re noitcud in ht e bmun er of i tn e rg a it no te ts se snoiss nwod( to 11 f or m )04 , a dn it a sdiov buts a dn vird er dev le po em tn . hT e e dn re tlus is ht ta ne sdoohrobhgi a er e ss e tn i ylla ht e sa iwdn c seh ht ta we ls ppi ed past in the rp evi suo sec it no . hT( ere is a ls i thg id ffere ,ecn beca su e the base inf amro t noi f ro nei sdoohrobhg si the call rg a hp , ton ht e ed com sop iti no rt ee.) W tah they hs are iw th sa hciwdn tni e rg ati no si rom e ngis fi i ac :tn nei doohrobhg in et rg a noit t se ti gn has ht e af ult losi a noit fid ficul it es fo m“ edi mu ba ”gn i tn e rg a .noit

4.7 taP h B desa In oitarget n

uM ch of ht e rgorp e ss in the deve mpol ent of ma ht e tam i sc c mo es f or m an ele gant p ta te nr : have a c el ar i ed a of hw ere uoy wa tn ot og , a dn then fed ine ht e c no ec stp that take uoy ht ere. We od t sih he er f ro pa ht b esa d ni et rg ta noi te ,gnits tub fi tsr ew n ee d ot tom iva et t eh ed f ini it .sno

eW a erl a yd wonk ht at the c bmo i an t noi of urts c rut al a dn f nu c noit al te ts i gn is hgih ly desirab el at t eh inu t el v ;le ti dluow be in ce ot have a s mi li ar ca ilibap ty f ro tni e rg ta noi (a dn sys met ) test gni . We a osl wonk ht ta we want ot e rpx e ss sys met et ts gni ni et rms of beha roiv la rht e sda . Las lt y, ew revise ruo

og la f ro in et rg ati no et st :gni r ta her ht an te ts i tn e fr ac se ma gno es parate yl deve pol ed a dn te ts ed nu i ,st we foc su no i tn erac it no s am gno these nu i st . (“ oC -f gninoitcnu ” m hgi t be a doog et rm.) tnI erfa ec s a er st ur ct ru a ;l tni e ar cti no is behavi ro a .l

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.in When a nu it e ex c tu es, mos e ap th of s ruo ec ts a met ents is trave sr ed. soppuS e that ht ere si a ca ll ot a on ther tinu a gnol us ch a pa :ht ta that op i tn , c no t lor si passed f mor the c la li gn inu t ot the ac l el d ,tinu

hw ere mos e other p ta h of cruos e ts atemen st is t var e sr ed. eW cleverly ngi ored t sih utis a it no in Part III , beca su e this is a eb tter place ot a rdd e ss ht e uq tse noi . hT ere are t ow ssop ib li iti e :s aba nodn t eh

si lgn e-e yrtn , gnis el e tix rp ece tp a dn ert at us ch c lla s as an ex ti f lo l wo ed yb an e tn ry, ro “ rppus e ”ss the c lla ats et ment beca su e c ortno l eve lautn ly re snrut to ht e ca ll i gn inu t a wyn ay. hT e s rppu e ss i no c oh i ec skrow w le l f ro tinu testi ,gn tub it is a itn t eh ti ac l to tni e rg a noit et .gnits We can fina yll am ke ht e defin ti noi s f ro pa ht b esa d i tn e rg at noi te .gnits ruO og al si to have na in et rg a noit te gnits a golan fo DD -Pa .sht Def tini i no

nA MM - taP h si an i tn e lr ae ved se uq ence fo m eludo ex ce noitu ap t sh a dn m sse age .s

hT e basic edi a of an MM -P ta h si that ew can won desc bir e se uq e cn es of ludom e e ex c noitu shtap that ni lc du e rt a fsn e sr of c no t lor am gno separate stinu . niS ec ht ese rt a srefsn are yb me ass ges, MM- Pa sht a wl a sy re rp ese tn ef a lbis e ex ce ut noi p ta sh , dna ht ese paths c ssor nu it dnuob ar ei s. We can f dni

MM -P ta sh in an e tx e dn de rgorp ma rg a hp ni hw i hc don es are udom el exe noituc p ta sh a dn e gd es a er em ss ag se . hT e opyh the it ac l exa lpm e ni Fi rug e 7.4 .3 swohs an MM -Pa ht ht( e da kr nil e) in ihw ch

m eludo A ca sll m eludo ,B ihw ch in nrut cal sl udom le .C

ugiF re 4. .7 .3 MM -Path Acr sso eerhT nU i st nI m ludo e A, don se 1 dna 5 are s cruo e don es, a dn don se 4 a dn 6 are si kn don es. S mi li a ylr in

m ludo e B, don es 1 a dn 3 are ruos ce don es, a dn don es 2 a dn 4 are si kn sedon . udoM el C sah a is elgn ruos ce ,edon ,1 a dn a gnis el knis ,edon .4 hT e er a er seven m eludo e ex c tu i no pa sht in rugiF e 4.7. :3

)1,A(PEM = ,1< ,2 >5,3

)2,A(PEM = ,1< ,2 >4

)3,A(PEM = ,5< >6

)1,B(PEM = ,1< >2

)1,B(PEM = ,3< >4

)1,C(PEM = ,1< ,2 ,4 >5

)2,C(PEM = ,1< ,3 ,4 >5

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eW can won de nif e an ni et rg ta noi tes it gn ana gol of ht e DD -P ta h rg a hp ht ta se vr se nu ti te ts i gn os e ff ec vit le y. Def tini i no

viG en a tes of nu i ,st the ri MM - aP th hparg si ht e directed rg a hp in ihw ch don es are m eludo e ex cut noi pa sht and e egd s c rro e dnops ot m se sages a dn r te snru f mor eno tinu to a hton e .r

itoN ce aht t MM -Pa ht rg a shp a er ed fined w ti h r se ep tc to a s te of stinu . hT is erid c ylt stroppus com op is noit of stinu a dn c isopmo it no based i tn e rg ati no te ts i gn . We can even c sopmo e nwod to t eh el vel fo dni ivi ud al m eludo ex ce noitu ap t ,sh tub taht si borp a ylb erom deta deli ht an cen e ss a .yr

ugiF re 4.7.4 MM -Pa ht rG a hp De vir ed f mor rugiF e 4.7.4 Figure 4.7.4 swohs the MM -Pa ht rg a hp f ro the exa lpm e ni rugiF e 4.7. .3 hT e os l di a sworr idni c eta

em ss ag ;se the c rro e dnops i gn re snrut are cidni ated yb tod et d a .sworr eW uohs ld c disno er eht re al spihsnoit ma gno udom el exec tu i no p ta sh , rgorp ma pa ht , DD -Pa sht , dna MM -P ta sh . A rgorp ma pa ht si a se uq en ec of DD -P ta sh , a dn an MM -Pa ht is a se uq e cn e of om lud e e ex cu it on pat .sh

nU f nutro ate yl , there si on lpmis e re al it pihsno be ewt en DD -Pa sht a dn m ludo e e ex c tu i no pa .sht iE ther m hgi t be c tno a ni ed in ht e othe ,r ub t rom e lik le y, they part lai ly vo e lr ap. cniS e MM -Pa sht

mi p el ment a f nu c noit ht ta tra csn e sdn tinu b dnuo a ir es, we od have no e re al it no ship: c disno er t eh “ tni e esr c noit ” of an MM -Pa ht htiw a tinu . ehT ludom e e ex cu it no pa sht in us ch an tni e esr c noit a er an an la go of a ls ice iw th r se ep ct to the MM( -Pat )h f nu c noit . S at ted htona er way, the m eludo e ex cut noi p ta sh in us ch an i tn e esr cti no are the irtser ct noi fo t eh f nu cti no to eht nu ti ni ihw ch ht ey o cc ru .

hT e MM -Pa ht d fe noitini nee sd mos e rp a tc ical diug ile nes. woH l gno si an MM -P ta h? htoN i gn in t eh defin ti noi stibihorp an MM -Pa ht ot c vo er an ritne e TA M es ss noi . ihT( s e rtx eme l so es ht e f tsero beca su e of the t er es.) hT ere are erht e sbo e vr a lb e beha roiv al c tir e ir a that tup e opdn i stn no MM - Pa ht s. hT e f tsri si “eve tn iuq esce cn e”, ihw ch co c ru s hw en a s tsy em si n( ea )ylr di le, aw iti gn f ro a

rop t i upn t eve tn ot rt i gg er f htru er orp ec ss i gn . hT e MTAS s sy et m e ibihx st eve tn uq ies ec cn e in several pla ec :s no e si ht e ti thg l poo ta ht e be gninnig of TAS M M ia n hw ere the s tsy em has lpsid ayed the ew cl mo e sc er en a dn si wa gniti f ro a ca dr ot be e tn ered otni t eh ca dr ols t. vE e tn iuq e cs e cn e si a

tsys me el v le porp erty; there si an an la go ta t eh tni e rg ati no level: m se sage uq ei s ec nce. M se sa eg iuq es ec nce co c sru hw en a tinu that se sdn on m se sages is er ca hed l( i ek udom le C in rugiF e 31 . .)3

hT ere si a its ll ltbus er f ro :m data uq ei s ec n ec . hT si o cc sru hw en a se uq ence of corp e ss i gn c nimlu a set

in ht e crea noit of ts ro ed data ht at si on t mmi e id a et ly su ed. nI ht e Vali ad et Ca dr nu it, the acc nuo t bala cn e si bo at ni ed, tub ti si ton u es d nu lit af et r a succe ss f lu NIP ent yr . Fi rug e 31 .5 swohs woh da at

iuq es ec nce a pp ae sr ni a rt a noitid al atad fl wo id ag ar .m

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hT e f ri ts ug ideline f ro MM-Pa :sht op in st of iuq e cs ence are “ rutan al” e stniopdn f ro an MM -Pa .ht ruO es c dno ug ide enil a osl se vr es to id ts ugni si h tni e rg ati no f mor tsys em testi gn .

Def tini i no

nA atomic sy ets m nuf c noit (ASF) si an ac it no t ah t is sbo e vr a lb e at ht e ys ets m level ni ter sm of trop i tupn a dn tuptuo eve .stn

nA ta om ci tsys me f cnu t noi begins w ti h a trop upni t eve ,tn rt ave sr es eno ro rom e MM -Pa ,sht a dn et rm ni ates htiw a trop uo t tup event. When v ei wed f or m the tsys me el lev , there si on com llep i gn

rea nos to d ce om sop e an SA F otni l wo er leve sl of de at il h( en ec ht e ta omic )yti . nI the MTAS s tsy e ,m di ig t e yrtn si a doog exa lpm e of an FSA , os are ac dr ent yr , ca hs psid e gnisn , a dn se ss i no clo is .gn

NIP ent yr is borp a ylb oot bi ,g ti m hgi t eb ca ll ed a elom c lu ar ys ets m f nu cti .no

ugiF re 4.7.5 Da at iuQ es ec nce

ruO s ce dno diug le :eni a ot cim sys met f snoitcnu ra e an ppu er li im t f ro MM -Pa sht : we t’nod wa tn MM -P ta sh ot c ssor FSA dnuob ar ei s. sihT em ans ht ta sFSA re rp esent the s mae between i tn e rg a noit

a dn ys ets m et s it gn . yehT are ht e al gr e ts item ot be te ets d yb tni e rg ta noi te gnits , a dn ht e ms la le ts eti m f ro s tsy em te ts i gn . We can te ts an FSA at ob th lev le .s gA a ,ni ht e tigid e yrtn FSA is a doog

exa lpm e. ruD i gn s tsy em et ts i gn , ht e trop i upn t event is a cisyhp al key rp e ss that is d te ected yb Ke Sy ens ro a dn sent ot Get NIP as a stri gn var ai elb . itoN( ec ht at Ke Sy e rosn perf smro ht e syhp ical ot l igo lac tra sn iti no .) Ge NIPt d te e nimr es hw e ht er a dig ti key ro the ac cn el key was rp essed, a dn res sdnop acc dro i ylgn . itoN( ce that nottub rp e ss es a er rongi ed.) hT e FSA te nimr ates iw th ei ht er sc er en 2 ro 4 be gni lpsid aye .d R ta her ht an re uq i er ys ets m ke tsy kor es a dn elbisiv scr ee n d si lp ays, we c uo ld su e a rd iver to vorp ide ht e es , a dn te ts eht dig ti e yrtn ASF iv a i tn e rg ati no et ts i gn . We can es e this gnisu ruo c gniunitno exa .elpm 4.7.2 MM - shtaP dna SFSA in ht e MTAS metsyS

hT e PDL desc snoitpir deve pol ed in Cha tp er 21 are re ep ated f ro c vno e in e tn refere cn e; ts ta eme tn frag em nts a er un mbered as ew did to c urtsno ct rgorp am rg a .shp

1. iaM n P rgor ma 2. atS et = A tiaw raC d 3. SAC E S etat FO 4. awA Cti :dra S eerc rDn revi 1( , un )ll 5. W hcta raC olSd ( t draC olS tSt uta )s 6. W ELIH aC lSdr Sto utat s is I eld OD 7. W cta raCh lSd to ( raC lSd Sto utat )s 8. C rtno Clo Rdra llo re ( cca tpe )

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9. V dila eta draC C( Odra ,K NAP ) 10. IF aC Odr K HT NE tatS e = wA tia NIP 11. LE ES tnoC lor raC oRd rell e( tcej ) 12. S tat e = wA Ctia dra 13. awA Pti :NI V dila eta NIP IP( ,koN AP )N 14. IF IP koN EHT N S eerc rDn evi r ( ,5 n llu ) 15. tS eta = awA rTti sna 16. SLE E S eerc rDn evi r ( ,4 n llu ) 17. tS eta = awA aCti dr 18. awA Tti nar s: M gana rTe asna itc no 19. S etat = solC eSe iss on 20. olC Ses isse no : IF eN rTw asna itc eRno euq ts 21. T NEH atS et = wA tia arT sn 22. E ESL irP Rtn iece tp 23. P tso narT cas noit coL la 24. C sol seSe ois n 25. C tno Clor dra lloR re je( tce ) 26. S tat e = awA aCti dr 27. dnE , AC( S E S tat )e 28. NE D. aM( ni orp arg m AS )MT

29. orP dec eru laV adi Pet NI ( NIP ,ko NAP ) 30. teG NIP Prof NA AP( ,N epxE etc NIPd ) 31. yrT = sriF t 32. SAC E T yr OF 33. riF ts : S erc Dne vir re ( ,2 lun l ) 34. PteG NI nE( eret IPd )N 35. FI E etn der NIP = E cepx det NIP 36. EHT P N koNI = EURT 37. R RUTE N 38. SLE E S eerc rDn vi re ( ,3 n llu ) 39. T yr = eS dnoc 40. ceS dno : Scre Dne vir re ( ,2 lun l ) 41. PteG NI nE( eret IPd )N 42. IF E etn der NIP pxE etce IPd N 43. EHT P N koNI = EURT 44. R RUTE N 45. SLE E S eerc rDn revi ( ,3 n llu ) 46. T yr = hT dri 47. ihT dr : S erc Dne vir re ( ,2 llun ) 48. G Pte NI nE( eret IPd )N 49. FI E etn der NIP = E cepx det NIP 50. EHT P N koNI = EURT 51. R RUTE N 52. SLE E S eerc rDn revi ( ,4 n llu ) 53. P koNI = SLAF E 54. DNE , AC( SE T )yr 55. NE D. ( orP dec eru laV adi IPet )N 56. orP dec eru teG NIP E( retn Pde ,NI naC Hlec )ti 57. coL la taD :a D igi eKt sy = { ,0 1, 2, ,3 ,4 ,5 ,6 ,7 ,8 9 } 58. GEB NI 59. naC lec tiH LAF ES 60. tnE ere NIPd = lun s l nirt g 61. giD sti dvcR 0=

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62. IHW EL TON iD( tig cRs 4=dv RO naC lec )tiH OD 63. GEB NI 64. yeK eS n ros K( iHye )t 65. FI yeK tiH NI giD Kti sye 66. HT NE B IGE N 67. E etn der NIP = E retn Pde NI + eK iHy t 68. CNI MER TNE D( tigi cRs )dv 69. FI giD sti vcR 1=d EHT N cS eer irDn rev 2( , ' X- ' ) 70. FI giD sti vcR 2=d EHT N cS eer irDn rev 2( , ' XX - ' ) 71. FI giD sti vcR 3=d EHT N Sc eer irDn rev 2( , ' XXX - ' ) 72. FI giD sti vcR 4=d EHT N cS eer irDn rev 2( , ' XXX X ' ) 73. DNE 74. DNE HW{ ELI } 75. NE D. ( corP ude er teG )NIP

hT ere are 02 cruos e don es ni MTAS M ia n: 1, 5, 6, 8, 9, 01 , 21 , 41 , 51 , 71 , 81 , 91 , 02 , 12 , 22 , ,32 42 , 52 , 62 , 72 . Valida NIPet has 11 os ur ec sedon : 92 , 13 , 43 , 53 , 93 , 14 , 64 , 74 , 84 , 35 ; a dn ni

Get NIP the er a er 6 ecruos don e :s ,65 ,56 ,07 ,17 ,27 .37

TAS M aM ni c no t snia 61 si kn don es: 4, 5, 7, 8, ,9 11 , 31 , 41 , 61 , 81 , 02 , 22 , 32 , 42 , 52 , 82 . hT ere are 41 si kn don es ni dilaV ate NIP : 03 , 33 , 43 , 73 , 83 , 04 , 14 , 44 , 74 , 84 , 15 , 25 , ;55 a dn 5 knis

don es ni Get :NIP ,46 ,96 ,07 ,17 .27

tsoM of ht e ludom e e ex cut noi shtap ni TAS M Ma ni are ve yr rohs ;t siht p etta nr si ud e to ht e gih h de ytisn of em ss ages to other nu i st . Here are ht e rif ts owt m udo el e ex cut noi pa sht in TAS M Ma :ni

1< , 2, 3, >4 , >5< a dn ,6< >7 , >8< . hT e udom el xe ec tu i no pa sht ni Validate NIP are s il hg lt y longer: 92< , >03 , 13< , 23 , >33 , >43< , 53< , 63 , >73 , a dn os no . hT e be gninnig op itr no of G te NIP si a oog d

exa lpm e of a m ludo e ex ce noitu p ta h: ht e se uq en ec < 85 , 95 , 06 , 16 , ,26 36 , >46 begins w ti h a ruos ec don e )85( a dn e sdn iw th a si kn don e 46( ) ihw ch si a ac ll to the Ke tiHy orp ec erud . sihT si

a osl a tniop fo “eve tn iuq e cs enc ,”e hw e er gnihton w li l ha pp en nu lit eht c otsu em r uot ches a ke .y

hT ere are f ruo MM-Pa ht s in ts atements 46 t hguorh :27 each begi sn htiw Ke Sy e rosn sbo e ivr ng a rop t tupni eve tn (a ke sy rt ko e) a dn e sdn htiw a cl so e yl nk it fa im yl of trop tuptuo eve stn (the ca ll s ot

Scr ee rDn iver wi ht id f ref e tn NIP ce oh se ). We c uo ld name these f ruo MM -Pa sht Ge Dt ig ti ,1 Get 2tigiD , Ge Dt ig ti 3, a dn Ge giDt i 4t . hT ey are hgils tly id f ef rent beca su e ht e al ter no se inc dul e t eh ea ilr er FI st ta eme .stn fI( t eh et ets r was eht de ngis er, this m ludo e thgim be re krow ed os taht t eh W ELIH pool repeated a gnis el MM -P ta h.) T ce inh c ylla , each of these is a osl an a imot c s tsy em f tcnu i no nis ce ht ey begin a dn e dn htiw trop event .s

hT ere are i tn ere gnits ASFs ni Va dil ate NIP . hT si nu it c ortno ls a ll sc er en lpsid a sy relevant to the IP N e yrtn orp sec s. tI be snig htiw the lpsid ay of sc neer 2 ihw( ch a sks the c motsu er to e tn er sih /her PI .)N Ne tx , Ge NIPt si ca ell ,d dna the ys ets m si eve tn uq ei sce tn tnu li a ke sy t kor e o cc sru . hT e es ke kortsy es in ti ai te t eh Ge Dt ig ti sFSA we j tsu id cs ssu ed. reH e we f dni a c ru i suo rgetni a noit fa tlu . oN tice that sc er en 2 is di lps ayed in owt lp aces: yb the NEHT clau es s ni the W ELIH l poo in Ge NIPt a dn yb t eh f tsri ts atemen st in ae ch C ESA clau es ni Va dil ate NIP . We c dluo fix t sih yb r me ovi gn the sc er en d si p al sy f mor Ge NIPt a dn ylpmis r te gninru t eh rts i gn e( . ,.g X‘ — )’ ot eb lpsid aye .d

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UNIT 5 SY METS TESTING, NI RET CA TION TSET I GN

Of ht e erht e el vels of t tse i gn , ht e ys ets m el vel si c sol e ts to eve dyr ay e px er ei nce. eW test ma yn thi :sgn a su ed car bef ero we yub ti , an no - il ne en t krow se ivr ce bef ro e we sbus c ebir , a dn os no . A com nom pa tt e nr ni ht e es fa im il ar f smro si ht ta we eva ul ate a udorp tc ni terms of ruo e epx cta ;snoit

ton wi ht resp tce to a s ep cifica noit ro a ts a dn a dr . snoC e uq ent yl , ht e og la si on t to f dni af lu ts, tub to d me no ts ar te perf ro am ecn . Beca su e of t sih , ew te dn ot a aorpp ch sys t me et s it gn f mor a f nu itc ano l s at opdn int ra ht er than f or m a curts t ru al eno . niS ec it si so i utn ti vi le y f ma li iar, sys et m et s gnit ni rp ca cit e te sdn to be le ss f ro m la ht an ti m hgi t eb , a dn ht is is com dednuop yb ht e re cud ed et gnits

in et vr al ht ta usu lla y rema sni eb f ro e a d le vi e yr d ae enild . We be nig w ti h f ru ther ela rob ati no no t eh t rh ead c cno e tp , hgilhgih t gni s mo e of the arp tc ical borp el ms of rht ead-based tsys em et ts i gn . niS ce

tsys me testi gn si cl so e yl c puo el d w ti h re meriuq en st ps e fic ic ta noi , ew liw l sid cu ss woh to f ni d t rh ea sd ni c nommo on t ta i sno . llA of th si el a sd ot an dro e ylr erht ad-ba des s sy et m te ts i gn ts rate yg that e iolpx st the s sisoibmy be wt ee n f nu itc no al a dn urts c rut al et ts i ;gn we w li l a ylpp ht e rts ate yg to

ruo TAS M tsys e .m 5.1 aerhT ds

rhT ae sd a er ha dr ot ed f eni , in af tc os me bup il hs ed defin ti snoi are c retnuo - udorp c it ve, mi ls ae di ,gn a dn / ro gnorw . It s’ op ss i lb e ot s ylpmi rt eat rht e sda as a timirp ive c cno e tp ihw ch en eds on f ro m la defin ti oi n. roF won we liw l esu exa lpm es ot deve pol a “shared vi ”nois . Here are several iv e sw of a t rh ea :d

• a oiranecs fo lamron egasu

• a metsys level tset esac

• a esnopser/sulumits riap

• roivaheb taht stluser morf a ecneuqes fo metsys level stupni

• na devaelretni ecneuqes fo trop tupni dna tuptuo stneve

• a ecneuqes fo snoitisnart ni a etats enihcam noitpircsed fo eht metsys

• na devaelretni ecneuqes fo tcejbo segassem dna htem do snoitucexe

• a ecneuqes fo enihcam snoitcurtsni

• a ecneuqes fo ecruos snoitcurtsni

• a ecneuqes fo cimota metsys snoitcnuf

rhT ae sd have nitsid ct lev le s. A nu it level rht ae d is usefu ll y dnu e dootsr as na exec noitu - it me p ta h fo ruos ec in ts ur cti sno , ro la te nr a vit le y as a p ta h of DD -Paths. nA in et rg ta noi lev le t erh ad si a se uq e ecn

of MM -Paths ht at melpmi ents an ta imo c tsys em f nu c noit . We m hgi t al os eps ak of an i tn e rg ta oi n

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.in el v le erht ad as an a tl e nr ati gn es uq e cn e of udom el e ex c snoitu a dn m gasse se . fI we c unitno e siht

pa tt e ,nr a tsys me lev le rht ead is a es uq ence of a imot c ys ets m f nu cti sno . Be ac su e a cimot sys t me f tcnu i sno have trop e stnev as t rieh stupni a dn uo t up t ,s ht e se uq ence fo a mot ic sy ts em f tcnu i sno mi pl sei an tni e elr aved es uq ence of trop i tupn dna uptuo t eve stn . hT e dne re lus t si ht at erht a sd

divorp e a nu fi gniy v ei w of ruo t erh e leve sl of tes it gn . tinU te ts i gn te sts dni ivi ud al f nu cti sno , in et rg a noit te ts i gn exa nim se tni er ca snoit a om gn nu ti s, a dn s tsy em te gnits exa nim es tni e ar c snoit a gnom ta omic ys ets m f nu c snoit . nI th si cha tp er, we foc su no s tsy em le ev l t rh ea sd a dn we a wsn er

emos f dnu ame tn al uq est :snoi woH gib is a erht ad? Whe er od ew f dni them? woH od ew te ts ht em? 5.1.1 T rh e da ibissoP il it es Def gnini the e stniopdn of a tsys me el v le t rh ae d si a l ltti e a wkw a dr . We tom ivate a ydit , rg a hp the yro based ed fin iti no yb krow ing b ca wk a sdr morf hw e er we wa tn ot og w ti h t rh ae .sd Here a er t rh ee ac didn a et t rh ae ds:

• tigid a fo yrtnE

• E yrtn fo a lanosreP noitacifitnedI rebmuN )NIP(

• A elpmis :noitcasnart MTA draC ,yrtnE NIP ,yrtne tceles noitcasnart epyt ,tisoped( ,)wardhtiw tneserp tnuocca sliated gnikcehc( ro ,sgnivas ,)tnuoma tcudnoc eht ,noitarepo dna troper eht

luser st .

• nA TA M ,noisses gniniatnoc owt ro erom elpmis snoitcasnart .

tigiD e yrtn si a doog exa lpm e of a m ni imal ta imo c s etsy m f nu c noit t ah t si im lp emen et d htiw a si lgn e MM -Path. It be snig w ti h a trop tupni event ht( e tigid ke kortsy e) a dn e sdn w ti h a rop t tuptuo event (the scr ee n tigid )ohce , os ti uq a il fies sa a st mi ul r/su e snops e riap . sihT level of rg a un al ytir is t oo f ni e f ro ht e soprup es of tsys em et gnits . We saw siht ot be an a irporpp ate level f ro in et rg a noit et ts i gn .

hT e s ce dno ca didn a ,et NIP yrtnE , si a doog exa lpm e of an ppu er li im t to tni e rg ita no te ,gnits a dn ta

the s ema t emi , a ts ar it gn op int of sys tem testi gn . NIP tnE ry si a doog exa lpm e of an ta mo ci s etsy m f tcnu i no . It si a osl a go do ex ma lp e of a fa ylim of s mit ulu /s r se snop e pa ri s (s sy et m el v le beha roiv that si itini ated yb a rop t upni t eve tn , rt aver es s os em rgorp mma ed gol i ,c a dn et nimr ates in no e fo several lbissop e re snops es [ rop t tuptuo even ]st .) sA we saw in Cha tp er 31 , NIP yrtnE en liat s a se uq e ecn fo tsys em level stupni a dn uptuo t :s

1. A neercs gnitseuqer NIP stigid

2. nA devaelretni ecneuqes tigid fo sekortsyek dna neercs sesnopser

3. ehT ytilibissop fo noitallecnac yb remotsuc eht erofeb eht lluf NIP si deretne

4. A metsys remotsuc A( :noitisopsid sah eerht c secnah ot retne eht c erro ct NIP . a ecnO c erro ct NIP sah neeb ,deretne eht resu sees a neercs gnitseuqer eht noitcasnart ;epyt esiwrehto a neercs

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sesivda eht remotsuc taht eht MTA drac lliw ton eb ,denruter dna on a ssecc ot MTA snoitcnuf is ).dedivorp

5.1.2 T rh e da De oitinif ns Def tini i no A tinu rht e da si a p ta h ni t eh rgorp am rg a hp fo a .tinu

hT ere are t ow el v sle of rht ae sd su ed in i tn e rg a noit te gnits : MM -Pa sht a dn a mot ci tsys me f nu cti .sno hT e de nif iti no s f mor hC ap et r 31 are re ep ated here os ht e c ho ere cn e ac ssor the leve sl si erom

evident. Reca ll t tah MM-Paths a er ed f ni ed as p ta sh ni the di er cted rg a hp ni ihw ch ludom e e ex c noitu pa sht a er don e ,s a dn e gd es wohs ex ce noitu it em se uq enc .e Def tini i no

nA MM - taP h si a pa ht in eht MM -Path rg a hp fo a es t fo inu t .s Def tini i no

viG en a sy ets m d fe ni ed in te smr of ta mo ci s etsy m f nu cti sno , ht e SA F hparG of ht e tsys me si the directed rg a hp ni ihw ch don es a er a cimot ys ets m nuf tc snoi a dn e gd es re serp e tn se uq e tn ai l lf .wo Def ni ti i no A ruos ce SA F is an ta imo c ys ets m f nu c noit taht a epp a sr as a cruos e don e in the FSA rg a hp of a

tsys me ; limis arly, a knis SA F is an ta imo c ys ts me f nu c noit t tah a epp ars as a knis don e in the FSA rg aph.

nI the MTAS s sy tem, ht e Ca dr nE t yr A FS si a s ruo ce FSA , a dn ht e se noiss ter im noitan FSA si a

si kn FSA . oN tice ht ta i tn e mr e id a yr sFSA c dluo ven er be t se ted at t eh s sy tem level yb ht me se vl se — they en ed t eh rp edece ross FSA s to “get ht e .”er Def tini i no A s etsy m rht e da si a ap th f mor a ruos ce FSA to a si kn FSA in eht FSA rg a hp fo a ys ets .m Def tini i no

viG en a s sy et m ed if ned ni et r sm of ys ets m rht ae sd , ht e hT er da hparG of ht e sys tem si the direct de rg a hp ni ihw ch don es are ys ets m t erh a sd a dn e gd es re rp ese tn se uq e itn al e ex c noitu of vidni i laud

t rh eads .

sihT set fo ed f snoitini vorp ides a c ho ere tn s te of ni c er a gnis ly orb ader iv e sw of t rh ae ,sd ts ar it gn w ti h t rh ea sd iw t nih a nu it a dn e gnidn wi ht tni e ar c snoit ma gno tsys me lev le rht ae sd . We can su e the es iv e sw sa ht e oc lu ar no a im c sor c epo , s tiw chi gn v ei ws ot get ot id ffe ner t el v sle of rg na u al ri .yt

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.in Ha gniv these c no ec stp si ylno part of the borp el m, troppus i gn them si a hton er. We next take a et ts e s’r iv ew fo re meriuq ents ps ce if ci ita no ot see woh to di e itn fy erht a .sd

5.2 B sisa stpecnoC f ro R emeriuqe n st icepS f oitaci n Reca ll ht e noiton of a bas si of a ev c rot s ap ce: a es t of i dn epe dn e tn ele em stn f or m ihw ch la l t eh e el em stn in ht e aps ec can be gene ar et d. R ta her ht an a tn ci pi ate lla eht var ai ti sno ni cs sero fo re riuq ements eps cif ci ita no me sdoht , ton ita ,sno a dn et c uqinh es, we w li l sid c ssu s tsy em tes it gn htiw res ep ct ot a basis set of re meriuq ents s ep c fi i ac it no c curtsno t :s atad , act snoi , strop , eve stn , a dn t rh eads. vE e yr s tsy em ac n be e sserpx ed in te smr of ht ese f vi e f dnu a tnem al c cno e stp (a dn eve yr re riuq ements sp ce if ci a noit cet uqinh e is os em c nibmo a it no of th ese ). eW ex ma ni e ht ese f adnu em tn al c cno epts he er to ees woh ht ey troppus t eh et ets s’r orp ec ss fo erht ad di e itn fi ac .noit 5.2.1 ataD When a s tsy em si desc bir ed in et rms of ti s da at , ht e foc su si no ht e inf mro a noit su ed a dn c er ated yb the ys ets m. We desc bir e da at ni te smr of var ai selb , data st ur tc ru es, fie sdl , r ce sdro , data st sero , a dn f li es. r/ytitnE ela noit ship dom els a er t eh tsom c mo nom c cioh e ta ht e hi hg est el ve ,l a dn some f mro fo a re ug lar e rpx e iss no e( . .g , aJ c nosk d ai rg a sm ro d ata st ur c rut e d ai rg ma )s is u es d at a rom e deta li ed el v le . ehT data-ce tn ered v ei w is a osl the s at gnitr niop t f ro sever la f sroval of bo cej t- iro en et d

an la isy s. Data fer ers ot inf ro am noit t ah t si either itini a zil ed, ts ro ed, dpu ate ,d ro ssop( ibl )y de ts roye .d nI ht e MTAS s tsy em, itini al data describe the vari uo s acc stnuo )sNAP( a dn the ri sNIP , a dn each

acc nuo t has a data rutcurts e iw th ni f mro ita no us ch as the acc tnuo bala ecn . sA MTA rt a asn c snoit o cc ru , the re us lts are kept as cre ta ed data a dn su ed ni the da yli gnitsop of nimret al d ata to ht e ec ntral bank. roF am yn s tsy me ,s the data ce tn ered v ei w od m ni ates. hT ese sys et sm are tfo en dev le po ed ni et rms of DURC ca ti sno rC( ae et , Re irt e ev , dpU a et , De tel e). We c dluo desc bir e the tra asn c noit

rop t noi fo eht MTAS ys ets m in siht wa ,y tub it ’ndluow t krow w le l f ro eht su er interf eca trop i .no

oS em it m se erht a sd can be di entified dire yltc f or m eht d ta a dom el. eR la spihsnoit ewteb en da at e ititn es can be no e- ot - eno , no e- ot -ma yn , ma yn - ot - no e, ro ma yn -to-ma ;yn these nitsid itc no s a ll ha ev mi pl ci a noit s f ro rht ea sd that orp ce ss the ad ta. roF exa elpm , if ba kn c motsu e sr can have seve ar l

acc nuo ts, ae ch acc nuo t iw ll n ee d a uqinu e IP N. If several pe po el can ca c sse ht e same a cc tnuo , yeht liw l en ed MTA ca sdr w ti h ide citn al .sNAP We can a sl o f dni ini ti la data us( ch sa ,NAP pxE ce te NIPd p ia )sr t tah are r ae d tub never nettirw . uS ch r ae d- ylno data tsum be part of the s etsy m

in ti ai il zati no corp e ss . If ,ton ht ere m tsu be erht a sd ht at create us ch da at . R ae d- ylno data si ht e er f ero an i idn ac rot fo ecruos sFSA . 5.2.2 Ac snoit Ac it no -ce tn ered dom ile gn si by far the som t co mm no re riuq ements eps cif ci ati no f mro . sihT si a h tsi ciro al uo t htworg of ht e a tc noi - ec tn ered na rut e of pmi er ta vi e rgorp a imm gn la ugn ages. Ac snoit have i tupn s a dn uo t ,stup a dn these ac n be e ti her data ro rop t events. He er are mos e me ygolodoht - ps e ic fic s smynony f ro tca snoi : rt a sn f mro , data snart f mro , c ortno l tra sn f mro , corp e ss , a itc v ti y, ta ,ks em doht , a dn se vr ci e. Ac snoit can a osl be d ce om sop ed into l wo er lev le ca ti no s, sa we saw wi ht t eh

da at f wol id a rg sma in Cha tp er 21 . hT e i uptuo/tupn t v ei w of ca ti sno si exactly ht e ba sis of f nu cti lano

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.in et ts i ,gn a dn the decom isop it no (a dn eventual i pm el me )noitatn of ac it sno is the ba is s of urts tc aru l et ts i gn .

5.2.3 oP r st

vE re y s tsy em has rop ts (a dn trop dev eci )s ; the es are the cruos es a dn de nits ta noi s of s sy tem le lev i stupn a dn uptuo st rop( t event )s . hT e ls i thg sid it nc noit b te ew en rop st a dn trop de iv sec si os m te i sem he pl f lu ot et ts e sr . T ce inh ac lly, a trop si t eh op i tn ta ihw ch an O/I de iv ec si a att ched ot a sys et m, as in se ir al a dn para ll el strop , net krow ,strop a dn t ele nohp e rop t .s yhP acis l ac snoit k( e sy t kor se a dn il hg t me snoissi f mor a sc er e )n o cc ru no trop de iv ec s, a dn these are t snar al ted f or m yhp acis l ot

l igo lac ro( gol aci l ot yhp is c la .) nI t eh a sb e cn e of ac ut la trop de civ es, hcum of s sy et m test gni ac n eb accompl hsi ed yb “m ivo ng ht e trop dnuob ra y ni wa dr ” to ht e l cigo la tsni ances of trop eve stn . orF m

won no , we wi ll j tsu su e ht e et rm “ rop ”t ot er f re to rop t de iv ec s. hT e strop ni ht e MTAS s tsy me inc dul e ht e gid it a dn cancel ke sy , ht e f nu c noit syek , the id sp al y cs er en, the de sop ti a dn rdhtiw awal

rood s, ht e ca dr a dn re tpiec stols , a dn es ver la less vbo i uo s dev eci s, us ch sa the lor el rs th ta m vo e ca sdr a dn de tisop e vn le po es otni ht e am c ,enih ht e ca hs psid e sn er, the rece tpi rp i tn er, a dn os .no

gniknihT a tuob the rop ts he pl s ht e te ts er ed if ne ob th ht e i tupn space ht at nuf cti no al ys ets m et gnits n ee ;sd a dn si lim a ylr , the tuptuo dev seci vorp ide tuptuo -based f noitcnu la et st inf ro m noita . oF( r exa ,elpm ew dluow l eki ot have e hguon erht a sd ot gener ta e a ll 51 MTAS sc er e sn .) 5.2.4 Eve tn s

vE e stn are mos e hw at schi rhpoz en :ci ht ey have some cha ar etc itsir cs of da at a dn os em of acti sno . nA eve tn si a ys ets m level upni t ro( uo t )tup ht at oc sruc ta a trop . kiL e da at , even st can be i upn st to ro

stuptuo of a tc i sno . vE e stn ac n be sid crete us( ch as MTAS ke kortsy e )s or they can be c tno i suoun us( ch as t me pera erut , edutitla , ro rp e russ e). Di rcs ete eve stn cen e ss a ylir have a ti em rud a noit , a dn

this can be a c citir al fac rot ni real- mit e ys ets sm . We mi thg p ci rut e upni t ve e tn s as dest ur itc ve er ad- tuo da ,at tub s’ti a rts etch ot mi agi en uo t tup eve stn as de urts c evit etirw po erati .sno

vE e stn are kil e ac snoit ni the se sn e ht at ht ey a er ht e rt a lsn a noit tniop be wt ee n er al- row ld syhp ci al

eve stn a dn in et nr al gol ci al am fin e ts ta snoi of ht se e. troP tupni events are yhp acis l-to- gol ic la tran ls snoita , a dn ys mm irte c ylla , trop tuptuo e nev ts are gol ic la -to- isyhp cal rt a lsn a it .sno tsyS me et ts e sr dluohs foc su no ht e isyhp cal dis e of eve stn , on t the gol ical dis e ht( e foc su of i tn e rg a noit et ts e )sr . hT ere are s ti ua noit s hw ere ht e c tno e tx fo rp ese tn data v la ues c gnah es the igol ac l em a gnin

of isyhp cal eve stn . nI eht MTAS s sy et m, f ro e elpmax , the rop t tupni eve tn of de rp e gniss ub t not 1B aem sn “Bala ecn ” hw en sc er en 5 si eb i gn d si p al yed, c“ h ce ki gn ” hw en cs er en 6 si be gni di lps aye ,d

a dn “yes” hw en sc er e sn 01 , 11 , a dn 41 are be gni di lps ayed. We refer to us ch autis it no s sa c“ no et tx se tisn i ev trop eve ,”stn a dn ew dluow e px ce t ot tset us ch events ni e ca h c tno e .tx 5.2.5 T rh e sda

nU f nutro ate yl f ro set te sr , erht a sd are the el a ts fre uq e tn ly su ed of ht e if ve f dnu ament la co tsn ur tc .s Sin ec we test rht ae sd , it usu lla y fa sll ot the et ts er to fi dn ht me ni ht e ni et r ca ti no s ma gno the da ,at eve stn , a dn ac .snoit tuobA t eh no ly alp ce aht t rht ae sd a pp ear per es ni a re riuq ements eps cifi ac oit n si hw en ra dip pytotorp ing si su ed ni c no j nu cti no w ti h a s ec na oir er c dro er. It s’ sae y to f dni rht ae sd ni

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.in c no t lor dom e ,sl as we w li l noos ees . hT e elborp m iw th siht si t tah c lortno dom sle are tsuj ht at — they a er dom e ,sl ton t eh r ae li yt fo a tsys e .m 5.2.6 eR l pihsnoita s A gnom B sisa noC ec stp Figure 5.1 si an e r/ytitn e pihsnoital m do el of ruo ba sis c no ce stp . toN ice that all relat noi ships a er

am yn - ot - am yn : Data a dn vE e stn are genera zil ed otni an e tn ti y; ht e t ow rela spihsnoit ot the noitcA e ytitn are f ro stupni a dn uo t up st . hT e s ma e eve tn ac n occ ru no seve ar l trop s, a dn t py laci yl ma yn eve stn co c ru no a si gn el trop . Finally, an ca ti no ac n co c ru in several rht ae sd , a dn a t erh ad si com op sed of several ac snoit . hT si d ai rg ma de rtsnom a et s so em of the iffid cul yt of tsys me et s it .gn Teste sr tsum su e even st a dn t erh ads ot ens ru e that all the ma yn -to-ma yn r ale ti spihsno ma gno t eh f evi ba sis c no ec pts a er c rro ce .t

ugiF re 5.1 E/R doM el fo Ba sis C no ec stp 5.2.7 M do eli gn w hti eht sisaB noC ce stp

llA fla rov s of re uq i er m stne eps cificati no deve pol m do e sl of a s tsy em ni t re ms of ht e b isa s c cno e .stp Figure 5.2 wohs s eerht f dnu ame tn la f smro fo re riuq emen st eps cifica it no dom els: st ur ct ru a ,l c no et utx al, a dn behavi ro al . St ur c rut al dom els are u es d f ro deve mpol e tn ; ht e es e rpx sse ht e f cnu it lano d ce noitisopmo a dn data d ce om sop ti noi , a dn the ni et fr ac se ma gno c nopmo e stn . C tno extual dom e sl are tfo en the start gni tniop of st rutcur la dom ile gn . hT ey me hp a is ze s etsy m strop a dn , ot a le ss er e tx e tn , a tc snoi , a dn rht e sda very dni irec lt y. hT e dom e sl of beha roiv ( la os called c lortno dom e )sl a er

hw ere f ruo of the five sisab c sno t ur c st co em got e ht er. eS le itc no of an a rporpp etai c ortno l m do el si the e ss en ec of requireme stn s ep cific :noita m edo ls that are oot ew ak tonnac e rpx e ss tropmi a tn

tsys me beha roiv s, hw ile dom sle that a er oot wop erful t ipy cally sbo c ru e i tn ere gnits beha roiv s. sA a general ur le, ed c noisi at lb es are a doog c oh ice ylno f ro com up at noit la etsys sm , if nite ts ate machines are doog f ro me un - rd iven tsys me s, a dn P te ir ne st are eht ledom of c ecioh f ro c no c rru e tn sys tem .s Here we su e f tini e ts ate cam hines f ro ht e MTAS sys et m, a dn ni Cha tp er 61 , we liw l su e Pet ir n ste ot an la ezy t erh ad tni e ar cti .no

ugiF re 5.2 gniledoM eR ital spihsno gnomA aB is s C urtsno c st We tsum make an tropmi a tn di nits itc no b te w ee n a sys et m itself er( a il )yt a dn m do els of a tsys e .m C sno ider a sys et m ni hw ci h mos e f nu c noit F ac tonn co c ru itnu l t ow rp e uqer tisi e eve stn 1E a dn 2E have o cc rru ed, a dn taht ht ey ac n co c ru ni tie her dro er. We c dluo u es ht e on it no of eve tn part gninoiti to dom el ht is s ti ua .noit ehT re tlus dluow eb a dia rg am l eki t tah in rugiF e .3.5

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ugiF re 5.3 vE ent Pa tr i noit i gn V ei w fo nuF cti no F nI the eve tn pa gninoititr v ei w, eve stn 1E a dn 2E o cc ru f mor ht eir re eps tc vi e ex et nr al enti it se . W neh

they o cc ru , they are dleh ni ht e ri re ps ce vit e eve tn rots es. nA( eve tn rots e stca kil e a dest ur itc ve re da po erat noi .) When htob ve e stn have co c rru ed, f nu c noit F ge st sti rp ere tisiuq e inf mro a noit f or m the

eve tn rots es. oN t ci e we ca tonn tell f or m ht e ledom ihw ch eve tn o cc sru f ;tsri we ylno wonk that ob th m su t o cc ru . We c uo ld a osl m edo l ht e tsys em as a if in te ts ate m ca hi en F( i rug e 5. ,)4 ni ihw ch ts ta es rec dro ihw ch eve tn has o cc rru e .d hT e st eta hcam ine v ei w e px li ltic y swohs ht e owt dro e sr of the eve tn s .

ugiF re 5.4 MSF f ro nuF tc noi F Both of these m do e sl e rpx e ss the same rp ere isiuq et s f ro the f nu c noit F, a dn ne hti er is the er a ytil fo the s tsy em. Of these owt m do e sl , the ts ate m ca nih e si rom e su fe lu ot the te ts er, beca su e pa sht a er in ats yltn c vno er it elb to rht ea .sd 5.3 iF n gnid T sdaerh

hT e fin eti tats e am c nih e m do e sl of ht e MTAS tsys me are the best alp ce ot l koo f ro sys et m t gnitse t rh eads. We ll’ ts a tr w ti h a ih erarc yh fo ts ta e ma nihc e ;s t eh ppu er lev le is nwohs in Fi rug e 5. .5 tA this el vel, ts ates c rro se dnop to s at ges of corp essi gn , a dn transi it no s are ac su ed yb l igo lac r( a reht than rop t) eve stn . hT e Ca dr nE t yr “ ts at ”e f ro e ax m elp , uow ld be ed c sopmo ed otni l wo er leve sl t tah d ae l wi ht detai sl kil e maj em d ca sdr , ac sdr t tah are spu ide- ,nwod uts ck ca dr or lle sr , a dn ch ce gnik ht e ca dr aga tsni the l tsi of ac sdr f ro ihw ch se vr ice si offered. cnO e the ted a li s of a am c or - ts ate a er et ts e ,d ew esu an ea ys t rh ae d ot get ot t eh ne tx am c or - ats .et

ugiF re 5.5 poT Level MTAS Sta et aM c enih

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hT e NIP nE try s at te si d ce opmo es d into ht e m ro e detailed iv ew in Fi ug re 5.6. hT e a jd ace tn ats t se are nwohs beca su e they are ruos c se a dn de ts in ta noi s of tra sn i snoit f mor the NIP nE try rop it no . tA this level, we foc su no ht e NIP r rte y mecha sin m; all of the tuptuo eve stn a er t ur e trop eve stn , tub t eh i tupn events a er ts lli l go aci l event .s hT e ts ates a dn e gd es are bmun ered f ro referen ec later hw en ew d csi ssu te ts c vo erage. oT ts a tr t eh t erh ad di e tn fi ci a noit orp ec ,ss ew tsrif l tsi the rop t even st nwohs no ht e ts ate tra isn it sno ; ht ey a epp ar in Ta lb e 1. eW piks ped ht e eje tc drac eve tn beca su e ti t’nsi

rea yll pa tr fo eht NIP yrtnE c nopmo e .tn

lbaT e 1 stnevE ni eht NIP yrtnE etiniF etatS enihcaM troP tupnI tnevE s

troP tuptuO stnevE

etamitigeL draC yalpsiD neercs 1

norW g draC yalpsiD neercs 2

erroC ct IP N yalpsiD neercs 3

nI c erro ct NIP yalpsiD neercs 4

elecnaC d yalpsiD neercs 5

itoN ce ht ta rroC e tc NIP a dn nI c erro ct NIP are re ylla c dnuopmo trop i tupn events. We ca t’n a tc yllau e tn er an e ritn e NIP , we en et r di ig ts, a dn at a yn op int, we im thg ih t the ac ncel key. hT ese erom de at eli d ssop ib li eiti s a er s nwoh in Fi rug e 5.7. A rt uly para dion te ts er m thgi d ce om op es ht e gid it

rop t tupni eve tn into t eh tca ual c cioh es 0( - rp e dess , 1- rp e ss ed, …,9- rp e )dess , ub t siht dluohs have b ee n te ts ed at a l wo er leve .l ehT trop events ni t eh NIP Try f etini ats te am c enih a er ni Table .2

giF ure 5.6 NIP yrtnE Fin ti e tS a et M ca enih

hT e “x” ni ht e ts ate man es ni the NIP Try am hc ine er fe sr ot ihw ch t yr (f tsri , es c dno , ro thi )dr si pa gniss hguorht t eh ma .enihc

lbaT e 2 troP stnevE ni NIP eht yrT etiniF etatS enihcaM troP tupnI stnevE

troP tuptuO stnevE

gid it ohce X‘ --- ’

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lecnac ohce XX‘ -- ’

ohce XXX‘ -’

ohce ‘ XXXX ’

nI a dd ti noi ot the t ur e trop eve stn in ht e NIP Try f tini e ts ate m ca enih , there are erht e gol ical tuptuo

eve stn (C rro e tc NIP , nI c rro ce t NIP , a dn Ca cn ele )d ; ht e es c rro e dnops ex ca ylt to eht hgih er le lev eve stn in giF ure 5.6. ehT ih erarc yh of f tini e ts eta m ca nih es m lu t ilpi es ht e bmun er of erht a sd . hT e er are 651 tsid in tc paths f mro the Fi sr t P NI rT y ts eta to ht e wA tia rT a sn a noitc C ioh ce ro aC dr yrtnE s at tes ni Fi rug e 5.6. Of ht e es , 13 c rro es dnop ot ve entua yll c rro ce t NIP ent ir es 1( no ht e f ri ts yrt , 5 no the es c dno t yr , a dn 52 no ht e driht ;)yrt the o ht er 521 pa sht c dnopserro ot soht e htiw inc erro ct

di ig st ro wi ht can ec l key ts kor es. sihT si a fa ylri t ipy lac ratio. hT e upni t trop i no of sys te sm , e eps cially tni e ar c vit e tsys me ,s usua yll has a al gr e bmun er fo t erh a sd ot ed al w ti h tupni e rorr s a dn e cx ept noi s. It is “ doog f mro ” to er ach a state m nihca e in ihw ch tra isn it no s are ac su ed yb ac ut la trop i tupn eve stn , a dn ht e ac snoit no tra sn ti snoi are rop t uptuo t events. If we have us ch a tinif e st eta

cam enih , genera gnit sys tem t tse cas se f ro ht ese rht ae sd si a em cha cin la orp ec ss — j tsu f wollo a pa ht of rt a tisn i sno , a dn on et ht e trop stupni a dn stuptuo as ht ey o cc ru a gnol the p ta h. hT si in et elr aved se uq e cn e is perf mro ed yb the tset e ex cut ro p( e nosr ro rp o rg ma ). Tab el s 3 a dn 4 fol wol owt pa sht t hguorh eht ih erarc cih ats te machine .s

ugiF re 5.7 NIP yrT niF ti e tS a et M ca enih Table 3 c rro e sdnops to a rht ae d ni hw ci h a NIP si c rro e yltc en et red no the fi sr t t yr . Ta lb e 4 c rro e sdnops to a rht ead ni hw ci h a NIP is inc erro tc yl e tn ered no eht fi tsr yrt , ac cn e sl after the thi dr di ig t no eht es c dno t yr , dna gets it thgir no t eh driht yrt . oT ma ek eht te ts c sa e e cilpx it, we a ss mu e a rp e-c tidno i no that t eh e px ected NIP si .’4321‘

lbaT e 3 troP tnevE ecneuqeS rof tcerroC NIP no tsriF yrT troP tupnI tnevE

troP tuptuO tnevE

neercS 2 deyalpsid w hti ‘ ---- ’

1 desserp

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neercS 2 deyalpsid w hti ‘X --- ’

2 desserp

neercS 2 deyalpsid w hti ‘ XX -- ’

3 desserp

neercS 2 deyalpsid w hti ‘ XXX -’

4 desserp

neercS 2 deyalpsid w hti ‘ XXXX ’

tcerroC( )NIP neercS 5 deyalpsid

hT e event in pare htn eses in eht l tsa wor of Ta lb e 3 si the igol cal eve tn taht “ spmub pu ” ot ht e pare tn s at et mac enih a dn ca sesu a transi it no ht e er ot t eh wA ia t rT a sn ca ti no eciohC ts ta .e

lbaT e 4 troP tnevE ecneuqeS rof tcerroC NIP no drihT yrT upnI troP t tnevE

troP tuptuO tnevE

neercS 2 deyalpsid w hti ‘ --- ’

1 desserp


2 desserp


3 desserp


5 desserp


errocnI( ct IP )N neercS 3 deyalpsid

dnoces( rt y) neercS 2 deyalpsid w hti ‘ ---- ’

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.in 1 desserp


2 desserp


3 desserp


lecnac yek p desser

dne( fo dnoces )yrt neercS 3 deyalpsid

neercS 2 deyalpsid w hti ‘ ---- ’

1 desserp


2 desserp


3 desserp


4 desserp


tcerroC( )NIP neercS 5 deyalpsid

If uoy kool lc so le y at baT el s 3 a dn 4, uoy w li l es e taht ht e ob tt om driht fo Tab el 4 si e ax tc yl Ta elb 3; suht a rht ae d ac n eb a sbus et fo a hton er t erh a .d 5.4 rtS u larutc S seigetart f ro hT aer d T gnitse While genera gnit rht ae d te ts cases si sae y, ed ic di gn hw ci h no es ot ca ut la yl su e si rom e c lpmo ex. I( f

uoy have an aut mo a it c et ts e ex cut ro , th si si ton a lborp em.) We have ht e same pa ht e noisolpx borp el m ta ht e sys et m lev le aht t we had at the tinu level. uJ ts as we did ht e er , we ac n su e eht

directed rg a hp hgisni st to ma ek an i tn e gill e tn c ioh ce fo t erh a sd to et .ts

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.in 5.4.1 B motto - pu hT er da s When we gro a zin e s at te m ca nih es ni a ih erarc ,yh we ac n krow f or m ht e mottob pu . hT ere a er xis pa sht ni the NIP Try st eta am c enih . If we trave sr e these xis , we t tse f ro t rh ee thi :sgn c rro ce t rec ngo ti noi and ec oh of tne ered gid i st , re snops e ot the ca cn el ke sy t ekor , a dn matc gnih e px ce ted a dn e tn ered sNIP . hT ese pa ht s are described ni Tab el 5 as se uq e cn es of ht e t snar ti noi s in Fi rug e 41 .7. A t rh ead aht t rt ave sr es the p ta h si desc bir ed in et mr s of sti i tupn key ts kor es, suht the tupni se uq ence

4321 c rro e dnops s ot the t rh ae d desc bir ed in rom e d te ia l ni Ta lb e 3 (the can ec l key ts kor e si i dn ci at de htiw a )’C‘ . nO ec th si rop it no is etset d, we ac n og pu a el v le ot ht e IP N Ent yr cam hi en , hw ere the er

are f ruo ap t sh . sehT e f ruo are c cno e nr ed wi ht the t rh ee t yr cem ha msin a dn ht e se uq e cn e of sc er e sn rp ese tn ed ot the su er. nI Tab el ,6 the pa sht in the NIP nE rt y sta et m ca hine F( i rug e 5. )6 are named sa

tran is noit se uq e cn e .s

lbaT e 5 daerhT shtaP ni eht NIP Try MSF tupnI tnevE uqeS cne e

htaP fo snarT i noit s

321 4 ,1x ,2x x3, ,4x x5

321 5 ,1x ,2x x3, ,4x x6

C ,7x 11x

C1 ,1x ,8x 11x

21 C ,1x ,2x x9, 11x

321 C ,1x ,2x ,3x ,01x 11x

lbaT e 6 daerhT shtaP ni eht NIP yrtnE MSF tupnI tnevE euqeS cn e

htaP fo snarT i noit s

321 4 1

3215321 4 ,2 3

321C5321 4 4,2 ,5

CC C 2, ,4 6

hT e es t rh ae ds were di e itn fied htiw the og al of pa ht trave sr al ni im dn . R llace f or m ruo d si cu iss no fo st ur ct ru al test gni ht at seht e og sla ac n be mi ls ae gnid . hT e a noitpmuss si ht at pa ht trave sr al nu c vo e sr af u tl s, a dn rt ave gnisr a va ir ety fo pa sht re ud ec s dnuder ancy. hT e la ts pa ht ni Ta lb e 6 rtsulli ates woh

st ur ct ru la og a sl ac n be nuoc et r- orp ud ct evi . iH tt gni the ac ncel key t rh ee mit es seod i edn ed ac su e the

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.in t rh ee t yr cem hani ms to f lia , a dn r te snru ht e tsys me to ht e Ca dr yrtnE sta et , tub ti seems il ke a degenerate t rh ae d. hT e er si a rom e seri suo lf aw htiw these t rh ae :sd we dluoc ton er a yll e ex c etu them “ yb ht e vlesm e ”s , beca su e of ht e ih erarc ih c ts ate machines. W tah really ha pp e sn htiw t eh

’5321‘ tupni es uq e cn e ni Ta lb e 5? It rt ave sr es an in et re gnits p ta h ni the NIP yrT m ca ,enih a dn ht en ti “re nrut ”s ot ht e NIP yrtnE m ca hi en hw ere it si es en as a l cigo al event (inc erro ct )NIP , hw i hc

ca su es a tra isn it no to ts ta e 2.2 S( ce dno NIP Try). If on a dd iti no al ke kortsy es co c ru , this m ca hi en uow ld rema ni ni ts eta 2. .2 We wohs woh to vo ercome such s ti ua snoit ne .tx

5.4.2 edoN dna E egd voC er ega Me irt cs Beca su e the f tini e ts ate machines are erid cted gra shp , we can su e the mas e et ts c vo erage em t scir that we a ilpp ed at the tinu level. hT e ih erarc cih re al pihsnoit m ae ns ht at the ppu er level m ca hine m su t t er at ht e l wo er hcam ine as a corp e rud e ht at si e tn ered a dn re nrut ed. A( tc ua yll , we en ed ot od this f ro no e m ro e lev le ot get to t ur e rht ae ds t tah begin iw th the aC dr nE t yr ts ta e.) hT e owt oivbo us c oh ices a er edon c vo e gar e a dn e gd e c vo era eg . Table 7 si exte dn ed f mor Table 4 to wohs ht e edon a dn e gd e c vo erage of eht rht ee - yrt rht ae d. doN e ts( ate) c vo erage si ana suogol to ts atement c vo era eg at the inu t level — ti is ht e bare muminim . nI t eh IP N tnE ry ex ma elp , we ac n ta ta ni don e c vo erage

tuohtiw ever e ex c gnitu a t rh ae d htiw a c erro ct NIP . If uoy ex ima ne Ta elb 8, uoy w li l s ee that t ow t rh ea sd tini( i ta ed yb C 4321 a dn 321 C1C )C1 t var e sr e all the states in ob th am c nih es. gdE e s( ta et tran is )noit c vo erage si a m ro e acce tp ab el ts a dn a dr . If the ts ate am c ih nes are “we ll f mro e ”d

rt( a tisn i sno ni et rms of rop t eve )stn , e gd e c vo erage osla ug ara tn ees rop t event c vo erage. hT e erht a sd in Ta lb e 9 were p ci ked ni a urts c larut way, to ug ara etn e th ta the le ss trav le de e gd es oht( es ca su ed yb canc le ke sy t kor se ) a er t ar ve sr e .d 5.5 Fu lanoitcn S seigetart f ro T aerh d T itse ng

hT e f tini e ats et m ca hine based a orpp aches to erht ad di entific ta i no are raelc ly su fe lu , ub t hw at if on beha roiv al m do el e stsix rof a s tsy em to be et ts ed? hT e tes it gn c ar f pst e nosr has t ow c cioh e :s deve pol a beha roiv la m do el, ro r se tro ot the sys et m level a an l sgo of f nu tc noi al et its gn . ceR lla ht at hw en f tcnu i ano l et st cases are di entified, we su e ni f mro a it no f mor ht e tupni a dn uo t up t ps aces as we ll sa the f tcnu i no sti e fl . We sed c bir e f nu tc noi al rht ae sd here in te smr of c vo era eg m te ir cs that are derived f mor eerht fo t eh ba sis c no ec stp (event ,s ,strop a dn data .)

lbaT e 7 edoN dna egdE lasrevarT fo a daerhT troP tupnI tnevE

troP tuptuO vE ne t edoN s segdE

neercS 2 alpsid y de htiw ‘ ---- ’ .2 1 a

1 desserp 2.1.1

neercS 2 alpsid y de htiw ‘X -- -’ 1x

2 desserp 2.1.2

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neercS 2 alpsid y de htiw ‘ XX -- ’ 2x

3 desserp 2.1. 3

neercS 2 alpsid y de htiw ‘ XXX -’ 3x

5 desserp 2.1. 4

neercS 2 lpsid ay de htiw ‘ XXXX ’ 4x

errocnI( ct IP )N neercS 3 alpsid yed ,5.1.2 3 ,6x 2

dnoces( rt y) neercS 2 alpsid y de htiw ‘ ---- ’ 2.2

1 desserp 2.2. 1


2 desserp 2.2. 2

ercS ne 2 alpsid y de htiw ‘ XX -- ’ 2x

3 desserp 2.2. 3


nac c le p esser d 2.2.4 01x

dne( fo dn2 )yrt neercS 3 alpsid yed 2.2.6 11x

neercS 2 alpsid y de htiw ‘ ---- ’ .2 3 4

1 desserp 2.3. 1


2 desserp 2.3. 2

neercS 2 alpsid y de htiw ‘ XX --’ 2x

3 desserp 2.3. 3


4 desserp 2.3. 4

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neercS 2 alpsid y de htiw ‘ XXXX ’ 4x

tcerroC( )NIP neercS 5 alpsid yed ,5.3.2 3 ,5x 5

lbaT e 8 etatS/daerhT nI c nedi ce tupnI stnevE

2.1 .x.2 1 .x.2 2 .2 x.3 .x.2 4 .2 x 5. 6.2.2 .2 2 2.3 3 1

321 4 x x x x x x x

3215321 4 x x x x x x x x

21C 34 x x x x x x x x x

321C21C1 4 x x x x x x x x

1C1C321 C x x x x x x x x x

lbaT e 9 noitisnarT/daerhT nI c ecnedi tupnI tnevE s

x1 2x 3x 4x 5x 6x 7x 8x 9x x 01 x11 1 2 3 4 5 6

321 4 x x x x x x

3215321 4 x x x x x x x x

21C 34 x x x x x x x x x

321C21C1 4 x x x x x x x x x x x

1C1C321 C x x x x x x x x x

5.5.1 Eve tn - saB ed T aerh d Te ts i gn C sno ider ht e aps ce fo trop upni t events. hT e er a er five rop t tupni rht ae d c vo erage me irt cs of in et rest. At at ini gn the es levels fo tsys em te ts c revo age re riuq es a set fo erht a sd such ht a :t

• :1IP hcae trop tupni tneve srucco

• :2IP nommoc es secneuq fo trop tupni stneve rucco

• :3IP hcae trop tupni tneve srucco ni yreve ”tnaveler“ atad txetnoc

• :4IP rof a nevig ,txetnoc lla ”etairporppani“ tupni stneve rucco

• :5IP rof a nevig ,txetnoc lla elbissop tupni stneve rucco

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hT e 1IP m te ir c si a ba er ,muminim a dn si ina uqed a et f ro m so t s sy tems. 2IP c vo erage si t eh m tso com nom , a dn ti c rro e sdnops ot ht e intu ti vi e view of ys mets te gnits ceb au es ti ed a sl w ti h “ amron l

su ”e . It si fid fic tlu ot itnauq fy, woh ever. Wh ta si a “c mo m no ” se uq e cn e fo tupni events? What is an nu co mm no eno ?

We ac n sla o ed if en owt voc erage m rte ci s based no trop uo t tup eve :stn

• :1OP hcae trop tuptuo tneve srucco

• :2OP hcae trop tuptuo tneve srucco rof hcae esuac

1OP c vo erage si an acce tp able min mi mu . tI is pa itr cu al rly ef ef itc ve hw en a s tsy em has a ir ch va ir e yt of uo t up t m se sages f ro e rorr c tidno i sno . hT( e TAS M tsys me od es ton .) 2OP c vo erage is a

doog og la , ub t it si ha dr ot uq antif ;y we lliw revi tis this in Cha tp er 61 hw en we ex ma ni e t daerh in et ar tc noi . roF won , on te ht at 2OP c vo erage refe sr ot t rh ae sd ht ta tni e ar ct wi ht re ps e tc ot a rop t

tuptuo eve ..tn usU ally a vig en tuptuo event ylno h sa a ams ll bmun er of ac su es. nI eht MTAS tsys me , sc er en 01 mi thg be dis lp ayed f ro rht ee er a :snos the ter im nal thgim be tuo of ca hs , it may be

mi ssop i lb e to make a c nno ec noit iw th ht e ec rtn al ba kn to g te the acc nuo t ab al ecn , ro ht e rdhtiw awal rood hgim t be j ma med. nI rp ca ecit , os me fo eht mo ts id ff ci tlu af lu st f dnuo ni f lei d t lbuor e re strop

are soht e in ihw ch an uptuo t o cc sru f ro an psusnu ce ted ac esu . nO e exa lpm :e ym col al MTA s tsy em on( t ht e TAS M) has a rcs ee n that inf ro sm me taht “ ruoY da yli wi rdht awal mil ti has eb en er ca he .”d

sihT sc er en dluohs o cc ru hw en I ta te tpm to iw t rdh aw m ro e ht an 003$ ni no e day. When I see t sih sc er en, I su ed ot a muss e that ym iw fe has made a maj ro rdhtiw awal erht( ad i tn eract )noi , os I re uq e ts a l esse r ma .tnuo I f dnuo uo t ht at the TA M osla udorp ec s th si sc er en hw en the a tnuom of c hsa in t eh d si pe sn er is wol . R ta her than divorp e a tol of ca hs to ht e f ri ts su e sr , ht e centr la ba kn erp ef sr ot

divorp e el ss ca hs to erom su e .sr 5.5.2 oP rt-B sa ed Thre da eT gnits

troP -based test gni si a su ef lu c lpmo ement to e nev t-b esa d t gnitse . tiW h trop -based te its gn , we a ,ks f ro each trop , hw at eve stn ac n o cc ru ta aht t trop . eW ht en seek t erh a sd taht exerc si e upni t rop st a dn

tuptuo strop wi ht r se pe tc ot the eve tn stsil f ro e hca rop t. sihT( rp e us em s us ch eve tn stsil have eb en ps e ic fied; mos e re eriuq em tn s ps ce ifica it no et c nh i uq es am dn ate us ch lis st .) roP t-based tes it gn si

par cit lu a ylr esu f lu f ro tsys me s in ihw ch eht rop t de iv ces c mo e f mor e tx e nr al ppus eil .sr ehT am in rea nos f ro trop -based t gnitse ac n be es en ni eht e itn t r/y le pihsnoita dom el of ht e b isa s c tcurtsno s F( i rug e 41 . )1 . hT e ma yn - ot - am yn r le ta i pihsno be wt ee n strop a dn eve stn uohs ld eb exe sicr ed in htob

direc noit s. vE e tn based et ts i gn c vo e sr ht e no e- ot - am yn r itale no ship f mor events to rop ts, and c vno e sr e yl , rop t-based gnitset c vo e sr ht e no e- ot ma yn rel ta hsnoi ip f or m rop st to eve stn . hT e MTAS

tsys me f lia s su at this niop t — the er si on MTAS ve e tn ht at co c sru ta erom ht an eno .trop 5.5.3 ataD -B sa ed hT er da seT gnit

troP a dn eve tn based te gnits wo kr w le l f ro ys smets ht at are rp ami ri yl event rd iven. uS ch s sy et sm a er mos e mit es ca ll ed “reac vit ”e tsys me s ceb a esu yeht r ae ct ot its lum i trop( i tupn even st ), a dn tfo en t eh

rea itc no si in ht e f or m of trop uo t tup events. R ae tc vi e sys et sm have t ow im rop tant chara tc e itsir c :s

egaP 1 14

Vtusolution.in

Vtusolution.in

vtuso

lution

.in they are “ gnol - innur ng” (as soppo ed to ht e s troh srub t of c upmo t ta i no we s ee ni a pa ory ll rgorp a )m a dn they ma tni ia n a relati pihsno w ti h eht ir e mnorivn e tn . pyT ic lla ,y eve tn vird en, er ac it ve sys et sm

od on t have a v re y tni ere ts i gn data dom le (as we s ee iw th ht e MTAS s tsy e )m , os d ata m do el based t rh ea sd are t’n partic lu a ylr su ef lu . But tahw a uob t c vno en it no al s tsy e sm hw ci h are data vird en?

hT e es sys et sm , described as “ ts ta ci ” in [ ppoT er ]39 , are rt a sn f mro ati no al r( a ht er ht an er ca vit e); they roppus t rt a sn ca snoit no a databa es . When ht e es tsys ems a er eps cified, ht e itne t r/y e al ti pihsno m do el

si od im na tn , a dn si the er f ro e a fer it le s cruo e of tsys me t tse gni erht a sd . oT a tt ca h ruo disc noissu to mos e gniht f ma ili ar, we u es the e itn t r/y ela noit pihs dom el of a pmis le il rb a yr ys ets m es( e rugiF e 5. )8

f mor [ ppoT er .]39

ugiF re 5.8 E/R doM el fo a rbiL a yr He er a er emos t ipy ac l nart as c it sno ni t eh il rb a yr tsys e :m

1. ddA a koob ot eht yrarbil .

2. eteleD a koob morf eht rarbil y.

3. ddA a rob rewor ot eht yrarbil .

4. eteleD a reworrob morf eht yrarbil .

5. naoL a koob ot a reworrob .

6. ssecorP eht nruter fo koob a orf m a reworrob .

hT e es tra asn c snoit are all iam n il ne erht ad ;s ni fact, they re rp ese tn f ima il es of t rh ae sd . roF exa ,elpm soppus e ht e koob loan rt a asn cti no is etta mp et d f ro a worrob er sohw e c tnerru un mber fo ch ce ked tuo

skoob si ta eht le gnidn imil t a( in ce dnuob a yr va ul e ex ma p el ). We im hg t sla o t yr to re nrut a koob that was never nwo ed yb the il rb a yr . nO e mo er : s soppu e we de el et a worrob er that has emos

rnu e nrut ed skoob . lA l of ht ese are i tn ere gnits t rh ae sd ot te ts , a dn a ll are at ht e sys et m level. We can ide itn fy each of ht ese xe a lpm es, a dn ma yn rom e, yb c sol e a tt en it no ot the ni f mro ati no in t eh e r/ytitn ela pihsnoit m do el. sA we did w ti h eve tn -based te ts i gn , we desc ebir s te s of t erh ads in et smr of data-based c vo erage em irt cs. hT ese refer to er al ti spihsno f ro an tropmi a tn reas no . nI f mro a noit ni re al spihsnoit is gene ar l yl upop al ted yb tsys me vel el t rh ae sd , hw e er as ht at in ht e enti it es si us yllau ha dn el d ta ht e nu it leve .l (When en it yt /rel ita no s pih dom le gni si ht e ts a tr i gn tniop of bo j ce t- iro ent de an la ,sisy this si enf cro ed yb e cn a lusp ati no .)

• :1MD esicrexE eht ytilanidrac fo yreve pihsnoitaler .

• :2MD esicrexE eht apicitrap noit fo yreve pihsnoitaler .

• :3MD esicrexE eht lanoitcnuf seicnedneped gnoma spihsnoitaler .

egaP 1 24

Vtusolution.in

Vtusolution.in

vtuso

lution

.in

aC dr i ilan ty r efe sr of the ruof ibissop il it es of rel ta hsnoi ip ht ta we disc ssu ed ni Cha tp er 3: no e- ot - ,eno no e- ot - am yn , ma yn - ot -o en , a dn ma yn -to-ma yn . In the il rb a yr exa elpm , ob th ht e loan a dn the setirw

re al spihsnoit are ma yn - ot - am yn , m ae gnin aht t no e aut roh can etirw ma yn skoob , a dn no e koob can have ma yn a rohtu ;s a dn ht ta no e koob can be ol aned to ma yn worrob e sr ni( se uq en ec ) a dn no e

worrob er ac n worrob ma yn skoob . E ca h of these bissop i itil es re us l st ni a esu f lu tsys em te gnits t rh ead . Parti ic pa noit refe sr ot hw te her ro on t eve yr tsni a cn e of an e tn ti y pa itr c pi ates in a er al pihsnoit . nI t eh

rw eti s re al it no ship, htob the B koo a dn the uA t roh e ititn es have ma dn a yrot par it pic a noit w( e ac onn t have a koob htiw on a srohtu , ro an a rohtu of on )skoob . nI os me ledom i gn tec uqinh es, part pici a noit si e rpx e ss ed ni te smr of un em cir al il mi ;st the rohtuA en tit y, f ro ex ma ple, im thg be e rpx esse d as a“ t el a ts 1 a dn at m so t ”21 . When such inf mro ati no is av alia elb , ti lea sd cerid lt y to obvi suo dnuob ra y

v la ue s sy et m t tse t erh a .sd

oS em it m se rt a sn ca snoit d ete nimr e e px l ci ti igol c la c nno ecti sno a gnom rela hsnoit ip ;s th se e a er nwonk as f nu cti lano depe dn encies. roF exa elpm , we ca tonn ol an a koob that si on t op sse ss ed yb t eh

il rb a yr , a dn we uow ld ton delete a koob that si tuo no ol an. Al os , we uow ld on t de el et a reworrob ohw ts lli has mos e skoob c eh cked tuo . hT ese ki sdn of depe dn encies are uder ec d hw en ht e data esab

si mron a zil e ,d tub ht ey sti ll e ,tsix a dn they el ad to in et re ts i gn sys et m te ts rht ae .sd 5.6 AS TM T tse hT aer ds If we a ylpp ht e disc noissu of t sih chap et r ot ht e MTAS s sy met , we g te a set of erht a sd aht t c sno tutit es a hguoroht s tsy me level et ts . We deve pol us ch a s te of t rh ae sd here ni te mr s of an vo er la l s at te m do el in ihw ch stat se c rro e dnops ot key ta imo c s tsy em f nu cti sno . hT e am c or -level states a er :

aC dr nE t yr , NIP tnE ry, rT a sn ca noit eR uq e ts , (a dn orp ec ss )gni , a dn S se si no Manageme tn . ehT s at ted dro er is ht e t tse gni dro er, eb ac su e ht ese ts ag se are ni rp ere etisiuq dro er. (We ca tonn e tn er a

NIP nu it l us ecc ss f lu ca dr ent yr , we ac tonn re seuq t a rt a sn ca noit nu t li s cu sec sf lu NIP e yrtn , a dn os no .) We al os en ed mos e rp e-c noitidno tad a ht ta de nif e mos e a lautc acc stnuo htiw sNAP , pxE tce de NIP s, a dn acc tnuo balances. hT e es are given in Tab el 01 . owT el ss vbo i suo rp e-c dno i snoit are t tah

the MTA et animr l si i in ait lly d si p al gniy scr ee n 1 a dn ht e t lato ac hs ava ali lb e ot the rdhtiw awal d si pe sn er si 005$ ni( 01$ ton e .)s

elbaT 01 MTAS tseT ataD NAP

etcepxE d PIN gnikcehC Bal cna e sgnivaS laB na ec

001 321 4 0.0001$ 0 00.008$

002 654 7 0.001$ 0 00.09$

003 876 9 0.52$ 0 00.02$

egaP 1 34

Vtusolution.in

Vtusolution.in

vtuso

lution

.in

eW iw ll e rpx e ss t rh ae sd ni at lb es in ihw ch pa sri fo swor c rro e dnops to rop t stupni a dn e px ected trop stuptuo ta e ca h of ht e f ruo am j ro ats ges. We ats tr htiw t rh ee ba is c rht ae ,sd no e f ro each tra asn cti no

t py e b( alance uqni i yr , oped is t, a dn iw t rdh awa .)l

oitcasnarT n eS ssi no

euqeR st naM ag neme t

,1B 1B 2B

neercs ,6 neercs 41 neercs 51 , je tce

0.0001$ 0 c dra , scr ee n 1

nI t rh ead ,1 a va il d ac dr wi ht NAP = 001 is e tn ered, ihw ch ca su es sc er en 2 ot eb d si lp ayed. ehT NIP

di ig st ’4321‘ are e tn ered, a dn cnis e ht ey am tch ht e e epx cted NIP f ro eht NAP , cs reen 5 vni i gnit a transa itc no sel ce noit si di ps al yed. When nottub B1 si uot ched the fi sr t ti em r( e uq e gnits a bala ecn i )yriuqn , scr ee n 6 a gniks ihw ch ca c tnuo si id sp al yed. When B1 is rp e ess d ht e sec dno ti em (c eh c )gnik , screen 41 si lpsid ayed a dn the c eh ck gni cca tnuo balan ec 0001$( . )00 si tnirp ed no t eh rec ie pt. When B2 si hsup ed, sc er en 51 is lpsid ayed, the er ceipt si tnirp e ,d the TA M ca dr si je e tc e ,d a dn ht en sc er en 1 is di lps aye .d

rhT ae d 2 si a de tisop ot c eh cki :gn Same NAP a dn NIP , tub B2 is uot ched hw en sc er en 5 si d si p al yed, a dn 1B is hcuot ed hw en cs er en 6 is psid layed. hT e a nuom t 52 . 00 si en et red hw en sc er en 7 is d si lp ayed a dn ht en sc er en 31 si di ps al yed. hT e de isop t rood po e sn a dn the de tisop e vn le po e si p al ced ni ht e de tisop ls ot. Sc er en 41 is lpsid ayed, a dn hw en B2 si hsup ed, scr ee n 51 si di lps aye ,d the er c tpie s gniwoh the new ch ce ki gn acc nuo t bala cn e of 5201$ . 00 si rp in et d, ht e MTA ac dr si ejecte ,d a dn ht en sc er en 1 si lpsid aye .d

snarT ca t oi n

qeR tseu

,2B ,1B 00.52 tresni

ne v.

neercs ,6 neercs ,7 neercs 51 , tceje neercs ,31 ped . rood

epo ns, eercs n ,41 5201$ 0. 0

rhT ae d 3 si a wi rdht a aw l f or m as vi sgn : Aga ni the same NAP a dn NIP , tub B3 is t uo ched hw en sc er en 5 si lpsid aye ,d a dn 2B is uot ched hw en rcs ee n 6 si di lps aye .d ehT ma tnuo 03 . 00 si en et der

hw en sc er en 7 si lpsid ayed a dn then sc er en 11 si lpsid ayed. hT e wardhtiw al rood po e sn a dn t eerh 01$ on et s are di ps e sn ed. Scr ee n 41 si lpsid ayed, dna hw en B2 si up shed, sc er en 51 si d si lp ayed, t eh

egaP 1 44

NIP yrtnE

4321

daerhT 1 )ecnalab(

troP stupnI

troP stuptuO

draC yrtnE )NAP(

001

eercs n 2 neercs 5

daerhT 2 oped( s )ti

oP tr stupnI

troP stuptuO

draC yrtnE )NAP(

001

NIP yrtnE

4321

eS ssi no tnemeganaM

2B

eercs n 2 neercs 5 ,drac sc neer 1

Vtusolution.in

Vtusolution.in

vtuso

lution

.in rec ie pt s oh gniw the new sa gniv s cca tnuo bala ecn of 077$ . 00 si tnirp ed, ht e MTA ca dr si eje cte ,d a dn ht en sc er en 1 is di lps aye .d



,3B ,2B 00.03 2B

neercs 51 , tceje

c dra , scr ee n 1 ardhtiw w la rood

,snepo 3 01$ ,seton neercs ,41 00.077$

A few of ht ese d te lia ed desc noitpir s are en eded ot wohs the ttap e ;nr the rem ia ni gn rht ae ds a er desc bir ed in te smr fo i tupn a dn tuptuo events that a er eht bo cej tive fo t eh set t rht ae .d

rhT ae d 4 si the rohs et ts t rh ae d in the TAS M ys ets m, it c stsisno of na vni alid ac dr , w ih ch si emmi id ate yl er jecte .d

erhT da 4

draC yrtnE )NAP(

troP nI up st 004

Po tr Ou pt u st tceje drac neercs 1

oF ll gniwo the am c or - ts ates a gnol rht ae d 1, we txen perf mro var ai ti sno no NIP nE try. We get f ruo new erht a sd f mor Ta elb ,9 ihw ch iy e dl e egd c revo age ni eht NIP nE t yr etinif ts ate mac nih e .s



sa ni th daer 1

oitcasnarT n eS ssi no euqeR st naM ag neme t

sa ni th daer 1

eercs n 2 sneercs 3,2,5

egaP 1 54

draC yrtnE )NAP(

001

aerhT d 3 ardhtiw( law )

troP stupnI

troP stuptuO

NIP yrtnE

4321

eercs n 2 neercs 5 neercs ,6 neercs ,7 neercs 11 ,

oitcasnarT n eS ssi no euqeR st tnemeganaM

NIP yrtnE

draC yrtnE )NAP(

001

neercs 2

draC yrtnE )NAP(

001

NIP yrtnE

43215321

sneercs 3,2,5

NIP yrtnE

4321C

daerhT 5 )ecnalab(

troP stupnI

troP uptuO st

daerhT 6 )ecnalab(

troP stupnI

troP stuptuO

Vtusolution.in

Vtusolution.in

vtuso

lution

.in

daerhT 7 nalab( ec ) draC yrtnE )NAP(

troP nI up st 001

Po tr Ou pt u st neercs 2

daerhT 8 nalab( ec ) draC yrtnE )NAP(

troP nI up st 001

Po tr Ou pt u st neercs 2

ivoM ng to the rT a sn ca ti no Re uq e ts ts a eg , ht ere are var ai ti sno htiw re ps ect ot ht e t py e of rt a asn c noit b( ala nce, de tisop , ro rdhtiw a )w , ht e ca c tnuo (ch ce ki gn ro savi )sgn a dn several ht ta d ae l iw th t eh

a nuom t re uq e ts e .d rhT e sda ,1 2, a dn 3 c vo er t eh pyt e a dn a cc nuo t var ai ,snoit os ew foc su no t eh a nuom t- rd iven erht a sd . rhT ae d 9 er ej cts ht e ta tpmet ot iw t rdh aw an a nuom t on t in 01$ inc mer e ,stn

rhT ae d 01 jer ce st the ta tpmet ot rdhtiw aw m ero than ht e acc tnuo balance, dna rhT ae d 11 ejer c st t eh a tt me tp ot iw t rdh aw erom ca hs than eht psid e sn er c no t .snia

aerhT d 9

ardhtiw( law )

troP stupnI

troP stuptuO


troP stupnI

troP stuptuO


troP stupnI


sa ni th daer 1


NIP yrtnE

4321C21C1

sneercs ,2,3 3,2,5

NIP yrtnE

1C321 C1C

sneercs 3, ,2 2,3 ,4,1

draC yrtnE )NAP(

001

NIP yrtnE

4321

oitcasnarT n eS ssi no euqeR st tnemeganaM

,3B ,2B 00.51 lecnaC 2B

neercs 51 , tceje c dra , scr ee n 1

eS ssi no

naM ag neme t

eercs n 2 neercs 5 sneercs ,7,6 ,9 7

snarT ca t oi n qeR tseu

draC yrtnE )NAP(

003

NIP yrtnE

9876 ,3B ,2B 00.05 lecnaC 2B

sneercs 7,6 ,8

snarT ca t oi n tseuqeR

B3, B2, 00.015

aC cn le

neercs 51 , tceje c dra , scr ee n 1

eS ssi no

naM ag neme t

2B

egaP 1 64

eercs n 2 neercs 5

NIP yrtnE

4321

draC yrtnE )NAP(

001

Vtusolution.in

Vtusolution.in

vtuso

lution

.in

Po tr Ou pt u st eercs n 2 eercs n 5 sneercs ,7,6 01

c dra , scr ee n 1

Ha gniv exerc si ed the snart act noi orp ce gniss noitrop , we orp ceed ot the noisses management s at ,eg

hw e er ew te ts t eh tlum i elp tra sn a noitc po it .no

eS ssi no

naM ag neme t

,1B lecnaC

neercs ,51 neercs ,5 neercs 51 , tceje

c dra , scr ee n 1

tA siht op i tn , ht e erht a sd vorp ide c vo erage of a ll uo t up t sc er e sn e cx ept f ro sc er en 21 , ihw ch ni f smro the su er ht at de stisop ca onn t be corp se sed. aC gnisu this c tidno i no is melborp a it c m( a by e we uohs ld p al ce a sif h sa wdn ci h ni ht e de tisop e vn le po e sl )to . sihT si an exa lpm e of a t rh ae d se el cted yb a rp e- c dno i noit aht t si a ha wdr are af eruli . We j tsu vig e ti a t rh ae d an em he er , s’ti erht ad 31 . Ne tx , ew deve pol rht ea sd 41 hguorht 22 to exer ic se c tno ext es tisn ive i upn t eve stn . hT ey are nwohs ni Ta lb e

;11 ton i ec ht at emos fo t eh rif st 31 t erh a sd exerc esi c tno e tx se tisn i iv ty.

elbaT 11 sdaerhT rof txetnoC evitisneS tupnI stnevE erhT ad

kortsyeK e neercS lacigoL inaeM ng

6 lecnac 2 NIP yrtnE rorre

14 lecnac 5 noitcasnart noitceles rre or

15 lecnac 6 tnuocca noitceles rre or

16 lecnac 7 tnuoma eles c noit rorre

17 lecnac 8 tnuoma eles c noit rorre

18 lecnac 13 tisoped epolevne ton ydaer

1 B1 5 ba nal ce

1 B1 6 cehc king

19 B1 10 sey a( non - lawardhtiw asnart c it no )

egaP 1 74

daerhT 21 )ecnalab(

troP stupnI

troP stuptuO

draC yrtnE )NAP(

001

NIP yrtnE

4321

snarT ca t oi n tseuqeR

,1B 1B

neercs ,6 neercs 41 0001$ 0. 0

eercs n 2 neercs 5

neercs 51 , tceje

Vtusolution.in

Vtusolution.in

vtuso

lution

.in 20 B1 12 sey a( non - tisoped asnart c )noit

12 B1 14 sey rehtona( asnart c )noit

2 B2 5 de op tis

3 B2 6 s va sgni

21 B2 10 on on( lanoitidda asnart c oit n)



hT e es 22 erht ads c sirpmo e a er a nos a elb set t fo the rop it no fo eht MTAS tsys me aht t ew have ps e ic fied. Of c sruo e t reh e are nu et ts ed a ps ect ;s no e oog d ex ma p el i vlovn es the b ala nce of an

acc tnuo . C disno er t ow rht ae sd , no e that de op s ti s 04$ to an a cc tnuo , a dn a es c dno aht t w ti rdh a sw 08$ , a dn oppus es ht at ht e balan ec tbo a ni ed f mro ht e centr la ba kn at ht e aC dr nE t yr ts age is .05$

hT ere a er t ow bissop i il tie :s no e si ot esu ht e ec rtn al ba kn bala ecn , er c dro la l rt a sn ca snoit , a dn neht resolve these hw en ht e daily op ts gni occ ru s. hT e other si ot mai tn a ni a gninnur lo lac bala ecn , hw i hc si hw at dluow eb nwohs no a balance yriuqni snart a noitc . fI the ec ntral ba kn b la a cn e si su e ,d t eh

rdhtiw awal rt ansact noi si ejer cte ,d tub fi eht col al b la a ecn si su e ,d it is corp e ss e .d

onA ther nimorp e tn tnu e ts ed noitrop of t eh MTAS s tsy em si the Am tnuo yrtnE orp ssec taht co c sru in sc er ens 7 a dn 8. hT e ilibissop ty of a cancel ke sy t kor e ta a yn tniop rud i gn ma nuo t e yrtn p udor ec s a ilpitlum c yti rg ae ter naht ht at of NIP nE t yr . hT ere si a erom bus tle (a dn the er f ro e rom e tni eres it )gn et ts f ro Am tnuo yrtnE . What a tc ua yll ha pp e sn hw en ew e tn er an a tnuom ? oT be eps cif ci , esoppus

we hsiw ot en et r 04$ . .00 We e epx tc an ce oh after each di ig t key ts kor e, ub t ni ihw ch sop ti noi od es the ce oh o cc ru ? owT suoivbo :snoitulos la wa sy re riuq e xis gid i st ot be en et red (so ew uow ld e tn er

)’000400‘ or su e the gih h dro er di ig st fi sr t a dn hs i tf l tfe as cus ce viss e dig ti s are e tn ered, as nwohs in Figure 41 . 01 . soM t MTA ys ets sm u es the ihs tf a aorpp ch, a dn siht r ia ses the s tbu el op i tn : woh od es the MTA s tsy em wonk hw en a ll ma nuo t gid ti s have b ee n e tn ered? hT e MTA s tsy em c el arly ca tonn

rp e id ct ht at eht de sop ti a tnuom si 04$ . 00 inste da fo 004$ . 00 ro 000.0004$ eb ac su e there si on “e tn er” key ot si ing fy when the al ts tigid has b ee n e tn ered. hT e r ae nos f ro siht di rg e ss i no si ht ta this si a doog e ax mp el of ht e ki dn of de iat l di vocs ered yb te ets sr taht si often m gnissi f or m a

re riuq ements s ep cific noita . uS( ch deta sli dluow kil ely be f nuo d wi ht htie er aR pid pytotorP i gn ro su i gn an e ex c tu a elb eps cifi ac noit .)

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ugiF re 5. 01 tigiD Ec seoh w ti h Le tf ihS f st 5.7 metsyS T itse ng senilediuG If we disa woll c dnuopmo se iss no s rom( e than no e rt a sn a )noitc a dn if we di rs ega dr the ilpitlum c yti ud e to Am nuo t yrtnE op ssi ilib ties, ht ere are 534 dist tcni erht ads per dilav a cc nuo t in the MTAS

tsys me . Fact ro in ht e effec st of com dnuop se snoiss a dn the tnuomA nE t yr op iss bi itil es a dn there a er et sn of t suoh a sdn of lbissop e t rh ae ds f ro ht e TAS M s sy et m. eW e dn siht chap et r iw th t erh e

str eta ig es ot ed al w ti h eht rht ae d e px lo is no .melborp 5.7.1 sP e odu - tS ruc ut ral tsyS em Te ts i gn When we iduts ed inu t te gnits , we saw that the com nib ation of f nu itc no al a dn urts c larut et gnits iy e sdl a de ris a lb e c ssor - hc ce k. We can no ly ialc m sp e odu - urts c rut al t tse gni [ groJ en es n ]49 , eb ca esu

the don e a dn e gd e c vo e gar e me irt cs a er ed f ni ed ni et rms fo a c no t lor m do el of a tsys me , a dn are ton derived erid tc yl f mor ht e tsys em lpmi eme tn at noi . eR( ca ll we ts a tr ed tuo iw th a c no ec nr vo er t eh d itsi noitcn be ewt en aer il yt a dn dom le s of er al ti y.) nI general, be roivah al m do e sl are no ly a amixorpp it no s of a sys et ’m s er a ytil , hw ci h si yhw we c uo ld ed c opmo es ruo dom e sl nwod ot several el v le s of detail. If we made a urt e st cur t ru al m do el, sti size a dn lpmoc exi yt dluow make it t oo cumbers mo e ot esu . hT e gib w ae nk e ss of oduesp -st ur ct ru la me irt cs is that ht e dnu e gniylr m do el

am y be a roop c ecioh . hT e rht ee m tso c nommo beha roiv al dom le s ed( c noisi tabl se , f tini e ts a et cam nih es, a dn Pe rt i en t )s are a rporpp ai et , re ps ce vit e yl , to tra sn f mro a noit al, tni e ar c evit , a dn

c no c rru e tn tsys me s. Dec isi no ta lb es a dn f tini e s tat e am c nih es are doog c ioh c se f ro FSA testi gn . fI an SA F is d se cribed gnisu a d ce noisi ta elb , c tidno i sno t ipy ca ll y ni lc edu trop i tupn eve stn , a dn acti sno are trop uptuo t eve stn . We can ht en sived e t se t cases ht at c revo eve yr c idno it no , eve yr acti no , ro tsom comp el et ly, eve yr lur e. sA we was f ro finite ts ate mac nih e m edo ls, et ts ca ses can c vo er eve yr s at ,et eve yr rt a tisn i ,no ro eve yr pa .ht

rhT ae d tes it gn based no d ce i is no ta lb es si c bmu e osr me. We thgim desc bir e t erh a sd sa se uq e cn es fo lur es f or m fid ef rent ed ci is no tabl se , tub siht eb co em s ve yr em yss ot t ar ck ni ter sm of c vo erage. eW

n ee d f tini e ts ate am c nih se as a inim m mu , a dn fi ht ere si a yn f ro m of i tn e ar ct noi , Pe rt i en st are a be tt er c ioh ce. hT ere we ac n devise erht ad te sts ht at c vo er eve yr lp a ec , ve ery tra sn i noit , a dn eve yr se uq e ecn fo rt a isn it .sno

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1. enO fo eht smelborp fo metsys ,gnitset ylralucitrap htiw evitcaretni ,smetsys si ot etapicitna lla eht egnarts sgniht eht resu thgim od . tahW sneppah ni eht MTAS metsys fi a remotsuc sretne eerht stigid

fo a NIP dna neht sklaw ?yawa

2. oT niamer ni“ ”lortnoc fo lamronba resu eb h roiva eht( roivaheb si ,lamronba ton eht ,)resu eht MTAS metsys thgim ecudortni a remit htiw a 03 dnoces emit - tuo . nehW on trop tupni tneve srucco

rof 03 s ,sdnoce eht MTAS metsys sksa fi eht resu sdeen erom emit . ehT resu nac rewsna sey ro on . esiveD a wen neercs dna yfitnedi trop stneve taht dluow tnemelpmi hcus a emit - tuo tneve .

3. esoppuS uoy dda siht emit - tuo erutaef ot eht MTAS .metsys tahW oisserger n gnitset dluow uoy

?mrofrep

4. ekaM na lanoitidda tnemenifer ot eht NIP yrT etinif etats enihcam ugiF( re )6.41 ot tnemelpmi ruoy tuoemit ,msinahcem neht esiver eht daerht tset esac ni elbaT 3.

5. ehT txet stressa taht eht“ 1B noitcnuf nottub srucco ni evif arapes te noc t stxe sneercs( b gnie

)deyalpsid dna h sa eerht tnereffid .”sgninaem enimaxE eht neetfif sneercs stniop( fo tneve )ecnecseiuq dna ediced rehtehw ereht era eerht ro evif tnereffid lacigol sgninaem ot a 1B ekortsyek .

6. seoD ti ekam nes es ot esu tset egarevoc scirtem ni noitcnujnoc htiw lanoitarepo ?seliforp ssucsiD ht is.

7. poleveD na lanoitarepo eliforp rof eht etaDtxeN melborp . esU eht noisiced elbat ,noitalumrof dna

edivorp laudividni noitidnoc .seitilibaborp ecniS a elur si eht noitcnujnoc fo its dnoc it ,snoi the tcudorp fo eht noitidnoc yrtne seitilibaborp si eht elur ytilibaborp .

5 8. I retn a itc on gnitseT Fa stlu a dn f lia ru se ud e ot tni er ca ti no are the bane of te ts e .sr hT ie r bus t ytel am kes ht em fid ficult to rec ngo ize a dn even erom diffic lu t to reve la yb et gnits . hT ese are d ee p fa lu st , no es ht at rema ni in a

tsys me even a etf r e tx ens vi e rht ae d et ts gni . nU f nutro a et ly, fau tl s of tni e ar c noit tsom fre uq e tn ly o cc ru as af ruli es ni viled ered sys smet t tah have b ee n ni su e f ro s mo e emit . cipyT ally ht ey have a ve yr wol

borp a lib ti y of e ex c ,noitu a dn they o cc ru no ly after a la gr e un mber of rht ae sd have b ee n ex ce .detu tsoM of t sih cha tp er si de ov et d ot descri gnib f smro of i tn e ar cti no , on t ot test gni ht e .m sA such, it is

rea yll rom e c cno erned wi ht re uq ireme stn eps ci if ca it no t ah t w ti h te gnits . hT e c enno c noit si mi trop ant: gniwonk woh to s ep cify tni erac snoit si the f tsri ets p in det ce gnit a dn te gnits f ro ht e .m

sihT cha tp er si a osl a os em hw at ihp l cihposo al a dn im dl ly tam h me lacita d csi noissu of af lu st a dn fa li ru es of tni e ar c ;noit we ca onn t poh e ot te ts mos e gniht if we t’nod dnu e tsr a dn ti . eW be nig htiw an mi trop ant a idd it no ot ruo f vi e bas si c no ts ur tc s, a dn su e th si ot dev le po a ta ymonox of t py se of

in et ar tc noi . Ne tx , we ved le po a s mi p el exte noisn ot c vno en it no al P te ir n te s ht ta er flects ht e basis c urtsno c st , a dn ht en ew rtsulli ate the lohw e sid cu iss no iw th ht e TAS M a dn S ta nru iW ihsdn le d W pi er ys ets sm , a dn mos etimes w ti h exa lpm es f or m et le nohp e ys ets sm . We c no c dul e yb a gniylpp t eh ta monox y to an tropmi a tn a lpp ica it no pyt e: c il ent-se vr er sys et .sm 5.8.1 C etno xt of nI oitcaret n

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.in Part of t eh id ff ic ytlu of ps ce if gniy a dn te gnits i tn e ar itc no s si ht at ht ey are so c mo m no . knihT fo a ll the gniht s ht at i tn e ar ct in ever dy ay efil : pe po le, a bomotu eli rd ive sr , re lug a snoit , che cim al com sdnuop , a dn a artsb cti ,sno to n ma e j su t a few. We are c cno e nr ed htiw tni er ca snoit ni os f wt a er c no t llor ed ys smets p( a itr c lu a ylr ht e nu e epx cted no e )s , os we ts art yb res irt tc gni ruo sid c noissu ot in et ar tc snoi ma gno ruo basis ys ets m c curtsno t :s acti ,sno da ,at eve stn ,strop a dn t erh a .sd

nO e way ot tse abl hsi a c tno ext f ro tni e ar c noit is ot v ei w ti as a re pihsnoital a gnom ht e fi ev c urtsno cts. If we did this, we dluow f dni ht at the r ale noit nI et ar tc s With is a r fe le vix e r le pihsnoita no e ca h e ytitn d( a at retni ca st wi ht da at , ac snoit wi ht hto er ac snoit , a dn os )no . tI a osl si a nib a yr

re al pihsnoit b te w ee n data a dn eve stn , data a dn rht ae sd , a dn eve stn a dn t rh ae sd . hT e data m gniledo a aorpp ch i t’ns a ed ad-e dn , woh ever. Whenever a data m ledo c tno ains us ch pe vr a vis e relati no shi ,sp that si a clue ht at an ropmi at tn e tn i yt is ssim i gn . If we a dd mos e ta lbign e r ilae yt to ruo fa ylri a artsb tc c urtsno tc s, we get a rom e esu ful frame krow f ro ruo ts ydu of tni e ar itc no . hT e gnissim e el em tn si l co at noi , a dn loca it no has t ow c nopmo e :stn mit e a dn .noitisop Data m do le gni divorp es a on ther c ioh :ec we can rt ae t col a noit as si htx bas ci e tn ti y, ro as an tta tubir e of the other f vi e. eW c ooh se t eh ta rt i etub a orpp ca h he .er What od es ti em an f ro loca it no (t mi e a dn )noitisop ot be an a rtt i tub e of a yn of ht e five ba sis c urtsno c st ? hT si si er a yll a s troh -c gnimo of en arly lla re riuq emen st ps ce if ci ita no ton ita sno a dn

cet uqinh se . hT( is si borp ably a osl ht e er a nos that i tn e ar c it no s are s le mod r ce ngo ized a dn tes et d.) nI f ro itam no a uob t col ta noi si usu lla y c er ated nehw a s tsy em is impl me e detn . oS em emit s col ta noi si am tadn ed as a re uq ire tnem — hw en t sih ha pp e ,sn the re riuq eme tn is er la yl a f cro ed mi lp eme tn a noit

c oh i ec . We f tsri cla ir fy eht em ani gn fo eht com nop ents fo col a :noit it em a dn op is .noit

eW can take t ow v ei sw of mit e: sa an sni at nt ro as a rud ita no . hT e ni ats tn ane suo iv ew el st su desc bir e hw en os m gnihte ha pp ens — ti si a tniop hw en mit e si an a .six hT e rud ati no iv ew si na in et vr al no the mit e a .six When we thi kn a tuob rud ati sno , we usu la yl are tni ere ts ed in ht e le htgn fo the t mi e tni e vr al, ton ht e e stniopdn ht( e ts art a dn if n si h mit e )s . htoB v ei sw are esu f lu . eB ac esu t rh ea sd ex ce ,etu ht ey vah e a rud a noit ; ht ey a osl have op ints ni mit e hw en ht ey e ex c etu . S mi li ar

sbo e vr ita no s a pp ly ot events. Often events have v re y rohs t rud ati sno , a dn this borp el m ta ic if t eh rud ati no is os s troh that eht eve tn i t’ns r ce ngo ized yb eht s tsy e .m

hT e op is t noi asp ce t is isae er. We c dluo take a v re y ta lbign e, isyhp c la iv ew of isop it no a dn de cs ri eb

ti ni te smr of s mo e c nidroo ate tsys em. soP iti no can be a rht ee mid e noisn al Car et is an c nidroo a et tsys me iw th re ps ect to mos e ro igin, ro ti c dluo be a l igno t du e-l dutita e-e vel a noit ge rgo a ihp c op i .tn roF mo ts s tsy e ,sm ti si rom e pleh f lu ot sl hgi t yl a artsb tc noitisop into orp ssec ro reside ecn . Ta nek

t go te her, t mi e a dn tisop i no et ll ht e te ts er hw en a dn hw ere mos e gniht ha snepp , a dn siht is se se itn la ot dnu e tsr a dn tni er ca .snoit

Bef ro e we deve pol ruo ta ymonox , we en ed mos e dnuorg ur el s a tuob t rh ae sd a dn corp e sross . roF

,won a orp ec ross is htemos i gn that ex ce tu es t rh e ,sda ro a dev ci e hw e er events co c .ru

1. ecniS sdaerht ,etucexe yeht evah a yltcirts evitisop emit ud .noitar eW yllausu kaeps fo eht noitucexe emit fo a ,daerht tub ew thgim osla eb detseretni ni nehw a daerht rucco s )setucexe( .

ecniS snoitca era etareneged sesac fo ,sdaerht ac snoit osla evah snoitarud .

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2. sdaerht owt ,rossecorp elgnis a nI c exe tonna c umis etu l at neo lsu y. atnemadnuf a selbmeser sihT l tpecerp fo cisyhp s: on owt seidob may ypucco eht emas ecaps ta eht emas .emit semitemoS sdaerht raeppa ot eb ,suoenatlumis sa ni emit gnirahs no a elgnis ;rossecorp ni ,tcaf emit erahs d

aelretni era sdaerht v de . nevE hguoht sdaerht tonnac etucexe ylsuoenatlumis ,rossecorp elgnis a no stneve nac eb is um atl n suoe . sihT( si yllaer citamelborp rof ).sretset

3. stnevE evah a yltcirts evitisop emit rud .noita nehW ew redisnoc stneve ot eb snoitca taht etucexe no trop ,secived siht secuder ot eht tsrif dnuorg elur .

4. ylsuoenatlumis rucco tonnac tneve na tub ,ylsuoenatlumis rucco nac stneve tupni )erom ro( owT ni owt ro( )erom srossecorp . sihT si demmi yletai raelc fi ew redisnoc trop secived ot eb etarapes

orp c srosse .

5. nI a elgnis ,rossecorp owt tuptuo stneve tonnac nigeb .ylsuoenatlumis sihT si a tcerid ecneuqesnoc fo tuptuo stneve gnieb c desua yb th daer execu it o sn . eW deen eht htob oenatnatsni su

dna noitarud sweiv fo emit ot ylluf nialpxe siht dnuorg elur . esoppuS owt tuptuo stneve era hcus taht eht noitarud fo eno si hcum retaerg naht eht noitarud fo eht rehto . Th palrevo yam snoitarud e

esuaceb( yeht rucco no etarapes ,)secived tub eht trats t semi tonnac eb ,lacitnedi sa nwohs ni F erugi .61 1. eehT si na elpmaxe fo siht ni eht MTAS ,metsys nehw a daerht uac s se neercs 51 ot eb

deyalpsid dna eht stceje taht MTA .drac ehT neercs si llits gnieb deyalpsid nehw eht drac tceje tneve .srucco sihT( am y eb a enif ;noitcnitsid ew dluoc osla yas taht trop secived era etarapes

,srossecorp dna trop taht tuptuo stneve era yllaer a mrof fo retni - orp c rosse c inummo c ).noita

ugiF re 5.8.1 vO er al gnipp vE ents

6. A daerht tonnac naps erom naht eno .rossecorp T sih noitnevnoc spleh ni eht noitinifed fo daerht s. yb gninifnoc a daerht ot a elgnis ossecorp r, ew etaerc a larutan opdne i tn rof ;sdaerht iht s

osla stluser ni erom elpmis sdaerht rehtar naht rewef lpmoc ex .sdaerht nI a itlum - gnissecorp ,gnittes siht ohc eci osla stluser ni rehtona mrof fo ecnecseiuq — snart - orp c rosse seiuq c ecne .

Taken got te her, ht ese xis uorg nd lur es f ro ec hw at we m hgi t ac ll “same beha roiv ” otno t eh in et ar tc snoi ni t eh ta ymonox ew ed f eni in s ce noit 61 . .3 5.9 ,noitcaretnI C noitisopmo , dna D msinimrete

hT e uq e noits of non -de mret in msi l oo sm as a ab c pordk to deep uq se ti no s in scie cn e a dn .yhposolihp tsniE e ni di t’nd b le ieve ni non -dete mr ini ms ; he no ec c mmo en et d that he buod et d t tah doG uow ld

p al y id ce htiw ht e revinu es . noN -deter im nism genera yll refe sr ot c sno e uq e cn es of ra modn event ,s a gniks ni e ff ce ,t if the er are t ylur ra odn m even st )stupni( , can ew ever iderp tc rieht c sno e uq e cn es?

hT e gol laci e rtx eme of ht is de ab et e dn s ni ht e hp li os cihpo a /l the golo ic la uq e noits of f er e wi ll ve susr rp e-de ts ina oit n. roF t nu ately, f ro te ts e sr , ht e os tf ware ve noisr of non -dete msinimr si el ss seve er . uoY im thg wa tn ot c disno er t sih es tc noi to be a et c inh c la e irotid al. It si ba des no my e px er ei cn e a dn

an la sisy su i gn the DSO f mar e krow . I fi dn it y lei sd rea nos a lb e a wsn e sr to ht e lborp em fo non - dete ;msinimr uoy may t .oo

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.in L te s’ ts a tr htiw a krow i gn defin ti noi fi d ete mr ini ms ; he er a er owt ibissop il it es:

1. A metsys si ,fi citsinimreted nevig ti s ,stupni ew nac syawla tciderp sti stuptuo .

2. A metsys si fi citsinimreted ti syawla secudorp eht emas stuptuo rof a nevig tes fo stupni . Sin ec the s ce dno v ei w (re ep atab el uo t )stup is el ss ts ri gn ent than the fi tsr rp( ed ci ta lb e uo t )stup , we’ll

su e it as ruo krow i gn ed f noitini . hT en a non - tsinimreted ic sys et m si no e ni hw ci h ht ere si ta l ae ts no e set fo upni ts ht ta re us tl s in t ow dis it nct se st of uptuo ts. s’tI ea ys ot dev esi a non - ed te itsinimr c

f etini ts a et m ca hine; Fi rug e 61 . 11 si eno e .elpmax

ugiF re 5. 11 A noN -de ret m citsini Fini et atS te aM c enih When t eh machine in rugiF e 5. 11 si ni ts ate 1d , if eve tn e1 co c sru , ht ere is a tra sn ti noi e hti er to etats 2d ro to .3d

If it si os easy ot cre ta e a non -dete mr i in ts ic finite ts ate m ca enih , yhw all the fuss a uob t deter im ni ms in ht e f tsri ecalp ? tI( snrut tuo that we can a wl ays fi dn a ed et citsinimr e viuq ela tn ot a yn non - dete itsinimr c finite ts eta machine a wyn ay.) Finite ats et m ca nih es are dom sle of rea ;ytil they no ly a amixorpp et the beha roiv of a real ys ets m. hT is is yhw ti is os ropmi at tn ot c sooh e an a rporpp i ta e m do el — we dluow ekil ot u es ht e be ts a ixorpp am ti no . R ylhguo ps ae gnik , d ce noisi at lb es are t eh m do e I fo c cioh e f ro ts at ci ni et r ca snoit , if ni et ts ta e m ca nih es us f cif e f ro nyd ma ic i tn e ar itc sno ni a si lgn e orp ce ss ,ro a dn mos e f mro of eP t ir ten is n ee ded f ro nyd a cim tni era snoitc in lpitlum e

orp ec ross s. eB f ro e gniog no , we dluohs idni c ta e tsni an ec s fo non -dete msinimr in eht other t ow m do els. A elpitlum hit d ce si i no ta lb e si eno ni hw ci h the i upn ts v( a ir a lb es ni the c noitidno )buts a er us ch aht t m ro e ht an no e lur e si se el etc d. nI Pe irt n te s, non -deter im ni ms co c sru hw en rom e than eno

tran is noit si ena lb ed. hT e c ioh ec of ihw ch ur le e ex c tu es ro ihw ch transit noi fires si made yb an e tx e nr al age .tn oN( t ci e ht at eht c ecioh si tca ua yll na )!tupni

ruO uq e ts noi of non -d te re im ni ms re cud se to t rh e sda ni an OSD net, a dn siht si hw ere ni et ar itc ,sno com op is noit , a dn deter im nism c mo e t go e ht e .r oT dnuorg ruo disc ssu i no ni s mo e gniht “ er a ,”l c disno er t eh MTAS rht e sda ew su ed ea ilr e :r

:1T wardhtiw 00.04$

:2T wardhtiw 00.06$

:3T tisoped 00.03$

rhT ae sd 1T , 2T , a dn 3T tni e ar ct iv a a data lp ace rof ht e acc nuo t balance, dna ht ey am y be e ex c tu ed in fid ef re tn corp e .sross ehT ini it al bala ecn si .00.05$

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eB nig w ti h t rh ead ;IT fi on hto er erht ad e ex c setu , ti lliw e ex c tu e c rro e tc ly, el avi gn a lab ance fo 01$ .00. soppuS e we began htiw t rh ae d ;2T we uohs ld r ae yll ac ll it “ tpmetta ot rdhtiw aw 06$ . ”00 ,

beca esu , if on hto er t erh ad exec tu es, ti lliw re us lt ni the fusni fi ic ent f dnu s sc er en. We s dluoh er a yll separate 2T otni owt rht ae sd , 2T .1 ihw ch si a us cc se sf lu w ti rdh a law ht at e sdn htiw ht e psid al y fo sc er en 11 t( ake ac )hs , a dn 2T .2 hw ci h si a fa li ed rdhtiw awal aht t e dn s htiw eht lpsid ay of sc er en 8

usni( f if cie tn f dnu s). woN le ’t s a dd emos tni e ar tc noi htiw t rh ae d .3T rhT ae sd 2T a dn 3T are 2- c enno cted v ai the bala cn e data place. If 3T exe tuc es eb f ro e 2T r ae sd t eh b ala n ec da at , then 1.2T o cc sru , other siw e 2T .2 o cc sru . hT e fid ef re cn e be wt een ht e t ow iv ews of ted e mr in msi si siv ib el he :er When the DSO n te of T2 be snig to ex ce ute, we tonnac rp e id ct the tuo c emo 2T( .1 ro 2T . )2 , os yb ht e f tsri de noitinif , th si is non -dete mr i sin t ci . By t eh s ce dno ed f tini i no , woh ever, we can er c er a et t eh in et ar tc noi ni( lc du i gn mit e )s between 2T a dn 2T . If we od , a dn we ac pt ru e the beha roiv as a

am kr i gn fo t eh com etisop DSO ne ,t ew w li l sa yfsit t eh repe ta a elb defin ti noi fo ted e mr i .msin 5. 11 C tneil -S revre itseT ng

lC ie tn -se vr er sys te sm ra e difficu tl to test beca su e ht ey e hx ibit the tsom diffic lu t f mro fo in et ar tc ,snoi the nyd a im c no es ac ssor mul lpit e orp ec sross . Here we ac n enj yo ht e be en tif s of ruo st gnor ht e ro e it c la dev le mpo e tn . Clie tn -se vr er syst me s a wl a sy e tn a li ta least t ow orp ce sross , eno

hw ere ht e se vr er os tf ware e stsix a dn e ex c tu es, a dn no e ausu( ll y several) hw ere ht e c il ent os ftware e ex c tu es. hT e m nia c nopmo e stn are usu lla y a tad a sab e management tsys em, a ilpp ac noit rgorp ma s that su e ht e databa es , a dn rp ese tn ta i no rgorp ma s ht ta cudorp e su ered if den uo t tup . hT e isop it no fo the es c nopmo e stn re us tl s ni t eh f ta se vr er .sv fat lc ei tn sid t tcni i no [Le siw ]49 ees( rugiF e 5. .)21

ugiF re 5. 21 Fat ilC e stn a dn Se vr ers

lC ie tn -se vr er ys ets sm a osl inc dul e a n te krow to c enno tc ht e c il ents wi ht ht e se vr er, the krowten os f wt a er , a dn a rg a hp ical su er tni erf ca e )IUG( f ro the c il ents. oT make am tters esrow , we c na

differe itn ta e gomoh ene suo a dn hete gor ene uo s SC s tsy e sm ni te smr of clie tn corp e ss sro ht ta a er ide itn c la ro dive sr e. hT e mul lpit e ter im nal ve sr i no of ht e MTAS s tsy em uow ld be a taf clie tn

tsys me , ecnis eht central ba kn od es ve yr il eltt fo eht tra sn ac it no corp e .gniss

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1. erugiF 1 4.6 sah a ni ec noitcennoc htiw s te yroeht . ekaT eht orp p snoitiso ,p ,q r, dna s ot eb the gniwollof tes yroeht stnemetats tuoba emos stes S dna :P

:p S P

:q S P = ∅

:r S P

≠ ∅

:s S ⊄ P

C nivno ec sruoy fle ht ta the rel ita spihsno ni ht e uqS are of noitisoppO ppa ly to ht ese set the yro soporp i .snoit

2. dniF dna ssucsid selpmaxe fo n- ytivitcennoc rof eht stneve ni elbaT 4.

3. ehT lartneC MTA metsys )MTAC( si eht o“ reht ”edis fo eht MTAS ;metsys ti su stropp the gniwollof ac :seitivit

a. nepO dna esolC knab occa u stn .

b. niatniaM yliad secnalab fo a stnuocc ot tcelfer eht eciwt iad ly sgnitsop fo MTAS snoitcasnart . esehT rucco ta 00:9 ma dna 00:3 mp .

c. edivorP cepxE det NIP dna tnuocca ecnalab noitamrofni ot na MTAS lanimret ,

d. ylppA a ecivres egrahc )00.1$( ot yna a ni snoitcasnart MTA eerht naht erom swohs taht tnuocca nevig day.

C no is der the C MTA a dn TAS M f nu itc no s, a dn colla ate these ot F ta Se vr er a dn Fat C il e tn f ro mul noita s .

4. tsiL selpmaxe fo eht suoirav sdnik fo noitcaretni s oy u nac dnif ni eht denibmoc MTAS/MTAC

metsys . ediceD rehtehw eseht snoitcaretni era detceffa yb eht taf revres .sv eht taf tneilc snoitalumrof .

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UNIT 6

PROC SSE F AR OWEM RK

6 1. laV ida it on a dn reV i itacif on

While sof wt are cudorp ts a dn orp ec ss se am y eb gduj ed no several porp e itr es ra gn i gn f or m t mi e- ot - am kr et to perf mro ance ot u as bi il t ,y eht os tf ware t se t a dn anal sy si t ce nh i uq es we c disno er a er

foc su ed rom e na worr ly no mi vorp ing ro a ss sse gni depe dn abi ytil . Asse gniss the de rg ee ot hw ci h a os f wt are tsys em tca ua yll fulfi ll s sti re riuq ement ,s ni ht e es sn e of m ee gnit ht e su e s'r real en e sd , si ac l el d v oitadila n. uF lf gnilli re riuq eme stn si ton the same sa c no f gnimro to a re uq i mer e stn ps e ic f ci a oit n. A ps ecific ita no is a ts atement a uob t a pa tr ci u al r soporp ed noitulos ot a borp el m, a dn

that soporp ed noitulos may ro am y ton ach ei ev ti s og la .s roM e vo er, sp ce if ci a snoit a er ttirw en yb pe po le, a dn ht e er f ro e tnoc a ni sim at kes. A tsys me that meets sti ca ut al og a sl is su e uf l, hw ile a

tsys me that si c tsisno e tn w ti h its eps cific ta i no si depe lbadn e . "Ver fi ci ita no " si c eh c gnik ht e c tsisno ency of an im lp me e tn a noit w ti h a ps e ic fica noit . He er , " ps e fic ica it no " a dn "imp el ment noita " are or el s, on t pa tr ci lu ar ar it af c st . roF exa elpm , an vo er la l de ngis c uo ld p al y the ro el of " eps cific ta i no " a dn a m ro e detai el d de ngis c dluo p al y ht e or el of " elpmi em tn at noi "; ch ce gnik hw e ht er the de at li ed de ngis si c tsisno ent w ti h the vo er la l d ise ng uow ld then be ver ifi cati no of the deta li ed d ise ng . Later, ht e same ted a li ed gised n c dluo p al y ht e lor e of " ps e fic ica it no " htiw re ps ce t to ruos ce c edo , hcihw dluow eb ve ir fied ag ia sn t eht de ngis . nI eve yr ca es , ,hguoht ver fi ic ta i no si a c eh ck of c tsisno ency ewteb en owt descri snoitp , ni c rtno a ts ot v la di ita no ihw ch com rap se a descrip it no hw( e ht er a re uq i mer e stn ps e ic ficat noi , a de ngis , ro a

nnur i gn sy ets )m aga tsni ca ut al nee sd . Fi rug e 5.1 ks etches ht e rel ta i no of ver fi ci ati no a dn v ila da noit activ seiti iw th re eps ct ot artifa tc s cudorp ed ni a os tf ware dev le mpo ent jorp e tc . hT e f rugi e dluohs

ton be tni e rpr eted as serp c gnibir a se uq e itn al orp ec ,ss cnis e ht e og la of a c tsisno e tn set of a itr fac st a dn u es r as fsit a tc noi are ht e as me hw ether the wtfos are art fi a stc (spe ic f ci a snoit , de ngis , c edo , e ct .) a er deve pol ed se uq e itn a yll , ite vitar le ,y ro in pa llar e .l

Fi ug re 2. :1 Valida noit ac tivit ei s check krow udorp tc s against ac ut al su er re riuq ement ,s elihw ve ir fica noit a tivitc ei s c eh ck c tsisno ency fo krow udorp tc .s

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.in Va il noitad act vi i it es re ref mirp a ylir ot t eh vo era ll ys ets m ps ecif ci ita no dna ht e final c edo . W hti res ep ct to vo era ll s etsy m eps cificati no , vali noitad c eh c sk f ro discrepanc sei ewteb en ca ut al sdeen a dn ht e tsys me s ep cif ci a noit as la di uo t yb eht an la stsy , ot e rusn e ht at the eps cific ta noi is an ade uq ate ug ide to ub li di gn a p udor ct that lliw f lu f li l i st og la s. iW ht re ps ect ot f ni al c edo , v ila da noit a smi at c eh c gnik sid crepancies be ewt en ac ut la en ed a dn ht e f lani udorp ct, to rev ae l op iss lb e fai serul of ht e deve mpol ent corp e ss a dn to ma ek rus e eht udorp tc em e st e dn - su er e epx ctati .sno V ila da noit c eh cks be ewt en the ps e ic fica it no a dn final udorp ct are mirp a ir ly c eh cks of dec snoisi ht at were tfel

po en in the ps ce if ci ita no (e. .g , ted ails of the u es r in et fr ace ro udorp tc fe ruta e )s . eW have imo tt de no e mi trop a tn s te of ver fi i ac noit ch ce sk f or m Fi rug e 2.1 ot a diov lc tu et r. nI a tidd i no ot chec sk t tah

compare t ow ro rom e a fitr ca st , ver fi ci ita no inc dul es c eh c sk f ro es fl -c tsisno ency a dn we ll - f ro em nd e ss . roF exa elpm , elihw ew ca tonn j gdu e ht at a rgorp am is " rroc e tc " e cx ept in fer ere ecn ot a ps e ic f ci a noit of hw at it dluohs od , we can ce tr a ylni dete nimr e that mos e rgorp a sm are " ni c erro ct"

beca su e ht ey a er i ll - f mro ed. We may likewise d ete mr i en aht t a s ep c ifi ac noit sti e fl si i ll -f demro beca su e ti si inc no is ts e tn r( e uq ir se t ow porp er eit s ht ta ca tonn htob be t )eur ro ma bi suoug ( ac n be in et rpr eted ot re uq ire mos e porp e ytr ro on t), ro b ace su e ti od es ton sat si fy os em hto er we ll - f ro em nd e ss c no ts rai tn ht at we im ,esop us ch as hda erence to a ts a dn a dr im sop ed yb a re lug ta yro agency. Va il noitad ag ia sn t ac ut al re riuq emen st necessari yl in vlov es muh an ju mgd ent a dn t eh op et aitn l f ro a ugibm i yt , im s dnu e tsr a gnidn , a dn disa rg ee me tn . nI c no tra ts , a s ep cific noita uohs ld be us ff ci ie yltn

rp ce si e a dn nu ma bi suoug that the er can eb on d asi rg ee ment a uob t ehw ther a pa itr cular s tsy em beha roiv si ca ce tp a elb . W lih e the te mr et s it ng is of et n su ed ni f ro m la ly ob th f ro gau gnig su eful ssen a dn ve ir fyi gn t eh udorp c ,t eht ac itivit es id ffer in ob th og la s a dn a orpp ca .h Depe dn a ilib ty porp e itr es cni l edu c erro tc ne ss , er ail lib ti y, ntsubor e ,ss a dn saf yte . C rro ce nt e ss si a tulosb e c no is ts ency htiw a ps ce if ci ati no , a wl a sy a dn in a ll c ri cum ts a cn e .s C rro tce ne ss htiw re ps ce t to non t ivir al s ep cif ci a snoit si a oml ts never ac veih ed. Re il ab ili yt si a tats tsi ci al a mixorpp ita no ot c rro ce nt e ss , e rpx se sed sa the kil eli dooh of c erro ct behavi ro in e px ce ted use. R ntsubo se s, lnu i ek c rro ce nt e ss a dn re il a ib l yti , wei shg porp erti se as m ro e a dn le ss c itir cal, dna d tsi ugni si hes hw i hc

porp er it es dluohs be am tni a ni ed even dnu er exc noitpe la c ri c tsmu a cn es in ihw ch f lu l f cnu it ytilano ca tonn be main at ni ed. A oog d re riuq eme stn od c mu e tn , ro s te of od cu em stn , uohs ld ni c dul e ob th a re riuq ements anal sy si a dn a re uq ireme stn eps cif ci ati no , a dn uohs ld cl ae rly di ugnits i hs ewteb en t eh two. hT e re uq ireme stn an la sy si describes the borp el m. hT e ps eci cif ati no desc bir es a desoporp os lu it on. hT si is ton a koob a tuob re uq ire stnem e nign ee ri gn , ub t we ton e in pa iss gn ht at

c no f gnidnuo re riuq eme stn ana yl is s htiw re uq i mer e stn s ep cific noita lliw ni e tiv a ylb have nega vit e mi ap tc s no htob val di ati no a dn verifi ac ti .no

6.2 De rg ee s fo Free mod

viG en a erp c si e s ep cif ci ta noi a dn a rgorp am, ti s ee ms that no e thguo to be a elb to a rr ive at emos l igo llac y s dnuo a ugr em nt ro oorp f that a rgorp am as tisfi se ht e ps ecified porp erties. After la l, if a c livi e gn i en er can perf mro ehtam am cit al c cla talu i sno ot wohs taht a gdirb e liw l ca yrr a ps ecified a nuom t of t far f ci , 'ndluohs t we be a lb e to limis a ylr a ylpp mathema cit al gol ci ot ve ir f ci ati no fo

rgorp ams?

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roF os em porp e tr ei s a dn os em v re y s mi p el rgorp a sm , it si ni fa tc lbissop e to tbo ain a gol ical c rro ce nt e ss a mugr e tn , a bl e ti ta hgih c tso . nI a few mod a sni , gol ic la c erro c nt e ss a mugr e stn may even be c tso -e ff ce vit e f ro a ef w losi ated, c ir t ci la c nopmo en st e( . .g , a as tef y tni e olr ck ni a em lacid dev eci ). nI gene lar , t hguoh , no e ac tonn cudorp e a c lpmo ete cigol al " foorp " f ro ht e f llu s ep cif ci a noit of arp c it c la rgorp a sm ni f llu deta .li hT si is on t j tsu a ngis ht at t ce onh l ygo f ro ve ir f ci a noit is

tammi ru e. tI ,si ra ht e ,r a c sno e uq ence fo eno fo eht most f adnu em tn al porp e itr es fo c tupmo ta .noi

soppuS e we od make su e of t eh af ct ht at rgorp a sm are e ex cu et d no r ae l mac nih se htiw fini et re rp esen at ti sno fo me yrom v la ue .s disnoC er t eh fo ll gniwo t ivir al Ja av lc sa :s

1 c ssal rT iv ai {l 2 citats i tn (mus int ,a i tn )b { re ut rn a ;b+ } 3 }

hT e Java la ugn age de if in it no ts eta s ht ta the re rp esen at noit of an ni t si 23 bina yr dig ti s, a dn t suh

there are ylno 232 × 232 = 462 ≈ 01 21 fid ferent upni st no hw ci h the me doht rT iv ai l. )(mus en ed eb et ts ed to tbo a ni a oorp f of ti s c erro tc ne ss . At no e na eson c dno 01( -9 s ce dno s) per et ts ca es , siht iw ll at ek a mixorpp at le y 01 21 sec ,sdno ro a tuob 03 , 000 y ae .sr

A t ce uqinh e f ro ver fi iy ng a p por e ytr ac n be i an c ruc eta in no e of owt dire snoitc (Fi rug e 6.2). It am y be pes mis i its c, mea gnin t tah ti si on t ug ara deetn to a cc e tp a rgorp ma ve en fi eht rgorp ma od es

ssop e ss eht porp erty b ie gn an la zy ed, ro it can be mitpo i its c if it may ac pec t mos e rgorp a sm aht t od ton ssop sse the porp e ytr .e.i( , it may ton d te ect lla loiv ta )snoi . moS e a sisylan et c nh i seuq may give a

thi dr op iss b el a wsn er, 'nod" t .wonk " We ac n c sno ider ht ese t ce nh i uq es ot be e hti er po it itsim c ro pe itsimiss c depe dn i gn no woh we in et rpr et the " t'nod wonk " re tlus . Pe efr itc no si tbonu ia na ,elb ub t

eno can c esooh et chni uq es that e rr in no ly a part luci ar di er c .noit

ugiF re 6. :2 Veri cif ati no trade-off id me snoisn

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.in A sof wt are verif ci ita no t ce uqinh e ht ta e rr s ylno in ht e pe itsimiss c erid ction si ca ll ed a c sno erv evita an la isy s. It im thg seem ht at a c sno e vr a it ve ana sisyl uow ld a wl ays be rp efera lb e to no e that c dluo ac ec tp a fa ytlu rgorp a .m woH ever, a c sno e vr ita ve ana sisyl wi ll often udorp ec a ve yr al gr e un m eb r of suoirups e rorr re rop st , ni a noitidd to a ef w acc ru ate re rop ts. A muh an am y, htiw mos e eff ,tro d itsi hsiugn r ae l fa stlu f or m a few suoirups re strop , ub t ca tonn c epo fe f ce t vi e yl w ti h a l gno tsil fo

troprup ed fa lu st of hcihw mo ts are fa sl e a al rm .s Often ylno a ca er f lu iohc ce of c lpmo eme tn a yr tpo imi its c a dn pe itsimiss c et c nh i uq es can pleh ni utum la yl re ud c gni the differe tn lborp e sm of t eh

cet uqinh es a dn ecudorp acce tp a elb re us l .st Ma yn dif ef rent te smr ler ated ot p se si sim tic a dn po t mi istic ni acc aru cy a pp ae r in ht e til er ta ru e no

rgorp am an la .sisy We have c soh en these pa ucitr al r terms beca esu it is f ia ylr ea ys ot er em rebm ihw ch si ihw c .h htO er te smr a er ader si l yleki to ne c nuo et r ni cl du e:

aS ef A s fa e ana sisyl has on mitpo si t ci ani cc ru ca ;y th ta is, ti acce stp ylno rroc tce rgorp a sm . In other

ki sdn of rgorp am ana sisyl , saf te y si r ale ted ot the og al of ht e ana sisyl . roF exa pm le, a as ef a an ly is s re al ted to a rgorp am po mit zi a noit is no e ht at a swoll ht at po it noitazim no ly hw en ht e re lus t of eht

tpo imi z ta noi iw ll eb c rro ce .t

oS und dnuoS n sse si a te mr to describe eva ul a noit of f lumro as. nA ana yl is s of a rgorp ma P w ti h res ep ct to a f lumro a F si nuos d if the ana yl is s re snrut True no ly hw en the rgorp am ac ut ally od es sa sit fy ht e f alumro . If as it fs a tc i no of a f ro m lu a F si taken as an cidni at noi of c erro tc ne ss , ht en a

nuos d an la sisy si t eh sa em as a fas e ro c sno ervat vi e an la sy si . If t eh es sn e of F is re rev es d i( . .e , if ht e t htur fo F cidni ates a fa tlu ra ht er than c rro e ntc sse ) then a

nuos d an la sy si si ton en ec ss a ir ly c sno erv ita ve. nI ht at c esa ti si lla wo ed po t simi tic cani c aru cy but m su t ton have p misse itsi c i an cc ru acy. etoN( , woh ever, ht at su e of ht e mret nuos d has ton la ways b ee n c isno ste tn ni ht e fos tware e enign eri gn il et ra rut e. oS em tirw e sr su e t eh te mr nuosnu d as we esu t eh te mr po timi cits .) Co lpm ete elpmoC et n se s, kil e ndnuos e ss , si a te mr ot desc bir e eva aul t noi of f umro al s. nA ana sisyl of a rgorp am P w ti h r se p ce t to a f ro m lu a F is c lpmo ete if ht e ana yl is s la syaw re snrut urT e hw en t eh

rgorp am ca ut ally od es as it sfy ht e f alumro . If s sita fa tc noi of a f lumro a F si taken as an i idn c ta i no fo c rro ce nt e ss , ht en a c lpmo te e ana sisyl si no e t tah imda st ylno po t simi cit ani cc ru ca y. nA a sisylan ht at si dnuos tub ni compl ete si a c sno e vr ta vi e an la .sisy

Ma yn e lpmax es of bus s it tu it gn is mpl ,e c eh cka lb e porp e itr es f ro ac aut l porp erties of i tn ere ts ac n eb f dnuo ni the de ngis of m do e nr rgorp mma gni la ugn ages. disnoC er, f ro xe a elpm , the porp e ytr ht at each va ir a lb e s dluoh be ini it la ized w ti h a va ul e b fe ro e i st va ul e is su ed in an e rpx e noiss . nI the C al ugn age, a compiler ca tonn vorp ide a rp e esic ts at ci check f ro siht porp erty, eb ac su e of t eh

ibissop il yt fo c edo ekil eht f lo l gniwo :

1 i tn ,i us m; 2 i tn if ;1=tsr 3 f ro ;0=i( ;01<i )i++ { 4 if (f ri ts ) { 5 ;0=mus fi ;0=tsr

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6 } 7 s mu =+ ;i 8 }

tI si i opm ssible in gene ar l to d ete mr i en hw te her ae ch c no t lor fl wo pa ht can be e ex c tu ed, a dn lihw e a

muh an lliw iuq c ylk rec zingo e ht at t eh va ir able mus si itini la zi ed no the f sri t iter ita no of ht e ,pool a com lip er ro other st ta ic na a yl is s loot lliw t ipy c ylla ton be a lb e ot lur e tuo na e ex cu it no in ihw ch t eh in ti ai il zati no si ppiks ed no eht fi tsr ti erat noi . Java nea lt y vlos se ht is lborp em yb am gnik c do e li ek this lli egal; that si , ht e ur el si th ta a va ir a lb e tsum be ini it la zi ed no all rgorp am c no t lor pat ,sh

hw te her ro ton esoht htap s ac n ever eb e ex cu et .d

oS f wt are deve pol e sr are s modle ta libe ytr ot desi ng new rest ir tc snoi into ht e rgorp a mm i gn al ugn ages a dn com ip el sr ht ey ,esu tub eht sa em nirp ic lp e ac n eb a ilpp ed hguorht ex et nr al t ,sloo on t

ylno f ro rp o rg a sm tub a osl f ro hto er fos twa er a afitr tc s. C sno ider, f ro ex ma elp , the fo ll gniwo c dno i noit that ew im thg w si h ot im esop no re riuq eme stn od cu em :stn

1. E ca h is ng i cif a tn mod ia n te mr s llah a epp ar htiw a de inif it no ni eht ssolg a yr fo eht od c mu e .tn

sihT porp erty is nearly mi op iss b el to c eh ck a otu tam ica ll y, since determ ni i gn hw te her a pa tr ci ralu drow ro rhp ase si a " ingis fi ac nt mod a ni te mr " si a ma tt er fo muh an j mgdu e tn . roM e vo er, muh an

ins ep ction of ht e re uq i mer ents od c mu e tn ot che kc siht re riuq eme tn wi ll be e rtx em le y te suoid a dn e rorr - norp e. What ac n we od ? nO e a aorpp ch si to separate ht e ed ic nois ht at re uq ires namuh j mgdu ent di( e itn fyi gn wo dr s a dn rhp ases as " ngis i if ac tn ") f or m ht e tedi suo hc ce k f ro rp ese cn e ni t eh

ssolg ary. E ca h ngis if ci a tn od m nia et rm hs all be s te fo f ni ht e re riuq emen st od c tnemu yb the u es of a ts a dn a dr ts y el term. hT e defa tlu vi laus re rp ese tn ati no of the term lyts e si a gnis el dnu erl ni e ni

tnirp ed od c mu ents a dn prup le te tx in no - il en lpsid a .sy E ca h drow ro rhp ase ni ht e t mre st ly e hs la l a epp ar w ti h a ed fi in t noi ni ht e glo ass yr of t eh od c mu e tn .

porP e ytr 1( a) ts li l re riuq es muh an j gdu em tn , tub it si won in a f ro m ht ta si um ch rom e a em na lb e ot

ins ep ction. porP erty b1( ) can eb ea ylis a amotu et d ni a way ht at iw ll be c lpmo et le y rp ce si e (e cx e tp that ht e ta ks of deter im gnin hw e ht er d fe snoitini ppa ae ri gn ni ht e ssolg a yr are elc ar a dn c rro tce m su t a osl be el ft to muh a )sn . sA a s ce dno ex ma elp , c no is der a eW b- sab ed se ivr ec in w ih ch su er se snoiss n ee d on t erid c ylt in et ar ct, ub t they od r ae d a dn idom fy a shared c ello c noit of tad a no the se vr er. nI this case a c citir al reporp ty si iam n at gnini in et irg ty of ht e hs ared da at . tseT i gn f ro siht porp e ytr is

roton i uo ls y id ffic ,tlu ceb au es a "race c dno iti no " tni( e fr ere cn e eb t ew en gnitirw data ni eno corp e ss a dn readi gn ro irw it ng ler ta ed data in a hton er orp ec )ss may ac su e an sbo e vr ab el fa ruli e no ly v re y rarel .y

nutroF eta yl , there is a ir ch ydob of a cilpp a lb e ser ae rch re stlus no c no c ru rency c no t lor ht ta can eb e px l tio ed f ro th si a acilpp t noi . tI dluow be f iloo sh ot re yl mirp ar li y no rid e tc et gnits f ro ht e de ris ed in et rg ti y porp e tr ei s. tsnI ae d, no e dluow c sooh e a w( le l- nwonk , f mro la yl ve ir fie )d c no c rru ency c no t lor otorp c lo , such as the t ow - hp ase loc gnik otorp c lo , a dn re yl no mos e c nibmo at noi of ts ta ci an la sisy a dn rgorp am tes it gn to ch ce k c no f ro am cn e to that otorp c lo . Im op is gn a pa tr ci lu ar c no c rru ency c no t lor otorp col sbus tit tu es a um ch lpmis er, us ff icient porp erty owt( - hp ase loc gnik ) f ro the com lp ex porp e ytr of i tn ere ts s( e ir al zi a ib l )yti , ta mos e co ts in ge ren al ti y; ht at is, there a er

egaP 1 06

a.1

1.b

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.in

rgorp a sm ht ta vi lo a et owt - hp ase ol cki gn a dn ye ,t yb d ise ng ro bmud luck, as sit fy se ir la izab li yti of da at acces .s tI si a c nommo rp ac cit e to f ru ther esopmi a bolg al dro er no lock a cc e sess , ihw ch ag ia n mis plifies et ts i gn a dn anal sy si . Te gnits uow ld di entify e ex c noitu se uq e cn es ni ihw ch da at is acce ss ed tuohtiw

porp er ol c sk , ro in hcihw loc sk are tbo ia ned a dn reli hsiuqn ed ni an dro er th ta od es ton r se ep ct the t ow - hp ase orp toc lo ro the gl bo al ol ck dro er, even if data tni e rg i yt is on t loiv ated no ht ta partic lu ar e ex cut noi , beca su e ht e ol c gnik orp toc lo f ia rul e dni ci ta es ht e top e itn al f ro a da gn e suor r eca c dno i noit in emos other ex ce noitu ht at mi thg co c ru ylno ra yler ro dnu er e ertx me ol a .d W hti ht e a tpod i no of c do i gn c vno en it no s that make ol c gnik a dn olnu c gnik ac it no s ea ys to rec ngo ize, it may be op ssi lb e ot rely mirp ar li y on f wol ana sisyl to d ete rm ni e c no f mro an ec htiw t eh loc gnik otorp c lo , iw th the elor of nyd am ci et gnits re cud ed ot a " ab ck- pu " ot ra esi c no fide cn e ni t eh

ndnuos e ss of the ts ta ic an la sisy . toN e th ta ht e c itir ac l ed c si i no to opmi es a pa itr c lu ar ol c gnik orp toc lo is on t a sop t- oh c ed c noisi ht at ac n be dam e ni a set ti gn " hp a es " at ht e e dn of deve mpol e .tn aR ht e ,r ht e lp an f ro verificati no acti iv eit s iw th a tius able ab al nce fo c tso a dn sa rus a cn e is pa tr fo

tsys me de .ngis 6.3 iraV e it es fo oS ftw era

hT e wtfos are testi gn a dn ana sisyl tec uqinh es rp ese tn ed ni ht e ma ni par st of t sih koob we er deve pol ed irp am ylir f ro orp ce rud al a dn jbo tce - iro e tn ed os f wt a er . While ht e es "gene cir " et c seuqinh are at lea ts par lt y a pp lic ba el ot tsom va ir te ies of os f wt a er , pa itr c lu ar a cilpp a noit mod a sni e( . .g , real-

emit a dn saf te y-c citir al os ftware) a dn c urtsno c noit m sdohte e( . .g , c no c rru ency a dn syhp aci l d tsi tubir i no , rg a ihp lac su er ni et afr sec ) ca ll f ro pa itr cu al r porp e itr se to eb verif ei d, ro ht e r ale tive mi trop an ec of id ffere tn porp erties, as w le l as mi sop i gn c no ts ra stni no a ilpp ac lb e t ce nh i .seuq

pyT aci yll a os ftware sys met od es ton f lla neat yl into no e ac te og ry ub t ra ht er has a bmun er fo re el va tn cha ar cte itsir cs that tsum eb c no is dered hw en lp a gninn ve ir fi ac .noit E ex cr si es

hT e knumpihC am kr te ni g di noisiv si ow rr ei d a tuob ht e ts art- pu it me of ht e new ve noisr fo ht e R do ent SO po er ita gn s sy et m (an mi( agina yr ) po era gnit tsys me of Chipm )knu . ehT

ma kr eti gn di siv i no re rp e es tn ita ve ggus e sts a os f wt are re riuq eme tn sta gnit ht ta eht start- pu t mi e sha ll ton eb a gniyonn to su e .sr

pxE al ni yhw siht lpmis e re riuq ement si on t ve ir fia lb e a dn yrt to r fe alumro et ht e re uq ireme tn

ot ma ek ti ve ir fia .elb

disnoC er a lpmis e eps cifica noit la ugn age LS that describes tsys e sm id a rg tamma ci lla y in te smr of f snoitcnu , ihw ch re rp ese tn data rt a rofsn itam no s a dn c rro e dnops ot don es of t eh

id a rg am, a dn flo sw , ihw ch re rp ese tn data f swol a dn c rro e dnops ot arcs of ht e id a rg am.[4]

D ai rg a sm ac n be h ei ra ihcr c ylla r fe ni ed yb a oss ciati gn a f nu itc no F (a don e of ht e id a rg a )m wi ht an LS ps eci cif a noit ht at ted ails f nu c noit F. swolF a er labeled to dni ica et t eh t py e fo d ta .a

Su gg est emos c eh c sk f ro es lf-c no is ts ency f ro LS .

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A c la e dn ar rgorp am uohs ld divorp e mit ely re dnim e ;sr f ro ex ma elp , ti s dluoh remi dn t eh resu of an pu co im gn eve tn ea ylr e hguon f ro t eh u es r to take a tc noi , ub t on t t oo earl .y

nU f ro t nu ately, "ea ylr e hguon " a dn " oot ae rly" are uq la iti es that can no ly be va dil ated htiw ca ut la su e sr . woH im thg uoy de vir e verifia lb e ped e dn a ib il yt porp e itr es f or m ht e mit ile ne ss

re riuq eme tn ?

It si os tem i em s mi trop a tn in itlum - erht aded a cilpp a snoit to e rusn e that a uqes e cn e of acce ss es yb no e rht ae d ot an a rgg egate data curts t ru e e( . .g , os me dnik of at bl )e a pp ae sr to hto er rht ae sd as an a ot mic tran as c it no . When ht e hs ared data ts ur tc eru is m ia n at ni ed yb a data esab tsys em, the database tsys me pyt ic la yl u es s c no c rru ency c no t lor slocotorp ot e rusn e t eh

ta omi tic y of ht e tra asn c snoit ti manages. oN us ch a motu a it c roppus t si pyt i ac yll av ia lab el f ro data ts ur tc ru es niam t denia yb a rgorp am ni ma ni m me .yro

Am gno the po t snoi av ia la elb ot rgorp emma sr to rusne e se ir ila zab ili yt ht( e li l su i no of atom ci acc )sse a er eht f gniwollo : hT e rgorp a mm er c luo d m ia tn ain ve yr co ra se- rg ia n loc gnik , rp eve nitn g a yn

ni et lr ae gniv of a cc e ss es ot ht e shared data st ur c erut , even hw en us ch tni e elr a gniv uow ld be ha lmr e ss . roF( ex ma ple, each rt a asn c noit c uo ld be e cn a lusp eta d ni an is elgn

nys c norh ized Java em doht .) sihT a orpp ca h can ac su e a rg ae t ed al of nnu ece ss a yr olb cki gn bet ew en erht a sd , gnitruh perf mro a ecn , ub t it si almo ts tri iv al ot verify ie ht er

aut mo a it c la ly ro am un a yll . uA to am ted ts ta ic ana sisyl et c uqinh es can mos e mit es ve ir fy es ir aliza lib ti y iw th if ner-

rg a ni ol cki gn , even nehw os em me sdoht od on t su e locks ta a ll . hT si a orpp ca h can ts li l ejer ct emos s ste fo me sdoht ht at dluow e erusn se ir alizabili .yt

hT e rgorp ma mer c dluo be re uq ired to su e a pa ucitr al r c no c rru ency c lortno otorp col ni sih ro her c edo , a dn ew uoc ld ub i dl a sta it c an la sy si loot ht at ch ce sk f ro c no f mro a ecn

htiw th ta orp toc lo . roF exa elpm , a hd erence ot the c nommo t ow - hp ase- ol c gnik orp toc ,lo w ti h a few re rts ci ti ,sno can eb c eh cked ni siht wa .y

eW hgim t a gu em tn eht data ca ec ss es to dliub a es air il ibaz il ty hparg urts c erut re rp esen it gn the "ha snepp b fe ero " rel ita no a gnom tran cas snoit ni te gnits . tI ac n eb

nwohs th ta ht e rt a asn cti no s e ex c tu ed ni se ir a zil a lb e ma nn er if a dn no ly if eht ser ai zil a ilib ty rg a hp si yca c .cil

pmoC are the ler a vit e isop it no s of ht ese a orpp ca hes no ht e t erh e a ex s of ve ir fic noita

t ce uqinh e :s pe simiss t ci ni acc aru c ,y po it mi cits ani cc ru ca ,y a dn mis lp if ei d porp e tr ei .s

When dpu ati gn a rgorp am e( . .g , f ro re vom i gn a fault, cha gnign ro a dd i gn a f cnu it ilano t ,)y orp rg a mm e sr may udortni ce new faul st ro e sopx e rp evi suo ly ddih en fa stlu . oT be rus e t tah

ht e dpu ated ve sr i no m tnia a sni ht e f nu c it no a il ty divorp ed yb ht e rp evi uo s ver is no , it si co mm no rp a tc ice ot er ex ce tu e t eh t tse ac ses d ise ng ed f ro the f ro em r ve snoisr fo ht e rgorp a .m

eR e ex c gnitu te ts c sa es ngised ed f ro rp evi uo s ve snoisr is c la led re rg e ss i no tes it .gn

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When test gni la gr e c lpmo ex rgorp ma ,s eht bmun er of re rg e noiss t tse ca es s may be la gr e. fI dpu ated sof wt are m su t be e px edited e( . .g , to rep ia r a sec ytiru nluv erab li yti bef ro e ti si

e px loite )d , t tse de ngis ers am y n ee d to se el ct a sbus te of re rg e noiss et st cases ot eb r ee x ce tu ed . sbuS e st of tset ac ses can be sel ce ted acc dro i gn to a yn of several differe tn c tir e .air

nA tni erest gni porp e ytr of some re rg e noiss te ts eles cti no crite air si that they od ton to exc edul a yn tset ca es taht c dluo ylbissop rev ae l a faul .t

6 3. Basic rp i cn ipl se

nA la sisy a dn et gnits )T&A( h sa eb en c nommo rp act ci e cnis e ht e ae lr ei ts so wtf a er orp jec .st T&A activ seiti were f ro a gnol t emi b esa d no c nommo es sn e a dn i dn i udiv la ks li sl . tI h sa me e gr ed sa a d itsi tcn di lpics i en ylno in eht la ts t eerh decade .s

sihT cha tp er a covd a et s six nirp c lpi es ht at char ca te zir e va suoir a aorpp ches a dn t ce nh i uq es f ro an la sisy a dn t tse gni : se isn it v ti y, re dnud ancy, re irts cti no , part noiti , siv ib li yti , a dn fee bd ack. oS em fo these nirp ci lp es, us ch as par it noit , siv ib li yti , a dn fee bd ack, are tiuq e general in e enign e gnir . htO e sr ,

ton a lb y se tisn i iv ty, re dnud ancy, and re irts itc no , a er s ep cific to &A T and c rtno i tub e ot char ca te zir i gn T&A as a dis ic lp i .en 6.3.1 Se sn i ytivit

uH am n deve pol e sr ma ek e rorr s, udorp c gni faul st ni os f wt a er . Fa lu st am y l ae d ot f lia seru , ub t fa ytlu os f wt are am y ton fail no eve yr ex ce itu no . hT e se sn iti v ti y nirp c lpi e ts eta s that it si be tt er ot f ia l eve yr

emit ht an s mo e mit e .s C sno ider t eh c tso of dete gnitc a dn repa ri i gn a wtfos are fa tlu . If ti si det ce ted emmi id ate yl e( . .g , yb an no -the-fly s tny tca ic c kceh ni a desi ng e id t )ro , t neh the co ts of c rro ec it no si

v re y ms a ll , a dn ni fa tc ht e il ne b te ew en fault verp e noitn a dn af u tl de et tc noi si bl rru ed. fI a fau tl si de et cted in psni ce noit ro nu it test gni , ht e c tso si its ll rela vit le y ams ll . If a fa tlu vivrus es ini ait l de et c noit e ff stro at the nu ti level, ub t ggirt e sr a iaf rul e det ce ted ni i tn e rg a it no tes it gn , ht e c tso fo c rro ce noit is much rg ae te .r If ht e fi tsr f ia rul e si de et cted ni tsys em ro ca c tpe a cn e t se ti gn , the c so t si v re y hi hg edni e ,d a dn t eh m so t costly fa stlu a er esoht eted cted yb c su t mo e sr ni eht fie .dl A af u tl ht at t ggir ers a f ia l ru e no eve yr e ex c tu i no si lnu i ylek to vrus ive pa ts tinu te ts .gni A char ca te itsir c of fau tl s that cse ape dete tc i no tnu li much later is that ht ey t ggir er liaf ru es no ly rarel ,y

ro ni combina noit wi ht circu sm ta cn es ht ta es me rnu e al ted ro are fid ficu tl ot c no t lor . roF exa elpm , a af u tl that resu tl s in a fa ruli e no ly f ro mos e usunu al c rugifno ta snoi fo c otsu em r e uq ip em nt may be

diff ci tlu a dn e px e visn e ot de et ct. A af tlu ht at stluser in a fa ruli e ra odn m yl ub t v re y rarely - for exa elpm , a ar ec c noitidno th ta ylno occasi no la yl ca su es ad at c purro it no - may kil e iw es e cs a ep de et c noit litnu the sof wt are si ni su e yb uoht as sdn of c motsu e sr , a dn even then be id f if c lu t ot d ai esong a dn c rro e .tc

hT e s am ll C rgorp am in Fi rug e 6.1 has t erh e ytluaf llac s ot st gnir c ypo orp ec rud es. hT e c la l ot strc yp , ts nr c yp , a dn ts ri gn Copy a ll pa ss a cruos e rts i gn " ldduM e ,d " ihw ch si oot l gno to fit in ht e a rr ay m elddi . hT e nluv era lib ti y fo rts c yp is we ll nwonk , a dn is eht cul tirp ni ht e yb - won - ats dn a dr ub ffer vo erf wol a tt ca sk no ma yn ne krowt res v eci s. nU f anutro et yl , ht e uaf lt am y ro may ton ca esu

an sbo e vr a lb e fai rul e ped e dn i gn no ht e a rr a gn ement of em yrom ni( ht si ac es , ti depe sdn no hw at

egaP 1 36

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.in a epp a sr in the isop it no that dluow eb m elddi [ ,]7 ihw ch iw ll be irwrevo ett n iw th a ne lw i en char ca ter). hT e ts a dn a dr r ce omme dn ati no si to su e nrts c yp ni lp ace of st ypcr . W ih el ts nr c yp a ov i sd

vo e tirwr ing hto er m me yro , ti nurt c ta es the ni tup tuohtiw wa nr i ,gn a dn s mo e mit es uohtiw t porp erly lun l-te mr in ta gni the tuptuo . hT e repl ca eme tn nuf c noit rts i ypoCgn , no ht e hto er ha dn , su es an

a ss e noitr ot e rusn e ht at, if ht e ta gr et st gnir si oot gnol , ht e rgorp am a yawl s af sli ni an sbo e vr a lb e am nn er . 1 / ** 2 * esroW naht korb e :n erA y uo fee il gn ul cky? 3 /* 4

5 #in lc du e <a ss e tr . >h 6

7 c ah r eb f [ero ] = " eB= f ro e="; 8 c ah r m [elddi ] = M" i eldd "; 9 c ah r afte [r ] = " A= fte =r ";

01 11 diov )(wohs {

12 rp i tn f(" s% \ s%n \ s%n \n", b fe ,ero im ,eldd af et )r ; 31 } 41 51 diov rts i gn C (ypo hc ra t* a gr e ,t c tsno c rah ruos* c ,e i tn )giBwoh ; 61 71 int m ia (n ni t a gr c, ahc r *a )vgr {

18 )(wohs ; 19 strc m(yp i dd l ,e " dduM el d" ;) */ ,tluaF tub yam ton liaf /* 20 )(wohs ; 21 st nr c ,elddim(yp dduM" el d", is zeof m( i ))eldd ; /* uaF l ,t yam ton liaf */ 22 )(wohs ; 23 st gnir C (ypo m ddi l ,e " ldduM ed", is zeof( im ldd e ;)) /* etnarauG ed to af il /* 24 )(wohs ;

52 } 62 72 /* Se evitisn ve isr no fo nrts cpy; c na eb c tnuo ed no ot fail 82 * ni na sbo erv ba le w ya YREVE emit t eh s ruo ce si oot egral 92 * rof t eh grat e ,t ilnu ke t eh st dradna ts rnc yp ro rts c .yp 03 /* 13 diov rts i gn C (ypo hc ra t* a gr e ,t c tsno c rah ruos* c ,e i tn )giBwoh {

32 a ss e (tr ts lr e cruos(n e) < woh iB ;)g 33 strc (yp at gr e ,t ruos ec ;) 43 }

ugiF er 6. :1 tS a dn a dr C nuf itc no s rts cpy a dn ts ypcnr am y ro may ton af li hw en the s cruo e ts gnir is

t oo gnol . hT e orp ce rud e s irt gn C po y is se vitisn e: tI si ug ara tn ee d ot fa li in an sbo e vr a lb e way if the ruos ec rts i gn si t oo long. hT e sensit vi i yt nirp ic p el says th ta we s dluoh yrt to am ke these faul st

egaP 1 46

Vtusolution.in

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.in eas ei r ot de et tc yb ma gnik them ca su e f rulia e rom e often. tI can be a ilpp ed ni erht e ma ni wa :sy ta the de is ng level, cha gnign the way ni hw ci h the rgorp ma fai ;sl at the na la sisy a dn testi gn leve ,l c ooh is gn a et c nh i uq e m ro e r le ia lb e iw th re eps ct ot ht e porp erty of tni e er st; a dn ta the e vn i mnor ent el ve ,l c sooh i gn a et c euqinh taht re ud ec s eht mi tcap fo e tx e nr al fa tc sro no t eh re .stlus

eR lp a gnic ts rc yp a dn st nr c yp w ti h stri oCgn py ni ht e rgorp am of giF ru e 6.1 si a lpmis e exa lpm e fo

a pp li ac noit of the se tisn i iv ty rp inc pi el ni de ngis . R nu - mit e a rr ay sdnuob ch ce gnik in ma yn rgorp a imm gn la ugn ages ni( c dul i gn Java tub ton C ro C ++ ) si an exa pm el of ht e sen is vit i yt p nir ic elp

a pp lied at ht e la ugn age level. A va ir te y of oot ls a dn re lp acements rof the s at dn a dr m me yro am nageme tn li rb a yr are av ia la lb e to e hn a cn e sen tis vi i yt ot em yrom lla co a it no a dn er ef re cn e af tlu s

in C a dn .++C hT e f ia l- af ts porp erty of Java iter ta sro si a hton er a pp l ci ita no of ht e sen is tiv ti y nirp ic lp e. A Java eti ra rot vorp ides a way of ac ec ss i gn each ti em ni a c llo ce ti no da at ts ur tc .eru

W tuohti ht e fa li -fa ts porp e ytr , idom f gniy the c llo ec noit ihw el ti erat gni over it c dluo el ad ot nu e epx cted a dn a br ti ra yr resu tl s, a dn fail ru e m hgi t o cc ru rar le y a dn be ha dr ot dete tc a dn id a .esong

A fail-fa ts ite rotar has the porp erty that an emmi id ate a dn sbo e vr ab el f lia ru e gniworht( C no c rru e idoMtn f ci ita xEno ec )noitp co c sru hw en ht e li el g la idom ficat noi o cc sru . hguohtlA fail-f sa t beha roiv si ton ug ara etn ed if t eh dpu ate co c ru s ni a ffid ere tn erht ad, a af li -f sa t ti erat ro is far rom e se tisn i ev ht an an eti ra rot w ti tuoh eht f ia l-f sa t porp e .ytr Re ud ndancy

eR dnud ancy si ht e soppo eti of dni epe dn ence. If no e pa tr of a os ftware ra tifa tc rgorp( am, desi ng od cu em nt, e ct .) c rtsno a sni the c tno ent of a on t reh , ht en ht ey are ton e ritn e yl dni epe dn e tn , a dn it si

lbissop e ot c eh ck them rof c tsisno enc .y hT e c cno ept a dn def ni i noit of re dnud ancy a er taken f or m inf ro m noita ht e yro . nI c nummo ica noit , re dnud a ycn ac n be udortni ced otni em ss ages in ht e f ro m fo e rorr - detecti gn a dn e rorr -c rro ecti gn c do es to ug a dr ag ia n ts tra sn m noissi e srorr . nI os f wt are te ts a dn an la sisy , we hsiw ot det ce t fa stlu aht t c dluo le da ot id fferences bet neew in et dn ed behavi ro a dn actual beha roiv , os the mo ts va ul a lb e f ro m of re dnud ancy si ni ht e f mro of an e ilpx ic t, re dnud a tn s at teme tn fo in et .tn Where re dnud ancy ac n be udortni ec d ro e tiolpx ed iw th an aut amo t ci , al rog i imht c c eh ck f ro c tsisno ency, ti has ht e a vd a tn age of b ie gn much hc ae per a dn rom e t hguoroh ht an nyd a im c te ts i gn ro

am laun inspe noitc . tS at ci t py e c eh cki gn is a lc cissa a cilpp ati no of ht is nirp c lpi :e hT e epyt d ce la ar t noi is a ts ateme tn of tni e tn ht at si at least pa tr ly re dnud ant iw th ht e su e of a va ir a lb e in t eh

ruos ec c .edo hT e t py e d ce la ar t noi c sno tr ia ns hto er parts of the c edo , os a c tsisno ency ch ce k (t py e c eh c )k can be a ilpp e .d nA im rop at nt tre dn ni ht e e ov lut noi of rgorp mma gni al ugn ages is int udor itc no of a dd ti noi al wa sy to ed clare i tnetn a dn a motu ta i ac lly ch ce k f ro c tsisno ency. roF exa elpm , Java enf cro se ur el s a tuob e ilpx cit yl ed clari gn e ca h exce noitp ht at ac n be t nworh yb a

em oht d . C eh cka lb e re dnud ancy si ton l mi i et d ot rgorp am ruos ec c edo , ron is ti mos te hi gn ht at ac n eb int udor ec d no ly yb rp og mmar gni al ugn age de ngis ers. F ro exa elpm , os tf raw e de ngis t oo ls pyt ica yll

divorp e ways ot c eh ck c tsisno ency be ewt en differe tn d ise ng iv e sw ro a afitr cts. nO e ac n a osl in et tn i no a ll y cudortni e re dnud ancy ni other os f wt are a fitr a stc , even soht e ht ta are ton e ritn e yl f ro am l. roF exa elpm , no e m hgi t udortni ce lur es tiuq e ana suogol ot t py e d ce larat snoi f ro

egaP 1 56

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.in se sim t ur c rut ed re uq i mer ents ps e cific a noit od cu em stn , a dn there yb e ban el a motu ta ic c eh c sk for c tsisno ency a dn mos e imil et d ik sdn of c lpmo etene ss . When re dnud a ycn si a rl ae yd rp ese tn - as be ewt en a os f wt are s icep fic noita od c mu e tn a dn s ruo ec c do e - ht en the re gniniam cha ll e gn e is to

am ke rus e ht e ni f ro am noit si re rp ese tn ed ni a way ht ta fa tilic ates c eh ap, t hguoroh c tsisno e ycn chec sk . Ch ce sk that can be mi lp eme tn ed yb aut mo a it c sloot are ausu ll y prefera elb , tub there is v la ue even in gro a zin i gn inf ro am noit to ma ek inc sno i ts ency ae s ei r ot tops ni ma un al i epsn cti .no fO c esruo , no e ca tonn la syaw bo tain c eh ap, hguoroht chec sk of uos cr e c do e a dn other od c mu e .stn

oS em it m se re dnud ancy is e px loited i sn tead iw th nur - emit c eh c sk . feD e visn e rp o rg a gnimm , e ilpx c ti nur -t emi ch ce sk f ro c dno ti snoi aht t dluohs a syawl be t ur e fi the rgorp ma si e ex c gnitu c rro ect ,yl is

a on ther a ilpp c ta i no fo uder dn ancy ni rgorp a mm i gn . Restricti no When there are on ac pec at ylb ch ae p a dn ef ef ct vi e wa sy ot ch ce k a porp erty, mos etimes no e nac cha gn e ht e lborp em yb hc ce gnik a id ffere tn , rom e re irts ctive porp erty ro yb l timi i gn ht e c eh ck ot a s am ll e ,r erom ser t ir ctive c al ss fo rgorp a .sm C sno ider ht e borp el m of e rusn i gn th ta each var ai lb e is init ai lized eb f ro e ti si u es d, no eve yr e ex cut noi . elpmiS as eht porp erty ,si ti si ton ssop ible f ro a c lipmo er ro anal sy si t loo to rp ec si yle dete nimr e hw e ht er it dloh s. S ee the rgorp am in Fi rug e 6.2 f ro an li rtsul a noit . Can ht e var ai lb e k ever be itininu alized ht e f ri ts mit e i si a dd ed ot it? fI mos Ce idno it )0(no a wl a sy re snrut t eur , then k will be i in it zila ed to ze or no ht e f ri ts time hguorht the pool , eb f ro e k si ni c tnemer ed, os pe hr a sp ht ere si

on op et itn al f ro a nur -ti em e rorr - tub tem doh os em noitidnoC c dluo eb a br ti rar li y com lp ex a dn im thg even depe dn no mos e c idno it no in ht e e mnorivn ent. Java's noitulos ot t sih melborp si ot

enf ro ce a st cir et r, relpmis c idno it no : A rgorp am si on t pe mr ti ted to have yna s ny cat it c c ortno l pa sht no ihw ch an nu in ti lai ized refere cn e c uo ld co c ru , rega ldr e ss of hw e ht er t soh e pa sht c uo ld actua yll be

e ex c tu ed. ehT rgorp am ni rugiF e 6.2 sah us ch a p ta ,h os t eh Ja av compiler er jec st .ti

1 /** A irt vi la me doht iw th a top e tn ial yl tininu ia il zed v ra i ba l .e 2 * aM y eb emos C tidno i (no 0) is la w sya ,eurt dna the er f ero k si 3 * a wl ays i in it al zi ed be of re esu ... tub it s' opmi ssi ,elb in 4 * gene ,lar ot k on w rof .erus Java er ej c st eht m te .doh 5 */ 6 ats cit diov noitseuq a lb e )( { 7 ni t ;k 8 for (int ;0=i i < ;01 )i++ { 9 if emos( C (noitidno i )) { 10 ;0=k 11 } e sl e { 12 ;i=+k 13 } 14 } 15 tsyS me . tuo . rp int ;)k(nl 16 } 71 }

egaP 1 66

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.in

ugiF er 3. :2 Can the va ir a elb k ever be zilaitininu ed the fi sr t time i si a dd ed to ti ? hT e porp erty si dnu ec di a ,elb os Java e rofn ec s a is m lp e ,r cirts et r porp e ytr .

Java s' lur e f ro ini it zila a noit b fe ro e su e is a rgorp am s ruo ec c do e re ts ir cti no that ena lb es erp c ,esi efficie tn ch ce gnik of a lpmis e tub ropmi at tn porp e ytr by ht e comp eli r. hT e c ioh ce of rgorp mma gni al ugn age )s( f ro a orp j tce am y en at il a bmun er of us ch rest ir cti no s ht at pmi a tc te ts a dn anal sy i .s

ddA i noit al re irts c snoit may be mi sop ed in the f mro of rgorp mma gni ats dn a sdr (e. .g , rest ir ct gni t eh su e of pyt e ac sts ro op i tn er ar ti hm ite c in C), ro by oot ls in a deve mpol ent norivne em tn . htO er f smro

of re irts cti no can a ylpp ot architect ru la a dn detailed desi ng . C sno ider, f ro ex ma ple, the lborp me fo e gnirusn ht ta a tra sn ac it no c no is s it gn of a se uq ence fo ca ec ss es ot a com lp ex data st ur c erut yb no e

orp ec ss a pp ae sr ot ht e distuo e row ld as if it had co c rru ed atomical yl , htar er ht an tni e elr aved htiw transa itc no s of hto er corp e ss e .s sihT porp erty si ac lled se ir la i baz li ti y: hT e dne resu tl of a es t of us ch transa itc sno uohs ld a epp ar as fi they we er a ilpp ed ni emos se lair dro e ,r e nev fi ht ey di .t'nd

aP r tit i no Part noiti , often osla nwonk as "di ediv a dn c uqno e ,r " si a gener la e gn in ee gnir nirp c elpi . Dividi gn a com lp ex orp lb em otni borpbus el sm ot be a att cked a dn vlos ed i dn epe nedn t yl is borp a ylb ht e mo ts com nom muh an lborp em-sol iv ng rts eta yg . oS f wt are e gn inee gnir in par cit lu ar a pp lies ht si nirp ic elp in ma yn dif ef re tn f ro sm a dn ta alm so t la l dev le po em tn el v le s, f mor ea ylr re uq ire em stn ps e ic f ci a snoit ot c do e a dn ma tni ena ecn . nA a syl si a dn te gnits are on except :noi ht e par it noit

nirp ic lp e si wide yl su ed a dn e px l tio ed. Partit noi i gn ac n be a ilpp ed ob th ta corp e ss a dn et c euqinh el v sle . tA ht e orp ssec vel el, we divid e c lpmo ex ca tivit ei s otni se st of lpmis e ca ti iv eit s ht ta ac n eb

a tt ca ked i dn epe dn e yltn . roF ex ma ple, tes it gn is usu a yll divided otni tinu , i tn e rg a noit , tsysbus e ,m a dn ys ets m te ts i gn . nI t sih way, we ac n foc su no differe tn s cruo se of fau tl s at id fferent ts e sp , a dn at each ts ep, we ac n take vda an at ge of the re stlus of ht e f mro er ets .sp roF sni at nce, we can esu nu i st that have eb en t se ted sa sbuts f ro i tn e rg ati no te gnits . moS e ts ita c a sisylan t ce uqinh es il kewi es f wollo the ludom ar st ur ct ru e of ht e os ftware tsys me to di div e an a an l sy si borp el m otni ms aller s et ps .

bisiV li yti

ibisiV il yt m ae sn the abi ytil ot aem rus e rgorp e ss or ts ta su aga tsni og la s. nI os f wt are e gn in ee gnir , eno enc tnuo e sr ht e libisiv ti y rp in ic lp e ma ylni ni ht e mrof of orp ec ss siv ib li yti , a dn ht en am ylni in t eh f ro m of sche lud e vi ilibis t :y a ib til y ot j gdu e the ats te of dev le mpo e tn ag ia tsn a jorp tce sche .elud

uQ ila yt orp ssec libisiv ti y la os a pp lies ot me sa gniru ac ih eved ro( rp e id c )det uq a ytil again ts uq al yti og la s. ehT rp inc lpi e of vi ilibis ty ovni lves se tt i gn og la s that ac n be a ssess ed sa we ll as devi gnis em sdoht ot a ss e ss ht e ri er la zi a .noit

ibisiV il yt si c sol e yl r ale ted to sbo e vr a ilib ty, the ba i il ty ot e artx tc su ef lu rofni am ti no f or m a os f wt a er

ar it af ct. hT e archi et tc laru de ngis a dn dliub lp an of a ys ets m determ ni es hw at lliw eb bo es vr a lb e ta each ts age of deve tnempol , ihw ch in nrut al gr e yl dete nimr se ht e vi ilibis ty of rgorp e ss aga tsni og als at that stage.

egaP 1 76

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.in A va ir e yt of mis ple tec nh uqi se can be su ed ot vorpmi e sbo erva ilib ty. roF exa pm le, ti is on ac ic dent that tropmi a tn tnI e nr te orp toc slo kil e PTTH a dn PTMS ( lpmiS e aM li narT s trop otorP col, su ed yb

tnI e nr te m ia l se vr e )sr a er based no ht e excha gn e of lpmis e textual c mmo a sdn . hT e c ioh ce of si elpm , muh an-r ae da lb e te tx ra ht er ht an a rom e com ap ct nib a yr enc do i gn has a s llam c so t ni perf mro a cn e

a dn a la gr e pa oy ff in sbo e vr abi ytil , inc dul i gn ma gnik c urtsno c noit of test rd ive sr a dn ro ca les much s mi p el r. U es of muh an-r ae da lb e a dn muh an- de ita lb e fi el s si il kewi es a vd isa lb e hw erever the perf mro a ecn c tso si acc tpe a .elb F ee abd ck F ee abd ck si a hton er c al ciss e gn i en e gnir nirp ic p el ht at a ilpp es ot ana yl is s a dn te gnits . eF e abd ck a pp lies ob th ot ht e corp e ss itself orp( c sse mi vorp eme )tn a dn ot i dn i udiv al t ce uqinh es e( . .g , gnisu et ts hist iro es to ezitiroirp re rg e noiss te .)gnits

S sy et ma cit i psn ec noit a dn wa hguorhtkl derive trap fo t rieh ccus e ss f or m fee bd ca .k Pa citr ipa stn ni ins ep cti no a er diug ed yb ch ce stsilk , a dn c eh c stsilk are re siv ed a dn er f ni ed based no e px e ir e .ecn New c eh c tsilk ti me s may be de vir ed f or m or ot ac su e ana sisyl , ana zyl i gn rp evi suo ly bo es vr ed fa li ru es ot ide itn fy eht ini tial e srorr that el ad to .meht E ex cr si es

dnI aci et ihw ch nirp ic lp es diug ed eht f lo l gniwo c ioh c :se 1. sU e an e tx e nr ally r ae da elb f mro at al os f ro ni et nr al f li e ,s hw en .elbissop 2. oC ll ect a dn an la zy e da at a tuob fa stlu rev ae led a dn er m vo ed f mor t eh c .edo 3. Separate set t a dn de iggub ng ca tivit ei ;s that is, separate ht e desi ng a dn xe ce noitu fo

te ts ac s se ot reve la af i rul es ( et )ts f mor the ol cal zi a noit a dn re vom la of t eh c rro e dnops i gn af stlu d( e iggub n )g .

4. ugnitsiD i hs te ts ac es ngised f mor ex ce .noitu 5. udorP ce c lpmo e et fault re rop t .s 6. sU e inf ro am noit f mor et ts ca es de ngis ot vorpmi e re uq ireme stn a dn de ngis

ps e ic f ci a noit s . 7. rP ovide tni e afr ec s f ro f yllu epsni c gnit t eh i tn e lanr ts a et fo a c .ssal

A mis lp e mecha msin f ro a mgu en it gn fa tlu lot e nar ce c sno tsi s of replicat gni com up at ti no a dn compari gn ht e tbo ia den re lus ts. aC n we c no is der re dnud ancy f ro fault lot erance an a pp li ac ti no fo t eh re dnud ancy rp inc elpi ?

A s sy et m fas ety eps c cifi a noit desc bir es horp ibi det beha roiv s hw( at eht tsys me m su t never od ). lpxE a ni woh eps c ifi ed as f te y porp erties nac be v ei wed as an im lp eme tn ati no of t eh

re dnud ancy nirp ci .elp

P or ec ss visi ilib ty can be inc er ased yb ext ar c gnit inf ro itam no a tuob ht e rgorp e ss of t eh orp ec ss . idnI ac et s emo ni f mro ati no taht can eb eas li y udorp ec d ot increa es orp ec ss v isi bi .ytil

egaP 1 86

3.1 3.2

3.3

3.4

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.in 6.2 T eh Qual yti Pr co ess

nO e can di e itn fy par cit lu ar acti iv eit s a dn re bisnops li iti es in a wtfos are deve mpol e tn corp e ss ht at a er foc su ed mirp ar li y no e rusn i gn ade uq ate depe ilibadn ty of the os ftware udorp ct, um ch as no e c na ide itn fy hto er a itc v ti ies a dn re snops i ib li it es c no ec nr ed rp ami ylir htiw orp cej t sche lud e ro htiw

udorp ct u as ilib ty. tI is c vno e in ent ot puorg ht ese uq a ytil a russ an ec a tc i iv eit s dnu er ht e irbur c " uq al ti y orp sec ,s " a tl hguoh we sum t a osl er c zingo e ht ta auq l yti si in et rt niw ed htiw a dn sni epara elb f or m hto er af ec st of ht e vo er la l orp ec ss . kiL e hto er par st of an vo era ll s wtfo are orp ec ss , the uq al yti

orp ec ss divorp es a fra krowem f ro es el itc gn a dn a rr a gnign ca itivit es a mi ed ta a pa itr c lu ar og la , hw i el a osl c sno ide gnir tni e ar c snoit a dn trade- fo fs iw th hto er mi trop ant og a .sl llA os f wt a er deve mpol e tn activ seiti reflect c rtsno a stni a dn rt ade- fo f ,s a dn uq a il yt acti iv ties are on e cx ep it .no roF e ,elpmax hi hg depe dn abi ytil is usu a yll in te noisn htiw mit e to ma kr et, a dn ni mo ts c sa se ti si be tt er to ca ih e ev a er a nos a ylb hi hg de rg ee of depe dn a ib il yt no a it hg t sche lud e naht to ca ih eve rtlu a- gih h depe dn a ib lity no a hcum l gno er sche ,elud alt hguoh ht e tisoppo e is t eur in mos e mod ia sn e( . ,.g ce tr ain me id ac l dev ci .)se 6.2.1 nnalP i gn dna M no i ot ir gn

orP ce ss vi libis ti y si a yek caf rot in sof wt are corp e ss ni genera ,l a dn os f wt are uq tila y corp e ess s ni par cit lu ar. A corp e ss si lbisiv e to the e tx e tn t tah no e can a wsn er ht e uq tse noi , " woH seod ruo

rgorp e ss c pmo a er ot ruo p al n?" ipyT ca ll y, sche lud e vi ytilibis si a main e hpm a sis ni corp e ss desi ng (" rA e we no sche ud le? woH af r ahead ro be dnih ?"), ub t in os f wt are uq al ti y orp ec ss an e auq l e sisahpm si n ee ded no rgorp e ss again ts uq ila yt og la s. If no e ca tonn ga ni c no fide cn e ni the uq a ytil fo the sof wt are ys ets m gnol b fe ore ti re ca hes f lani te gnits , ht e uq al ti y orp ec ss has on t ca ih eved ade uq ate v isi bi ytil . A we ll -de ngis ed uq a il ty orp ec ss bala cn se seve lar ca t vi i it es ac ssor the lohw e deve mpol e tn corp e ss , les e tc i gn a dn a rr a gnign them to be as c tso -e ff e vitc e sa ,elbissop a dn ot mi vorp e ae rly vi libis ti y. siV ib li yti is par cit lu a ylr chal el gnign a dn si no e reas no (a gnom severa )l aht t uq la yti act vi i it es are usu la yl lp aced as ae ylr ni a os tf ware orp ssec sa elbissop . roF exa elpm , no e

de ngis s et ts cases ta the ae ilr est inutroppo ty ton( "j tsu in t mi e") a dn u es s ob th auto am ted a dn am laun ts a cit ana sisyl t uqinhce es no os ftwa er a fitr ca st taht a er udorp ec d eb f ero ac ut al c .edo

6.2.2 Q au l yti G ao ls

orP ce ss siv ib li yti re seriuq a cl ae r eps ic cif a noit fo og a sl , a dn ni eht case of uq al ti y orp c sse siv ib li yti this inc dul es a ac r fe ul id its nc noit ma gno depe ibadn il yt uq a itil e .s A te ma ht at od es on t have a c el ar id ae of the ffid eren ec bet ew en r le iabil ti y a dn ntsubor e ss , f ro exa elpm , ro of ht eir re al it ev mi trop an ec in a ejorp ct, has l ltti e chance of a tt a gnini either. oG sla m tsu be f ru ther er fined into a

c el ar a dn r sae no a lb e s te of bo ej ct vi es. If an gro aniz ta noi clai sm ht at gnihton el ss ht an %001 re ail lib ti y w li l us ff eci , ti si on t se gnitt an ma bi uoit s bo jecti ev . R hta er, ti si es tt i gn on bo je tc vi e at a ,ll a dn c gnisooh ton ot make r ae nos ed trade- fo f d ce snoisi ro ot bala ecn l timi ed ruoser c se ac ssor va suoir ca ti iv eit s. It ,si ni ef ef ct, a gorb ta gni er nops is bi ytil f ro fe ef tc vi e uq ila yt p al gninn , a dn

ael vi gn trade-o ff s a gnom c tso , cs he elud , a dn uq al yti to an a rtibr a yr , da oh c d ce si i no based on d ae enild a dn gdub et la eno . hT e er al vit e mi natrop ce fo ilauq ties a dn the ri r ale ti no to other orp j ce t

jbo e tc ives va ir es. Time- ot -ma kr te may be the m so t ropmi at tn porp e ytr f ro a am ss ma kr et udorp c ,t

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.in su a ytilib may be rom e morp ine tn f ro a Web based a cilpp ita ,no a dn as fe yt may be ht e vo e gnidirr

re riuq eme tn f ro a l fi e-c tir ci la sys te .m

hT e e tx e lanr porp e itr es of os ftware can mitlu ate yl be divided into depe badn i ytil od( es ht e sof wt a er od hw at it is tni e dn ed to od ?) a dn usefulne ss . hT ere si on rp ice se depe dn abi ytil way to dist ugni i hs

the es , tub a ur le fo bmuht is ht at hw en fos twa er si ton depe dn a elb , we say ti has a fa ,tlu ro a de ef c ,t ro (m so t tfo e )n a ,gub re tlus i gn in an dnu e ris a lb e behavi ro ro fail eru . It is uq ite ssop i elb ot dliub

tsys me s taht are ve yr re il abl ,e relativ le y f er e f mor esu f ssenlu haza dr s, a dn c lpmo te yle u es el ss. hT ey am y be bnu ae rably wols , ro have te lbirr e u es r i tn e fr aces a dn nu fat moh a lb e od c mu e tn ta noi , ro they am y be im gniss sever la c ur ci la fe ruta es. woH dluohs ht ese porp erties eb c sno idered ni sof wt a er

uq la yti ? nO e a wsn er is ht ta they are on t part of uq la yti ta all lnu e ss ht ey have b ee n e ilpx c ylti ps e ic fied, is cn e ilauq ty si the rp esen ec of ps e ific ed porp e itr es. woH ever, a oc mpa yn sohw e udorp c st

are er cej ted yb sti cu ts o em sr liw l take til tle c trofmo in wonk ing ht ta , yb so em ed f snoitini , ht ey we er hi hg - uq la yti udorp c .st

eW can od b te ter yb c disno e gnir uq a ytil as fu ifl l eml tn fo re riuq ed a dn de ris ed porp erties, sa d itsi hsiugn ed f or m eps c ifi ed porp er it se . roF exa elpm , even if a c il e tn od es on t e ylticilpx ps ce ify ht e re riuq ed perf mro a cn e of a s tsy em, the er is a wl a sy mos e level fo perf ro am nce ht at si re riuq ed ot eb

su eful. enO fo the m tso c citir al ta sks in os ft raw e uq al ti y a ylan is s si maki gn de ris ed porp ert sei e px li ic t, is nce porp e eitr s ht ta rem ia n nu spe ic fied (even ni f mro la )yl are ve yr kil e yl ot rus fa ec

lpnu e sa a tn ly hw en ti si d csi vo ered that they are on t met. nI am yn cases th ese implicit re uq ireme stn can on t no ly be made lpxe ci ti , ub t a osl am de us f if cie yltn rp ec esi that ht ey ac n be made pa tr fo depe dn a ib lity ro rel ai ytilib . roF ex ma ple, ihw le ti si be tt er ot e ilpx citly r ce zingo e su a ib lity sa a re riuq ement ht an to l vae e it lpmi ci ti , it is be tt er yet ot a mgu ent[1] u as lib ti y re riuq eme stn htiw ps e ic f ci tni e afr ce ts a dn a ,sdr os that a de iv a noit f mor eht sta dn a dr s si er c zingo ed as a fed ce .t

tnI e fr ace ats dn a sdr a gu em tn , r ta her ht an re lp a ec , su abi ytil re riuq e em stn ceb a su e c no f mro a cn e ot t eh

s at dn a dr s si ton us f if cie tn a russ a cn e t tah the re riuq eme tn is met. hT si si ht e same er l noita th ta hto er ps e ic f ci a snoit have ot ht e su er re riuq eme stn ht ey are in et dn ed ot f lu fill. nI general, ver fi gniy

c no f mro a ecn to eps c snoitacifi od es ton re lp a ec va dil at gni s ita fs a noitc fo re uq ireme .stn 6.2.3 Depe badn i ytil Pr po re it es

hT e elpmis ts of the de dnep a lib ti y porp e itr es si c rro tce :ssen A rgorp am ro tsys em si c rro tce if it si c tsisno ent htiw i st s ep cif ci a noit . yB d fe noitini , a eps cif ci ati no divid es a ll lbissop e tsys me beha sruoiv otni owt c sal es s, cus ec ss es ro( c rro ce t e ex c )snoitu a dn f rulia es. All of ht e ssop i lb e beha sroiv fo a c erro ct s tsy em a er us cce ss e .s A rgorp am ac tonn be yltsom c rro ce t ro s mo e hw at c rro ce t ro %03 c erro c .t tI si a tulosb le y c erro tc no a ll ssop i lb e behavi sro , ro e sl e ti si on t c erro ct. tI is v re y ae ys ot ca ih eve c rro tce ne ss , cnis e eve yr

rgorp am si c rro ce t w ti h re eps ct to os em v( e yr ba )d ps ice cif ati no . Ac ih e gniv c rro tce ne ss w ti h re ceps t to a esu f lu spe fic ica ,noit no eht hto er ha dn , is modles arp tc i ac l f ro non tri iv al tsys me s. hT er fe ,ero

lihw e c erro c nt ess may be a bon el og al, we are often tni eres et d ni a ss e gniss mos e m ro e ac ih eva elb el v le of depe dn ab li yti . Re il abili yt is a lacitsitats a xorpp imati no ot c erro c ent ss, ni ht e sense ht at

%001 r le ia ib til y is ugnitsidni si ha lb e f or m c erro c nt se s. hguoR ly sp ae ki gn , re il ab li ity is a em as ru e fo

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.in the il doohilek of c erro ct f nu itc no f ro os me " tinu " of beha roiv , ihw ch dluoc be a s gni el u es ro

rgorp am ex ce noitu ro a pe doir of t emi . Like c rro ce nt ess, er ail ilib ty si er al tive ot a sp ce if ci a noit hw( ci h ted e nimr es hw e ht er a tinu fo beha roiv si tnuoc ed sa a cus ec ss ro af ruli e .) ekilnU c erro tc ne ,ss

re ail lib ti y si a osl rela it ve to a pa tr ic lu ar su age orp fi el . hT e same gorp mar can be m ro e ro le ss re ail elb depe gnidn no woh it si su e .d Partic lu ar em a rus es of ler ai ytilib can be su ed f ro dif ef re tn stinu fo ex ce ut noi a dn fid ef rent wa sy fo c nuo t gni us cc sse a dn fa li eru . Av libalia ity si an a rpp irpo eta em a rus e hw en a fa ruli e has emos

rud ati no in it em . roF exa elpm , a f lia ru e of a ne krowt uor et r am y make ti mi ssop i lb e ot su e s mo e f tcnu i sno of a col al a er a n te krow tnu li ht e se ivr ce is re rots e ;d eb t ew en tini ai l fail ru e a dn re rots ati no we say the r tuo er is " nwod " ro " nu av ia la .elb " hT e ava li abi ytil of the retuor si the it me in ihw ch t eh

tsys me si " pu " gnidivorp( nor am l es vr eci ) as a rf ca noit of tot la emit . suhT , a n te krow uor et r ht at averages 1 ruoh fo nwod t mi e in e ca h 42 - ruoh pe doir uow ld have an av ia labi ytil fo ,4232 ro 59 . .%8 Me na mit e be wt een f ia l ru es TM( B )F si yet a on t reh em a rus e fo r le ia ilib ty, al os su i gn t mi e as eht nu it of ex ce u it no . hT e htopyh te i lac n te krow tiws ch aht t ipyt ac lly f ia sl no ce in a 42 - ruoh pe doir a dn takes a uob t an ruoh ot er c vo er has a em an mit e be wt e ne f ia l ru es of 32 sruoh . toN e ht at ava li ab li yti od es

ton sid it hsiugn b te w ee n owt f ia erul s fo 03 m uni et s each a dn no e fail ru e las it gn an ruoh , elihw FBTM od es. hT e ed fini snoit of c rro tce ne ss a dn r aile lib ti y have (at ael )ts wt o maj ro w ae nk e ss e .s

Fi sr t, si cn e ht e cus ec ss ro f erulia fo an e ex c noitu si aler it ve to a s ep cifi ac noit , ht ey a er ylno as st gnor as the s ep cif ci a noit . eS c dno , ht ey make on tsid in tc noi b te w ee n a af il ru e ht at is a m roni a yonn an ec a dn a fa eruli that re lus ts in catast ehpor . hT ese are ilpmis fyi gn a tpmuss i sno ht ta ew ac ec tp f ro ht e sake of rp ce si i no , ub t ni mos e ic tsmucr an sec - pa itr c lu a ,ylr tub ton ylno , f ro cr ti ical

tsys me s - it si i tropm ant ot c no is der depe dn abi ytil porp e tr ei s that are le ss ped e dn e tn no specif ci a noit a dn taht od sid it hsiugn am gno fa ruli es depe dn i gn no seve ir ty.

oS f wt are as fety si an e tx e sn i no of ht e we ll -e ts a ilb shed f ei ld of tsys me as ef yt otni os tf wa er . aS f te y si c cno e nr ed iw th rp eve gnitn ec r at ni dnu esira lb e beha ,sroiv ca ll ed drazah s. It is uq i et e cilpx i ylt ton c cno e nr ed wi ht ca ih evi gn a yn su fe ul behavi ro apart f mor hw atever nuf c noit al ti y is en eded ot

rp event haza sdr . oS f wt are as fe yt si t ipy lac yl a c no ce nr in "cri cit al" sys et sm such sa a inoiv cs a dn em id ac l sys et sm , tub ht e ba cis cnirp lpi es a pp ly ot a yn ys ets m ni hcihw pa itr cu al ylr dnu esira elb

beha sroiv can be ugnitsid i hs ed f mor nur -of-the- im ll f ia .erul roF exa elpm , elihw it si a yonn i gn hw en a drow orp ec ross cra hs es, ti is much rom e a yonn i gn fi it erri c vo era ylb c stpurro od c mu e tn f li es. ehT deve pol e sr of a drow orp ec ross mi thg c disno er safe yt htiw r se ep tc ot t eh haza dr of f li e c noitpurro separa ylet f mor r aile ilib ty htiw re ps ect ot the c lpmo ete f tcnu i ano l re uq ir eme stn f ro ht e drow

orp ec oss r. tsuJ as c erro c nt e ss is meani gn el ss iw t tuoh a s ep cificat noi of la wol ed beha sroiv , as f yte is aem lgnin sse wi uoht t a ps ce if ci a it no of haza sdr ot be rp eve tn ed, a dn in rp ac cit e the if tsr s et p fo

safe yt ana sisyl is a wl ays f gnidni a dn cl sa is f gniy haza sdr . ipyT c la ly, zah a dr s are ssa oci eta d htiw mos e tsys em ni hw ci h the os ftware si e bm e dd ed e( . .g , ht e em cid al dev eci ,) ra ht er ht an ht e sof wt a er

a nol e. hT e gnihsiugnitsid fea rut e of saf te y si taht it si c cno e nr ed lno y htiw ht ese haza sdr , a dn ton htiw other as ep c st fo c rro ce t f nu c .gninoit

hT e c no ec tp of s fa e yt si pe hr aps ea is er ot rg a ps w ti h f ilima ar isyhp ac l s tsy e sm . roF exa elpm , la nw -

m wo e sr ni ht e tinU ed tS at se are e ppiuq ed w ti h na tni e olr ck devi ec , os m te emi s ca ll ed a "d ae d- nam tiws c .h " If t sih ws itch si on t a itc ve yl he dl yb ht e po era rot , ht e e nign e tuhs s ffo . hT e d ae d-man

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tiws ch od es ton c no t tubir e in a yn way ot c tu it gn rg a ;ss i st s elo oprup es si ot rp eve tn eht po er ta ro f mor er ca gnih otni t eh wom er lb ades hw li e t eh e nign e .snur

nO e si t me pted to say ht ta safe yt is an a ps ect of c rro ce nt e ss , eb ca su e a doog tsys me s ep cif ci ati no uow ld ur el uo t haza sdr . woH ever, s fa e yt is best c disno ered sa a uq a ytil sid it tcn f or m c erro tc ne ss

a dn er lia ib il yt f ro owt er a snos . Fi tsr , yb foc su i gn no a few haza dr s a dn gnirongi other f nu tc noi al ti ,y a separate saf te y eps ic if ac ti no can be much is mp el r than a c lpmo ete s tsy em eps ic fic ita no , and the er f ero ea is er ot ve ir fy. oT up t ti a hton er wa ,y lihw e a doog tsys me s ep cif ci a noit dluohs ur el uo t haza sdr , we ca onn t be c no f di e tn aht t either s ep cifi ac snoit ro ruo a ett mpts to verify s tsy e sm are doog e hguon ot rp ovide ht e de rg ee of ass ru a cn e we re riuq e f ro haza dr a nadiov ce. eS c dno , even if t eh safe yt ps ce if ci ta noi were re dnud a tn iw th rega dr to the f lu l ys ets m ps e ic fic noita , ti si tropmi a tn beca su e yb( defi )noitin we rega dr a ov idance of haza sdr as m ro e c ur ci la ht an sa sit fyi gn other pa str

fo t eh tsys em eps cifica it .no C rro tce ne ss a dn re il abi ytil are c tno i gn ent nopu mron la po era gnit c snoitidno . tI si ton reas no a lb e ot e epx tc a drow corp se si gn rgorp am ot save cha gn es ron am yll hw en t eh file od es ton f ti ni rots a eg , ro to e epx ct a database to noc it un e ot po erate amron ll y hw en the com up et r ol es s wop er, ro ot e epx ct a

eW b tis e to divorp e comp el te yl sa afsit tc yro serv eci to all siv ti sro hw en ht e load is 001 mit es rg ae ter than ht e am mumix f ro ihw ch ti w sa de ngis ed. oS tf ware ht ta af sli rednu ht e es c idno it sno , hcihw

loiv ate ht e erp im ses fo its desi ng , may ts lli be " rroc ect" ni t eh st cir t se esn , yet the ma nn er ni hw i hc the sof wt are f ia sl is ropmi at tn . It si ca ce tp a lb e ht at ht e drow orp ec ross liaf s to tirw e ht e new if le t tah od es ton f ti no ksid , ub t nu ac ec tp a lb e ot la os c tpurro the rp e suoiv ve noisr of the f li e in the a tt me .tp tI si ca ce tp a lb e f ro ht e ad at sab e s tsy em to c ae se ot f nu itc no hw en the rewop si cut, tub anu cce tp a elb

f ro ti ot leave the tad abase in a c purro t ats et . dnA it si usu la yl rp efera lb e f ro the Web s tsy em to t nru away so em a ivirr ng u es sr ra ht er than b ce gnimo t oo sl wo f ro la l, ro cra gnihs . foS tware t tah rg feca llu y de rg ades ro fai sl " fos lt y" uo tsi ed its mron al po er ita gn paramete sr si r subo t .

oS f wt are fas te y si a dnik of subor tne ss , tub ubor ts ne ss si a rom e gene lar on t noi that c cno e snr ton

ylno a ov idance fo ha draz s (e. ,.g da at c purro it )no tub osla part ai l f cnu t oi na il ty dnu er unu s lau s ti ua snoit . R subo tne ,ss like as ef ,yt be snig iw th e px licit c disno era noit fo usunu al a dn dnu esira lb e s ti ua snoit , a dn dluohs ni c edul a gu em gnitn os f wt are ps ecific ita sno htiw a rporpp ai te re esnops s ot

dnu e is ra elb eve .stn Figure 6.1 ulli rts ates eht relati no a gnom depe dn abi ytil porp e itr e .s

ugiF re 6. :1 Re al noit a gnom depe dn ab li yti porp e tr ei s

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eW are ilpmis fyi gn ma tt e sr os em tahw yb co disn eri gn no ly ps ice fic ita no s of behavi sro . A ps e ic f ci a noit may la os deal htiw hto er porp e eitr s, us ch as ht e ksid caps e re riuq ed ot ni ts la l t eh

a pp li ac noit . A s tsy em m ya suht a osl eb "inc rro e "tc fi ti viol ta es eno fo the es ts ta ci porp ertie .s

sisylanA

nA la sisy cet uqinh se that od ton i vlovn e tca lau ex ce noitu of rgorp am ruos ec c do e lp ay a orp m ni e tn lor e in vo er la l fos tware uq a ytil orp ec ss es. Ma un la psni ce ti no tec nh i uq es a dn a motu ated an la sy es

can be a ilpp ed at a yn deve pol em tn ts age. hT ey are partic lu a ylr we ll suited ta ht e early ts ages fo ps e ic f ci a snoit a dn de ngis , hw ere ht e al ck of e ex c tu ab li yti of ma yn tni e demr ai te a tr i af c st re cud se t eh

efficacy fo te ts i .gn .

E cx re tp fo Web rP esence Fe sa ibi ytil dutS y

esopruP fo siht od c mu e tn

sihT od c mu e tn was rp epared f ro ht e knumpihC TI management et am. tI describ se the res lu st of a fea ilibis ty st ydu dnu ert ka en ot a sivd e C knumpih c ropro ate am nage em nt hw te her ot e bm a kr no a

tsbus a itn al redeve tnempol eff tro to a dd nilno e gnippohs f nu tc ilanoi ty ot ht e C knumpih Com tup e 'sr eW b rp esenc .e

aoG ls

hT e rp ami yr og al of a Web rp ese cn e redeve pol m tne si ot a dd nilno e ppohs i gn f ca itili es. Ma kr eti gn

e mits ates an ni c er ase fo %51 vo er c rru ent dire tc sales htiw in 42 shtnom , a dn an a idd it no al %8 savi sgn ni rid ce t sales roppus t c stso f mor ihs fti gn tele ohp ne cirp e i iriuqn es ot enilno rp ice i uqn i ir e .s [ ]…

rA chit ce ut r la Re riuq e stnem

hT e gol aci l arc tih e rutc e lliw be divided into erht e nitsid tc sysbus te :sm uh am n i tn e fr a ec , sub ine ss l igo c, a dn s roppu it gn ni fra urts tc eru . aE ch am j ro bus s tsy em tsum eb urts ct ru ed f ro hp ased deve mpol e ,tn htiw laitini fe ruta es delivered 6 shtnom f mor cni ep it no , f llu ef ta ru se at 21 m no t ,sh a dn a lp a nn ed rev isi no at 81 shtnom f mor jorp ect ni ec .noitp [ ]…

auQ l yti Re riuq ements Depe badn li ti y W ti h the int udor c noit fo di tcer s la es a dn c motsu er r le ta i pihsno am nag eme tn f tcnu i sno , depe dn abi ytil of Chi 'knump s eW b se vr seci eb c mo es sub ine rcss ti ci al. A c itir ac l c ro e fo f tcnu i ano l ti y liw l be ide itn fied, si alo et d f or m el ss c itir ac l f nu c noit a til y in d ise gn a dn mi p el me tn a ,noit a dn jbus ected ot ht e hgih e ts level fo sc tur i .yn We es amit te ht at this will eb

a amixorpp et yl %02 of new deve mpol e tn a dn re iv is sno , a dn ht ta ht e V&V c stso f ro soht e trop i sno liw l eb a amixorpp let y t elpir eht c tso fo V&V f ro non c ir ti ac l dev le mpo e .tn

libasU ity hT e new Web rp esence lliw eb , ot a um ch rg ae ter e tx e tn than b fe ero , the ilbup c face of

Chipm knu Com tup e .sr [ ]…

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.in S ce u ir yt nI t udor itc no of no l ni e dir ce t dro eri gn a dn b li gnil ra si es a bmun er of es c iru ty ssi ues. emoS of ht e es can be a diov ed ni ti ially yb c rtno ca gnit iw th no e of sever la se ivr ce compani se ht ta vorp i ed s ce ru e cre id t ca dr t snar a tc i no se vr eci s. noN te leh e ss , dro er rt ca ki gn , c motsu er r ale pihsnoit

am nageme tn , ret snru , a dn a bmun er of other f nu cti sno ht ta ac tonn be ffe ce vit e yl ruostuo ec d ra si e si ing f ci a tn es c ru ti y a dn rp ivacy si us es. dI ent yfi ing a dn i los ati gn es c ru ti y c no ce snr iw ll a dd a si ing f ci a tn tub mana eg a elb c tso ot desi ng va adil it .no [ ]…

oS em it m se ht e best a eps c st of ma laun i epsn cti no a dn aut mo ated sta it c anal sy si ac n be tbo a ni ed yb car fe yllu dec opmo is gn porp er it es ot be c eh c dek . roF exa elpm , soppus e a d ise red porp e ytr of re riuq ements od cu em nts si ht at each spe ic al et rm ni ht e a pp li ac ti no od m nia a epp ar ni a gl sso a yr of et rms. sihT p por e ytr is on t di er c ylt ma ena lb e to an a otu am ted st cita a sisylan , cnis e c rru e tn sloot

ca tonn d tsi ugni si h m ae ni gn ful mod ia n te smr f mor hto er ter sm ht ta ha ev the ri dro ina yr aem ni .sgn hT e porp e ytr can eb checked htiw ma laun insp ce noit , tub the corp e ss si et suoid , e px e visn e, a dn

e rorr - norp e. A dirbyh a orpp ca h can be a ilpp ed fi each mod ain term is ma kr ed in the te .tx Ma un a yll c eh cki gn that mod ain terms are ma kr ed si much fa ts er a dn ht er fe ro e l sse pxe e visn e than ma yllaun l gnikoo cae h et rm pu ni the gl so as yr , a dn ma gnikr ht e te smr perm sti eff tce ive auto am noit of c or ss - c eh cki gn w ti h eht glo ass yr .

eT gnits Despite ht e at art itc vene ss of aut mo ated ts a it c ana syl es hw en they are a cilpp a elb , a dn de tips e t eh su ef nlu e ss of ma aun l i psn ce snoit f ro a va ir te y fo od c mu e stn gnidulcni ub t on t mil ti ed ot rgorp am

ruos ec c edo , nyd ma ic t gnitse r iame ns a tnanimod tec euqinh . A solc er kool , hguoht , swohs t tah nyd am ci te ts i gn si rea ll y vid ided into several nitsid ct ca ti iv eit s ht at may co c ru at di ff ere tn op i tn s ni

a orp ject. Tests a er e tucex ed hw en the c rro e gnidnops c do e is ava li a elb , ub t tes it gn ca tiv ti ies s at tr ea ilr er, as noos as ht e art fi ac st re uq ired f ro de gningis te ts case eps cif ci a snoit are av alia elb . ,suhT ac ec tp ance a dn tsys me set t su eti s dluohs be gener eta d b fe ro e inte rg a noit dna nu it t tse us ites, even fi e ex c tu ed in eht etisoppo dro e .r Ea ylr te ts de is ng has several a vd a tn age .s Te ts s are ps e ic fied dni epe dn ent yl f or m c do e a dn hw en t eh c rro e gnidnops os tf ware ps ce if ci a snoit are f hser ni ht e m dni of ana stsyl a dn devel po e sr , fac tili a gnit re iv ew of et ts desi ng . roM e vo er, test cases may hgih il thg inc tsisno encies dna ni com lp etene ss ni the c rro e gnidnops sof wt are ps e ic f ci a it sno . Early de ngis of te ts cas se a osl a swoll f ro early rep ia r fo os f wt are ps ec fi ci ati ,sno rp eve gnitn spec fi ci a noit faul st f or m porp aga gnit to later stages ni

deve mpol ent. Fina yll , rgorp a mm e sr am y esu te ts cases to rtsulli ate dna cla ir fy the sof wt a er ps e ic f ci a ,snoit e eps cia ll y f ro e srorr a dn nu e epx c det c tidno i .sno

oN e nign ee r uow ld iub ld a c pmo el x rts u tc ru e f or m par st that have on t smeht e vl es eb en s bu je etc d ot uq la yti c lortno . J tsu sa the "ea lr ei r is better" lur e id cta et s gnisu i epsn cti no ot rev ae l fla sw ni

re riuq ements a dn ngised bef ro e they are porp agated ot rgorp ma c edo , the sa em lur e id ctates m do elu et ts i gn ot nu c vo er as ma yn rp o rg am fa lu st as ssop ib el eb f ro e ht ey ra e ni c ropro ated ni la gr er

tsysbus sme of the udorp ct. tA pihC - knum , ved e pol e sr are e px ce ted ot perf ro m f lanoitcnu a dn st ur ct ru al m ludo e t tse gni b fe ro e a krow issa nemng t si c sno idered c lpmo ete a dn a dd ed ot ht e orp j ce t ba les ine. hT e te ts vird er a dn a ilixu a yr f li es are trap of the krow udorp tc dna are e px ce ted ot ma ek

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.in reex ce tu i no of et ts c sa e ,s inc gnidul re us lt c eh cki gn , as mis p el a dn aut mo at ci as elbissop , is n ec t eh sa em te ts cases iw ll eb su ed vo er a dn vo er aga ni as eht udorp ct e ov lve .s

pmI r gnivo ht e Pr co se s While ht e a ss e ylbm -li ,en ma ss udorp cti no irtsudni al dom el si ni a rporpp ai et f ro os f wt a er , hw ci h si at el a ts par ylt cu ts om- ,tliub there si somla t a wl a sy mos e c nommo al ti y a gnom orp j ce st dnu e tr aken yb

an gro a zin a noit vo er emit . noC f tnor ed yb mis ali r lborp e sm , deve pol ers te dn ot am ke the s ma e sdnik of e srorr vo er a dn vo e ,r a dn c sno e uq e yltn the as em ki dn s of os f wt are luaf st are tfo en enc tnuo e er d

orp cej t after orp ject. ehT uq la ity orp ce ss , as well as the os f wt are deve pol em tn orp ec ss as a ,elohw can eb mi vorp ed yb g ta heri ,gn ana zyl i gn , a dn a tc gni no d ta a rega gnidr fa stlu a dn fail ru e .s

hT e f tsri part of a corp ess mi rp vo ement ef e abd ck pool , a dn tfo en the mo ts diff ci tlu to mi lp eme tn , si g ta heri gn su ff icie yltn c lpmo ete a dn acc aru te raw data a tuob fa stlu a dn fail ru es. A m ia n tsbo ca le si that data ga ht ered ni no e jorp ect og es m ia nly ot neb e tif hto er jorp ce st ni eht fut ru e a dn may es em ot have il eltt erid ct be en f ti f ro ht e c rru ent ejorp ct, um ch le ss to eht pers sno a ks ed to divorp e ht e war da at . It si ht er fe ro e he pl ful ot tni e rg ate data elloc cti no as we ll as ssop ib el wi ht hto er, ron m la deve mpol ent a itivitc es, such as ve sr noi a dn c no f rugi a noit c lortno , orp ject am nage em tn , a dn gub tr ca nik g. It si osla essen ait l to inim im ze ext ar fe f tro . roF exa elpm , fi re noisiv sgol ni t eh revi nois c no t lor database ac n be a ss oci eta d htiw gub tr ca gnik r ce sdro , then ht e emit be ewt en chec gnik tuo a m ludo e a dn c eh c gnik ti b ca k ni m hgi t be taken sa a guor h diug e ot c tso of repa ri . Raw d ata no fa stlu a dn af il ru es mu ts be a rgg egated into cate irog es a dn roirp ti zi e .d Faul st am y be ac te rog ized a gnol several mid e sn i sno , enon of ht em pe fr ce .t nutroF eta yl , a fla lw e ss c ta e rog iz ta noi is ton cen e ss a yr ; a ll that si en eded is mos e c ta e rog iza noit scheme that si fus ficien lt y f ni e- rg ia den a dn te sdn ot a rgg ega et af u tl s htiw si im lar ca sesu a dn op iss lb e mer e id e ,s a dn ht ta can be a oss c tai ed wi ht at le sa t hguor

e mits ates of re al t vi e fre uq ency a dn c tso . A ms la l bmun er of ac te rog ei s - ma by e j su t no e ro owt - a er c soh en f ro f htru er .yduts

hT e ana yl is s s et p c sno ists of t ar c gni several insta cn es of an sbo e vr ed luaf t ro fail ru e ab ck ot the muh an e rorr f mor ihw ch it re us lted, ro even f htru er ot eht fact sro ht at el d to t tah muh an e rorr . ehT

an la sisy a osl i vlovn es the er a nos s ht e fa lu t was on t dete tc ed a dn le nimi ated ea ilr er e( . .g , woh it s il pp ed hguorht va suoir i epsn cti sno a dn leve sl of tes it )gn . sihT orp c sse si nk nwo as " oor t ca esu an la ,sisy " tub the amitlu et aim si f ro ht e mo ts tsoc -e ff ce tive c tnuo e mr e rusa e, ihw ch si mos eti sem

tub on t a wl ays ht e mitlu ate toor ca esu . roF ex ma elp , ht e pe tsisr ence of sec ytiru nluv erab li iti es t hguorh ub f ef r vo e fr l wo e srorr ni net ow kr ppa l ci a snoit may eb rtta i ub et d at l ae ts pa tr ly to

diw es erp ad esu of rgorp a gnimm la ugn ages wi ht nu c rtsno a ni ed tniop e sr dna iw t tuoh a rr ay sdnuob c eh cki gn , hw ci h may in t nru be att tubir ed to perf ro am n ec c cno e snr a dn a re meriuq ent for in et por erabi ytil iw ht a gral e ydob of legacy c .edo hT e c tnuo e emr a rus e c uo ld vlovni e differenc se ni

rgorp a imm gn em sdoht e( . .g , re gniriuq u es of ec itr fied " as fe" li rb a ir es f ro fub fer manageme )tn , ro mi vorp ements ot uq la yti ssa ru ance a itc v ti ies e( . ,.g a dd ti noi s ot ins ep c noit hc ce lk tsi )s , ro mos etimes

cha gn es ni manageme tn rp tca ci e .s

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.inrO lanoitazinag aF c ot rs

hT e uq a ytil orp ec ss cni l du es a wide var ei yt of a tc tivi ei s ht at re uq ire spe ic fic sk li sl a dn ta it dut es and am y be perf mro ed yb uq a ytil eps ci ila sts ro yb sof wt are devel po e sr . lP a nn i gn ht e uq a ytil orp ec ss

i ovn lves ton ylno re ruos ce management ub t osla di entific ta i no a dn olla ac noit of re ilibisnops ties. A roop a ll co ta noi of re ibisnops il it es ac n el ad to am j ro p bor el sm in hw ci h iusrup t of udividni al og a sl

c lfno i stc wi ht vo era ll orp ej ct cus ec ss . roF exa elpm , ilps tti gn re ilibisnops eit s of dev le mpo e tn a dn uq la yti -c no t lor be wt ee n a deve mpol ent a dn a uq a il ty aet m, a dn rewa gnidr hgih udorp cti iv ty ni et rms

of l ni es of c do e per pers no -m no th rud i gn dev le mpo ent am y cudorp e dnu e ris ed resu tl s. hT e deve mpol e tn te ma , ton rewa dr ed ot cudorp e hgih - uq a il ty os tf wa er , m ya ta t me tp ot am x mi ezi

udorp c ytivit to the de irt em tn of auq til y. hT e re ruos sec i in it la ly p al nn ed f ro uq a ytil a russ a cn e yam ton us ff ci e if the ini it al uq la yti of c do e f mor ht e v" re y udorp ctive" dev le po em tn et am si l wo . nO t eh

other ha dn , combini gn deve pol em tn a dn uq a ytil c no t lor r se ilibisnop t sei ni no e dnu i ff ere itn a det et ma , lihw e a ov idi gn ht e pe vr er es ni ec tn ive fo di div ed r se ilibisnop ties, ac n osla have nu i tn e dedn

e ff ec :st sA d ae dl ni es n ae r, re cruos es may be sh fi ted f mor uq a il ty a russ a cn e to c do i gn , at the e px e esn fo udorp ct uq a ytil . C no f il c gnit c disno er ta snoi s roppu t ob th ht e se rap ta noi of lor es e( . .g , er cr tiu i gn uq a il yt ps ce ai il ,)sts a dn ht e m libo ti y of pe lpo e a dn or sel e( .g, tor a gnit engin ee sr b te w ee n dev le po em tn a dn et gnits at sks ). tA knumpihC , nopser is bi ytil f ro d le vi e yr of the new Web rp ese cn e si tubirtsid ed am gno a

deve mpol ent et am a dn a uq a ytil a ss ru a cn e et am. htoB et a sm are f ru ther a tr ci lu ated otni puorg s. ehT uq la yti sa rus a cn e team is vid ided into the an la sisy a dn te gnits rg o pu , re nops is lb e f ro t eh

depe dn a ib lity of ht e tsys em, a dn the su abil ti y te gnits puorg , res lbisnop e f ro u as ytilib . Re nops is bil yti f ro es c iru ty ssi ues si ngissa ed to ht e ni frast rutcur e deve mpol e tn puorg , hw ich re il es part yl no e tx e nr al c tlusno ants f ro final t tse s based no e tx e nr al at at ck ta te .stpm Ha gniv di nits tc teams od es on t ylpmi a lpmis e sivid i no of lla ta sks be ewt en te ma s yb c ta e yrog . At Chipm knu , f ro exam elp , ps e ic f ci a snoit , de ngis , a dn c edo are insp ce ted yb im xed et a ;sm cs affoldi gn a dn ro ca les are de ngis ed yb ana syl ts a dn dev le srepo ; tni e rg ita no , s sy et m, acce tp a ecn , a dn re rg e oiss n et sts a er ngissa ed ot ht e t tse a dn anal sy si ;maet nu it te sts are gene ar et d a dn e ex c tu ed yb the

deve pol e ;sr a dn c vo erage si ch ce ked yb the te ts i gn maet eb f ro e ts art gni in et rg a noit a dn s tsy em et ts i gn . A ps ce ia tsil has b ee n rih ed f ro an la zy i gn af ul st a dn im vorp ing ht e orp c sse . hT e corp e ss mi vorp ement eps ci ila st krow s ni cr eme tn la ly hw eli deve nipol g ht e sys tem a dn soporp es mi vorp ements ta each rel ae .es

E ex cr si es

We have ts ated that %001 rel ai ilib ty si ugnitsidni si ha lb e f mor c erro c nt e ss , tub ht ey are ton tiuq e ide itn c la . dnU er hw at ic rcu sm ta cn e im hg t an inc rro ce t rgorp ma be %001 r le ia ?elb

Hint: Reca ll ht at a gorp ram may be rom e ro le ss r aile lb e depe dn i gn no woh ti si su ed, tub a rgorp am si e hti er c erro ct ro ni c rro ce t rega ldr e ss fo su age .

We hgim t aem rus e the re il abi ytil of a ne krowt tuor er as the f ar cti no of a ll p ca ke st ht ta a er c rro ce tly uor et d, ro as the frac it no of tot la se ivr ce mit e in ihw ch ap cke st are c erro c ylt tuor e .d When hgim t the es t ow m sae ru es eb ffid ere tn ?

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4.1

4.2

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If I am olnwod adi gn a v re y large f li e vo er a ls wo dom em, od I ac re m ro e a uob t t eh ava li a ib l yti fo ym i tn e tenr se ivr ce vorp ider ro its aem n emit eb t ew en fail ru es?

Can a ys ets m eb c erro ct a dn yet asnu fe?

dnU er tahw circum ts anc se ac n am gnik a ys ets m rom e as fe am ek ti le ss er l elbai ?

oS f wt are a cilpp ati no od iam ns can be cha ar cte zir ed yb the rel ta vi e tropmi a cn e of sche elud ( ac le dn ar mit e), t to al c tso , a dn depe dn abi ytil . roF exa pm le, lihw e all t rh ee are tropmi a tn f ro game sof wt a ,er sche lud e gnippihs( udorp ct in Sep et bm er to eb avai al lb e f ro oh l di ay

rup chas se ) has pa tr ci u al r w ie hg t, hw ile depe dn a ib il yt ac n be os em hw at relaxed. hC a ar cte ezir a od m nia uoy a er fa im li ra htiw in ht e es te .smr

disnoC er res visnop en se s as a ed is ra lb e porp erty of an tnI e nr et chat rgorp am. hT e inf lamro re riuq ement is ht at me ss ages pyt ed yb each bmem er fo a chat se ss i no a pp ae r tsni an at ne ylsuo

no t eh d si lp ays fo hto er su ers. eR fi en siht inf mro la re riuq eme tn otni a c no crete s ep c cifi a noit ht at ac n eb ve ir fie .d sI a yn thing tsol ni t eh er fineme tn ?

dI e itn fy emos c erro c nt e ,ss ubor ts ne ss a dn as f yte porp er eit s fo a drow ecorp ss .ro

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4.3 4.4

4.5

4.6 4.7

4.8

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UNIT 7

F UA TL -BA ES D TSET ING, TSET EX CE U IT ON A m do el of top en it la rgorp am fa stlu si a v la ua lb e ruos ec of ni f mro a noit f ro ev la ua gnit a dn de ngis i gn et ts s iu et .s oS em fa lu t lwonk e gd e is oc m nom ly u es d ni f nu c noit la a dn urts c larut test ,gni f ro exa pm el hw en di entif gniy lgnis te no a dn e orr r values f ro par ma te er cha ar cte tsir ci s in cate yrog - par it noit t gnitse ro hw en lupop a gnit c ta a gol s htiw e norr e suo v la ue ,s tub a af ult m do el can also eb

su ed erom erid c ylt . Fa tlu -based t se ti gn u es s a fa tlu m do el di er c ylt to htopyh esize op et itn al fau tl s ni a rgorp ma dnu er test, as we ll as to c er ate ro eva ul ate test tius es based no i st e ff ci acy ni det ce gnit t esoh opyh the it c la faul .st Over weiv

gnE in ee sr st ydu fa ruli es ot dnu er ats dn woh ot rp eve tn mis ilar fail ru es in the fut eru . roF exa ,elpm fa li ru e of the T ca mo a sworraN gdirB e ni 0491 led to new dnu e sr at dn ing fo so ic l al ti no in hi hg dniw a dn ot t eh udortni c noit of a an l sy es to rp edi tc a dn rp eve tn us ch de urts ctive so lic l ita no in bus es uq e tn

gdirb e de ngis . hT e ca sesu of an ai lr ine cra hs are like siw e e tx en is ve yl iduts ed, a dn hw en rt aced ot a st ur ct ru la f ia l ru e they fre uq ent yl re lus t ni a rid ec it ve to a pp ly d ai ng o its c te sts ot a ll ria c tfar c disno ered top entially uv lnera elb ot limis ar fa .seruli

pxE e ir ence wi ht c nommo sof wt a er fa stlu mos e mit es leads to mi vorp e stnem in de is ng met sdoh a dn rgorp a imm gn la ugn ages. oF r exa elpm , ht e ma ni soprup e of aut mo a it c em m yro am nag eme tn ni Ja av

si ton ot spare the rgorp am em r ht e t buor el fo re el a gnis sunu ed em yrom , tub ot rp event t eh rgorp a emm r f or m maki gn ht e dnik of me yrom management e rorr s d( a ilgn ng tniop e sr , re dnud ant

d ae lloc ita ,sno a dn m me yro ael )sk that fre uq e yltn occ ru in C a dn C ++ rgorp a sm . motuA ta ic a rr ay sdnuob c eh c gnik ac tonn rp eve tn a rgorp a mm er f mor gnisu an i dn ex e rpx isse no stuo ide a rr ay sdnuob , tub can ma ek it much l se s il ke yl ht ta ht e fa tlu e cs apes de et c noit ni tes it gn , as lew l as

mil gniti eht damage rrucni ed if it od es el ad to po era noit la f ia erul (e nimil ta ,gni ni pa tr ci u al r, t eh ub ffer vo erf wol a att ck as a mea sn of vbus er it gn rp i liv eged rgorp a )sm . epyT ch ce ki gn r aile ylb

de et c st ma yn hto er fa lu ts gnirud rgorp am rt a lsn ta .noi 7.1 snoitpmussA ni uaF lt-B sa ed T se gnit

hT e ef ef tc vi en sse of af tlu -based t tse gni depe sdn no the ilauq ty of ht e fa tlu m do el a dn no mos e b sa ci a snoitpmuss a uob t ht e re al t noi of the es eded stluaf ot af lu st ht at im thg ca ut lla y be rp ese tn . nI rp ca ecit , the es eded af lu ts a er ams ll nys ta tc ic hc a gn es, l ki e re lp a gnic no e var ai lb e fer ere cn e yb

a on ther ni an e rpx e ,noiss ro cha gn i gn a c pmo a nosir f mor < to =< . We yam h htopy esize ht ta ht e es a er re rp ese tn ati ev fo fa lu ts ac aut l yl rp ese tn ni t eh rgorp a .m

uP t a hton er way, if ht e rgorp am dnu er test has na ac ut la fa tlu , we may htopyh e zis e ht at ti id ffe sr f or m a hton er, c rro ec et d rgorp am yb no ly a ms la l et utx la cha gn e. If os , then we n ee d em re yl d itsi hsiugn ht e rgorp ma f mor all us ch ms all var ai tn s yb( se el cti gn et ts ca es s f ro hw ci h e hti er the

giro inal ro ht e va ir ant rgorp am fa )sli ot e rusn e de et cti no of all us ch af u tl s. hT si si nwonk as t eh

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.in c pmo ete tn mmargorp er hy ehtop is s,an a noitpmuss th ta the rgorp am dnu er et st is "c sol e ot " (in t eh se esn fo te utx al fid ef re cn e) a c rro ce t rgorp a .m

tluaF -B sa ed Tes :gnit T re mi on l ygo

rO lanigi pr go r ma ehT rgorp ma tinu e( . ,.g C f nu cti no ro Java cl ssa ) to eb t tse e .d Pr go r ma ol c noita A re noig ni the cruos e c do .e hT e erp ic se ed f tini i no is ed fined rel ta vi e to eht

tnys ax of a par cit lu ar rgorp a mm i gn al ugn age. ipyT lac col a snoit are ts ta e em stn , a mhtir et ci and B oo lean e rpx e ,snoiss a dn corp e erud c .slla

tlA re tan e e rpx e noiss ruoS ec c edo et tx that can be legally bus ts i tut ed rof ht e text at a rgorp am loca it on. A tsbus ti ut noi is leg la if ht e re tlus i gn rgorp am si s tny itca cally c rro ce t i( . .e , it c selipmo

tuohtiw e .)srorr

tlA re tan e pr go r ma A rgorp am bo at ni ed f mor ht e ro iginal rgorp am yb itsbus tu it gn an a tl e tanr e e rpx es nois f ro t eh te tx at emos rgorp ma l co ati .no

tsiD inct be vah ior of na tla re tan e pr go r ma R for a te ts t ehT beha roiv of an alte nr ate rgorp am R si nitsid ct f or m ht e be vah i ro of the ro iginal rgorp am P f ro a te ts t,if R dna P udorp ec a fid fere tn

result f ro t, ro fi t eh tuptuo fo R is ton ed f ni ed f ro t.

tsiD i iugn shed set of alt re tan e pr go r sma for a te ts tius e T A set of al et nr ate rgorp a sm are nitsid ct fi ae ch al et nr ate rgorp am in ht e set can be di ugnits i hs ed f mor the ro iginal rgorp am yb at least eno et ts in T.

Fa tlu -based te gnits ac n ug ara tn ee fau tl ed te tc noi ylno fi ht e competent rgorp ma mer htopyh e is s and the c lpuo i gn fe fect htopyh ise s oh ld. tuB ug ara tn e se a er m ero than ew e epx ct f mor other a aorpp ches to desi gning ro eva ul ta gni et st tius es, ni c gnidul the st ur ct ru la a dn f cnu it no la te ts ade auq cy c tir e air d csi ssu ed ni ae rlier cha tp e sr . Fa lu t-based te gnits t ce nh i uq es ac n be esu f lu even fi we ed c nil e to ta ek the el ap of faith re riuq ed to f yllu ca ce tp rieht dnu e iylr ng a tpmuss i sno . Wh ta si e ss e itn al si ot rec ngo ize the depe dn e cn e of seht e tec uqinh es, a dn a yn ni fere cn es a uob t os ftware uq a ytil b esa d no af u tl -based te ts gni , no ht e auq l yti of ht e af tlu m do el. sihT a osl ilpmi es ht ta deve gnipol b ette r fault

m do e ,sl based no ha dr atad a tuob er al fa lu ts r ta her ht an ug esse ,s si a doog vni e mts e tn fo fe f .tro 7.2 M tatu i no sisylanA

tuM a noit a sisylan si ht e m tso co mm no f ro m of os ftware fa lu t- sab ed te its gn . A fau tl dom el si su ed ot udorp ec opyh the it c la fa lu ty rp o rg a sm yb cr ae gnit var ai stn of the rgorp am dnu er et ts . Va ir an st a er

c er eta d yb " es e gnid " fa ,stlu ht ta i ,s yb maki gn a s am ll cha gn e to t eh gorp ar m dnu er te ts f wollo i gn a pa tt e nr ni the af tlu m do el. hT e pa tt e snr f ro cha gn ing rp o rg am et tx are llac ed tatum i no po re ators, a dn e ca h va ir a tn rgorp am si c la led a um t na t .

M noitatu isylanA s: T mre i golon y

rO lanigi pr go r ma dnu er test ehT rgorp ma ro orp ec erud (f nu ct )noi to eb te ts e .d M natu t A rp o rg am aht t di ff e sr f mor the giro in la rgorp ma f ro no e nys at c it c eleme tn e( . .g , a s at teme ,tn a c ,noitidno a va ir a ,elb a labe .)l

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tsiD i iugn shed mu at nt A tum a tn that ac n be ugnitsid i hs ed f ro ht e ro i anig l rgorp am yb exec gnitu at el ast eno et ts c sa .e

E laviuq e tn tum a tn A tum a tn that ac tonn eb hsiugnitsid ed f mor t eh nigiro al rgorp a .m M noitatu op re ota r A ur el f ro cudorp i gn a um at nt rgorp am by s ny cat it c ylla idom fying the nigiro al

rgorp ma .

tuM a stn dluohs be lp a bisu el as fa ytlu rgorp a sm . tuM a tn rgorp a sm ht at a er jer ected yb a compile ,r ro that fa li a tsoml all test ,s are on t doog dom els of the af lu st we es ek ot nu c vo er w ti h s tsy ema cit et ts i gn .

eW say a tum ant si valid if ti is s atny c cit a yll c rro ce t. A mu at tn bo t nia ed f mor ht e rgorp am of rugiF e

7.1 yb bus its gnitut elihw f ro ws itch ni ht e ts ta me e tn at il ne 31 dluow ton be va dil , nis ce it dluow resu tl ni a c lipmo e- emit e rorr . eW say a tum a tn si esu f lu fi , ni a tidd i no ot be gni va ,dil i st beha oiv r diffe sr f mor ht e beha roiv of ht e ro i nig al rp o rg ma f ro on m ro e than a ams ll s sbu et of rgorp ma te ts cases. A m tu ant tbo a deni yb bus st gnituti 0 f ro 1 000 in ht e ts atement at line 4 dluow be v la di , ub t

ton esu f lu , cnis e ht e m tu a tn dluow be ugnitsid i hs ed f mor eht rgorp am dnu er test yb a ll tupni s a dn t suh uow ld ton vig e a yn esu f lu inf mro a noit no ht e e ff e vitc ene ss fo a te ts us i et . eD f gnini tum a oit n

po era srot ht at ecudorp valid a dn su fe lu m tu a snoit is a non t ivir al ta .ks 1

2 **/ vnoC e tr each li en morf st dradna tupni /* 3 ov id tra udsn ec )( { 4 ed# fine B NELFU 0001 5 c ah r fub [B ELFU N]; */ A cc etalumu enil otni t sih ub ffer /* 6 int ;0=sop /* dnI ex f ro en xt c ah racter in ffub er /* 7

8 c ah r ni Cha ;r /* eN xt c arah cter f mor tupni */ 9

10 ni t a RCt = ;0 */ =0 "w ti hin ,"enil =1 " anoitpo l SOD "FL /* 11

12 while hCni(( ar = ge ct ha ))(r =! FOE ) { 13 s iw tch (inCha )r { 14 c esa FL : 15 if (atC )R { */ pO it lano SOD FL /* 16 a RCt = ;0 17 } e esl { */ nE c etnuo er d CR iw niht li en /* 18 me ,fub(ti op s ;) 19 ;0=sop 20 } 21 rb eak; 22 c esa C :R 23 e ,fub(tim op s ;) 24 ;0=sop 25 a RCt = ;1

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.in 26 rb eak; 27 ed f lua t: 28 if sop( => NELFUB - )2 fa li ("Buffer vo erf wol " ;) 29 ub f[ ]++sop = hCni a ;r 30 }/* sw ti ch /* 31 } 32 fi sop( > )0 { 33 e im t ,fub( ;)sop 34 } 53 }

ugiF er 7. :1 rgorP am tra cudsn e c vno e str enil e dn i sgn a gnom nU i ,x SOD , a dn aM c ni t hso

c vno enti no s. hT e m ia n orp ce ,erud ihw ch es le tc s t eh tuptuo l ni e e dn c vno e tn i no , a dn eht uptuo t orp ec erud emit a er ton .nwohs

Sin ec tum an st tsum be va dil , tum ita no po era rot s are nys cat it c tap et snr defined rela vit e ot partic ralu

rgorp a imm gn la ugn ages. Figure 7.2 swohs os em tum a noit po e ar t sro f ro ht e C la ugn age. C sno tr ia stn are a oss ciated htiw m noitatu po era rot s to diug e se el c noit of test cas se l yleki to di ugnits si h m tu a stn f or m ht e giro in la rgorp am. roF exam elp , ht e tum a it no po er ta ro vs r s( ac lar var ai lb e re lp aceme )tn nac be a pp lied ylno to vari ba el s fo c pmo ati elb pyt e ot( be v la ,)di a dn a te ts ac se ht at ugnitsid i hs es t eh mu at tn f mor the ro igi lan rgorp am m su t e ex c etu the ifidom ed ts a et em tn ni a ts ta e in ihw ch eht

giro inal var ai elb a dn i st itsbus t etu ha ev id ffere tn v la .seu

pO ne ta lb e as pO re ota r De cs ir tp i no tsnoC r nia t rps ae hsd e te DI

pO e dnar ifidoM cat snoi

c pr c tsno ant for c tsno a tn re alp ce c tsno ant C1 tiw h C1

≠ C2 replac me ent c no sta tn C2

scr sca al r for c tsno a tn re alp ec c no sta tn C w ti h s ac lar C

≠ X replac me ent va ir able X

acr a rr ay for c no ats nt re alp ec c tsno ant C htiw a rr ay C

≠ A[I] replac me ent reference A[I]

scr urts ct for c tsno a tn re alp ec c tsno ant C w ti h urts ct C

≠ S replac me ent fi le d S

vs r sca al r va ir a lb e re alp ec sca al r var ai lb e X w ti h a X

≠ Y replac me ent sc la ar var ai elb Y

rsc c tsno ant for cs alar re alp ec sca al r var ai lb e X w ti h a X

≠ C var ai elb re alp c me e tn c no sta tn C

rsa a rr ay f ro s ac lar va ir a elb re alp ec s ac lar va ir ab el X htiw X

≠ A[I ] replac me ent an a rr ay reference A[I]

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pO ne ta lb e as pO re ota r De cs ir tp i no tsnoC r nia t

rps ae hsd e te DI

ss r urts ct f ro cs alar re alp ec s ac lar va ir ab el X htiw X

≠ S replac me ent urts ct if dle S

vie sca al r va ir a lb e re vom e itini a zil a noit fo a scalar aitini l zi a oit n va ir able

mile ni a noit

car c tsno ant f ro a rr ay re alp ec a rr ay fer ere cn e A[I] A[I]

≠C replac me ent wi ht c no sta tn C

sar sca al r for a rr ay re alp ec a rr ay fer ere cn e A[I] A[I]

≠C replac me ent wi ht cs alar var ai elb X

c rn c pmo ara lb e a rr ay re alp ec a rr ay r efe re cn e htiw a replac me ent compara elb a rr ay fer erence

sar urts ct for a rr ay re alp ec a rr ay fer ere cn e A[I] A[I]

≠S reference replac me e tn wi ht a st tcur f ei dl S

Ex rp se si no ifidoM ca it sno

abs a etulosb va eul sni e tr i no re alp ce e yb a (sb e) e < 0

a ro a mhtir te ic po era ot r re alp ce a emhtir it c po erat ro ψ e1ψe2

≠e1φe2 replac me ent wi ht ar ti mh e cit po era rot φ

lcr gol laci c nno ce ot r re alp ec gol ci la c otcenno r ψ e1ψe2

≠ replac me ent wi ht l cigo al c enno tc ro φ e1φe2

or r relati no la po era ot r re alp ce rela noit al po era rot ψ e1ψe2

≠ replac me ent wi ht r le lanoita po e rotar φ e1φe2

ou i nu a yr po era rot sni e noitr sni e tr nu a yr po era rot c rp c tsno ant f ro rp e id ac te re alp ce rp e cid ate iw th a

replac me ent c no sta tn v la eu

tatS eme tn ifidoM cat snoi

lds ts ateme tn led e noit d le ete a ts ateme tn s ac ws cti h ac se rep al ce eht label fo no e ac es

replac me ent wi ht a on ther

ses e dn block tfihs vom e } no e stateme tn ae ilr er a dn al et r

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ugiF er 7. :2 A sa lpm e es t of tum a noit po er ta sro rof ht e C al ugn a eg , htiw a coss ai ted c rtsno a stni ot select et ts ca es s that sid t hsiugni generated tum a stn f mor eht ro iginal rgorp a .m Ma yn of ht e tum an st of Figure 7.2 ac n be a pp lied e auq ll y we ll ot other orp ce rud al la ugn ages, tub ni general a m tu ati no po e rotar th ta udorp ces v la di dna u fes ul um at stn f ro a given al ugn age may on t a pp ly to a different la ugn age ro am y udorp ec i vn la di ro u es le ss tum ants f ro a on ther la ugn a eg . roF exa elpm , a tum a it no po era rot aht t re vom es the f" ir e dn " ke drowy f or m ht e d ce la tar i no of a C ++ cla ss

uow ld ton eb a pp il ca elb ot Java, ihw ch seod ton inc edul f ir e dn cla ss e .s 7.3 tluaF - saB ed dA e auq cy Cr ti ire a

viG en a rgorp ma a dn a t tse us i et T, tum ita no a an l sisy c stsisno fo t eh f gniwollo ts e :sp Select mu noitat oper ota rs fI ew are i tn eres et d ni eps cif ci lc a ss es fo fa ,stlu ew may s ele ct a set fo mu at ti no po e ar t sro r ele tnav ot esoht faul .st

eG n re ta e tum a stn tuM an st a er gene tar ed m hce anical yl yb a ylpp i gn mu itat no po er ta ro s ot eht giro inal rgorp a .m

tsiD i iugn sh tum a stn Ex ce tu e ht e nigiro la rgorp am a dn e ca h gener eta d um at tn wi ht the et ts cases ni

T . A tum a tn si llik ed hw en ti ac n eb di ugnits si hed f mor eht nigiro al rgorp a .m Figure 7.3 s swoh a sa lpm e of mu at stn f ro gorp r ma rT ans ud ce, tbo a ni ed yb a pp l iy ng the tum a tn po era srot ni Fi rug e 61 .2 S( ee 7.8. .)3 T se t etius TS

pO ne Op re ta lin Or /lanigi Mut 1 1 2 2 2 nE L no Mix at lb e as ro e na t U D U D M d g ed

rps ae hsd ee t DI

Mi or r 28 sop( >= - - - - - - - - BUF NEL –2)

sop( == BUF NEL –2)

Mj or r 32 sop( > 0) - x x x x - - - sop( => )0

Mk lds 16 atCR = 0 - - - - - - - - on thi gn

Ml rss 16 atCR = 0 - - - - - - - x sop = 0

pO ne ta lb e as De cs r oitpi n Test De cs r tpi i no

rps ae hsd e te eT ts c esa c esa

egaP 1 38

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pO ne ta lb e as De cs r oitpi n Test De cs r tpi i no

rps ae hsd e te eT ts c esa c esa

1U nO e enil , nU xi M2 owT nil e ,s caM line-e dn il ne-e dn

1D nO e enil , OD S nE d La ts l ni e on t te nimr ated wi ht il ne-end line-e dn se uq e ecn

2U wT o l ni es, nU xi oL ng Very long il ne rg( ae ter than il ne-end ub ffer le gn th)

2D wT o l ni es, OD S Mixed xiM of SOD a dn nU ix nil e e sdn il ne-end ni eht sa em file

ugiF er 7. :3 A sa pm el s te of tum an st f ro rgorp am udsnarT ce generated iw th tum a noit po er ta sro

f mor rugiF e 7.2 x i idn ac tes eht tum a tn si kil el d yb eht te ts ca es in eht c ulo mn h ae .d k lli s Mj, hw ci h ac n be di ugnits i hs ed f mor the giro in la rgorp am yb et ts c sa es 1D,2U, 2D, a dn 2M.

tuM a stn Mi, Mk, a dn Ml are on t dist ugni si hed f or m the ro iginal rgorp am yb any te ts ni ST . eW s ya that m tu a stn ton k li led yb a t tse us i et a er live . A m tu a tn ac n r me nia live f ro owt r ae :snos hT e m tu a tn can be di hsiugnits ed f or m ht e ro i nig al rgorp a ,m tub the te ts tius e T od es ton

c atno in a t tse case aht t sid it hsiugn se th me .e.i( , the te ts us i et si on t ade tauq e htiw resp ce t ot t eh m tu a .)tn

hT e tum ant ac tonn be tsid i hsiugn ed f or m ht e igiro n la rgorp am yb na y et ts case .e.i( , t eh mu at tn si e uq ivale tn to eht ro i nig al rgorp a )m .

viG en a s te of tum a stn SM a dn a te ts tius e T, ht e fr ca noit of non e laviuq e tn tum a stn ik lled yb T em rusa es ht e ade auq cy of T htiw re ps ce t ot MS . nU f ro t anu et yl , t eh orp lb em of di e itn fying

e uq ivale tn tum an st si dnu ce di a lb e ni general, a dn we c dluo e rr htie er yb lc mia gni aht t a m tu a tn si e uq ivale tn to ht e rgorp am dnu er t tse hw en ti si ton ro yb c tnuo i gn os em viuqe ela tn tum a stn a gnom t eh rema gnini vil e tum a tn s .

hT e ade auq cy of the te ts ius et ST eva taul ed htiw re eps ct to ht e f ruo tum a stn of rugiF e 61 .3 si .%52 woH ever, we can ea is ly sbo e vr e ht ta tum ant Mi si e viuq ela tn to ht e nigiro la rgorp am i( . .e , on tupni uow ld di ugnits i hs i )t . vnoC er es yl , mu at stn Mk dna Ml seem to be uqenon ivalent ot ht e ro igi lan

rgorp a :m hT ere dluohs be ta el a ts no e te ts c sa e ht ta di ugnits si hes e ca h of t eh m f mor the ro i nig al rgorp ma . suhT t eh ade uq ca y fo ST , em a rus ed af et r mile ni a gnit t eh e uq iva tnel um at tn Mi, is .%33

tuM a tn Ml si killed yb t se t case iM ex d, ihw ch re rp esents the usunu la ca es of an tupni f li e c tno a gnini htob SOD - a dn nU ix-te mr inated lines. We dluow e epx ct aht t xiM ed dluow al os ik ll Mk, tub siht

od es ton ac ut lla y ha :nepp htoB Mk a dn the nigiro la rgorp ma udorp ec ht e as em user lt for M xi ed. sihT ha pp e sn eb ac su e htob the mu at nt a dn ht e nigiro la rp o rg ma iaf l ni the as me way.[1] hT e su e of a

s mi p el ro acle f ro c eh c gnik the c rro ce nt sse of the stuptuo e( . .g , c eh c gnik cae h tuptuo aga tsni na

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.in e epx cted uo t )tup dluow er v lae the fault. hT e te ts us ite ST 2 bo iat ned yb a gnidd te ts case M dexi ot TS

uow ld eb %001 ade uq a et r( elati ev ot t sih set fo tum an st ) after re vom i gn eht faul .t

M noitatu isylanA s .sv tS ruc ut r la Te gnits

roF t ipy lac s ste of s tny ca it c tum ants, a m tu ita no -ade uq eta et ts suite lliw a osl be ade uq ate htiw res ep ct to is m lp e curts t ru al c tir e ir a such as ts a met ent ro rb anch c vo erage. tuM ati no ade auq cy can s mi ul ta e a dn bus su em a urts tc ru la c vo erage criteri no if the s te of tum stna can be k li led ylno by sa sit fyi gn the c rro e dnops i gn et st c vo erage bo il ga snoit . tS ta me e tn c vo e gar e ac n be lumis eta d yb a pp lyi gn the tum a noit po er ta ro lds ats( et ment led te )noi to ae ch ats t me e tn of a rgorp am. oT llik a mu at tn esohw no ly diff re e cn e f mor the rgorp am dnu er test si ht e a sb e ecn fo st ta eme tn S re uq ires e ex cut gni the tum ant a dn the rgorp am dnu er te ts w ti h a te ts case that xe ce tu se S in the ro igi lan

rgorp ma . suhT ot llik all tum a tn s generated yb a pp lyi gn ht e po era rot lds ot ts atements of t eh rgorp am dnu er et st, we n ee d a t tse ius et ht at ca su es the e ex c noitu of ae ch ts atement in the ro i nig al rgorp ma . rB anch c vo e gar e can be s mi lu ated yb a ylpp i gn ht e po era rot c rp (c no ats tn f ro rp e id c eta

replace em )tn ot lla rp e id ac tes of the rgorp am dnu er et ts iw th c tsno a stn urT e a dn aF sl e. oT ki ll a mu at tn ht ta di ff ers f or m ht e rgorp ma dnu er et ts f ro a rp ed ci a et P s te to the c tsno ant v la ue slaF e, ew n ee d ot e ex c tu e the mut na t a dn t eh rgorp am rednu te ts htiw a t tse ca es taht ca sesu ht e e ex c noitu fo the urT e branch of P. oT llik a tum ant aht t di ff e sr f mor the rgorp am dnu er te ts f ro a rp e cid ate P s te to ht e c tsno ant v la ue True,we en ed ot e ex c tu e the mu at tn a dn the rgorp am dnu er te ts wi ht a et ts ac es that ca su es eht e ex c noitu fo eht aF sl e rb anch fo P. 7.4 raV tai i no s no M noitatu anA ly is s

hT e m tu a noit an la sisy orp ec ss described ni ht e rp ecedi gn es c snoit , ihw ch ki sll tum ants based no t eh stuptuo cudorp ed yb e ex c noitu of test cases, si nwonk sa gnorts mu at ti .no tI ac n generate a bmun er

of tum a tn s uq a rd ta ic in ht e is ze of ht e rgorp ma . E ca h m tu a tn tsum be com lip ed a dn e ex c tu ed w ti h each test ac se nu til ti is ik ell d. hT e t mi e a dn ps ace re uq ired f ro com ip il gn a ll tum ants a dn f ro e ex cut gni all t tse ac ses rof each m tu a tn am y be rpmi ca tical. hT e c tatupmo i no al e ff tro re uq ired f ro mu at ti no an la sisy can be re cud ed yb d ce er a gnis the bmun er of tum a stn generated a dn ht e bmun er fo et ts cas se to be e ex c tu ed. W ae k tum ita no ana sisyl d ce er a es s the bmun er of et sts to be e ex c tu ed yb

k lli gni tum a tn s hw en t yeh udorp ec a ffid erent retni em di eta ats t ,e ra ht er t nah w tia gni f ro a di ff erence in eht if nal r se tlu ro vresbo a lb e rgorp am beha roiv . ew ak m tu ati no ana sisyl W hti w ae k mu at noit , a gnis el rgorp ma ac n be dees ed htiw ma yn fa lu ts. A "meta tum a tn " rgorp am is di div ed otni se mg e stn c tno ia ni gn nigiro la a dn tum ated s cruo e c ,edo htiw a em cha sin m ot sele tc

ihw ch seg em stn ot e ex c etu . owT c ipo es of the tem a- tum ant are exec tu ed ni ta dn em, no e htiw ylno giro inal rp o rg am c do e s le e tc ed a dn the other iw th a s te of vil e muta stn se el cted. xE ce ut noi si pa desu

af et r ae ch se mg ent ot oc mpare ht e rgorp am ts ate of ht e owt ve snoisr . If the st ta e si e viuq ale ,tn e ex cut noi res mu es iw th ht e ne tx se mg ent fo nigiro la a dn atum et d c .edo If ht e ts ate ffid e ,sr t eh mu at tn si ma kr ed as ed ad, a dn e ex cu it no of iro nig la a dn tum ated c edo is re ts a tr ed iw th a new selec noit fo live tum ant .s

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sE tamit i gn upoP l ita on Siz se

uoC n gnit f hsi Lake Wi nn e num c ih e si hni a detib yb t ow sdnik of sif h, a na vit e t tuor and an int udor ec d s ep cies of c buh . hT e F si h a dn W ildli fe Se ivr ec hsiw es to esti am te ht e lupop a snoit ot eva ul a et the ri fe f stro to re adica et t eh c buh iw t tuoh har im gn eht alupop t noi fo vitan e uort t .

hT e lupop ita no of c buh can be e mits ated s at t tsi ci la yl as fo ll swo . 0001 c buh are n te ted, ht ie r asrod l f sni are am kr ed yb a gnihcatt a tag, ht en they are released ab ck otni ht e ekal . vO er ht e ne tx w ee ,sk f hsi e mr en are a ks ed to re rop t the un mber of at gg ed a dn nu at gg ed c buh ac thgu . If 05 ta gg ed c buh a dn 003 tnu a gg ed c buh ra e ca ,thgu ew ac n c la c alu te

a dn suht the er a er a tuob 0006 tnu a gg ed c buh mer ia ni gn ni t eh la .ek tI may be t me pt gni to la os a ks fi hs e mr en to re rop t the un mber of t tuor ac thgu a dn ot perf ro m a

s imi lar c la c lu ita no ot e mits ate the ar it o b te w ee n buhc a dn uort t. woH ever, siht is v ila d ylno if t tuor a dn c buh are e uq la yl e ysa to ac tc ,h ro if no e ac n adj tsu the ar t oi su i gn a nwonk dom le of t tuor a dn c buh uv lnera ilib ty ot fi hs i gn .

uoC n nit g er laudis f ua lts A mis ali r orp ec rud e can be su ed ot e ts imate ht e un mber of fa stlu ni a rgorp a :m S ee d a vig en un mber S of fa stlu ni the rgorp am. Te ts ht e rgorp ma htiw os em te ts s tiu e and

c nuo t the un mber of re ev ela d faul st . eM a rus e ht e un mber of es eded fau tl s de et cted, DS, a dn la os t eh bmun er of rutan la af u tl s DN detected. tsE imate the latot un mber of fau tl s r me gninia ni the rgorp am,

a gnimuss ht e et ts tius e is as ef ef ctive at f gnidni n ta ru la fa stlu sa ti is ta fi dn i gn seeded fa stlu , gnisu t eh f alumro

If we es it mate the rebmun of faul st er am gnini in a rgorp am yb d ete gninimr the noitroporp of s ee ded af u tl s de et cted, ew tsum be wa yr of ht e pi ft la l of e mits a gnit tuort lupop at noi yb c nuo it gn c buh . ehT

s ee ded af lu st are c buh , ht e re la fa lu st are tuort , dna we mu ts e hti er have doog r ae nos f ro belie gniv the es eded faul st are on ae is er to de et tc ht an real rema gnini fa lu t ,s ro esle make ade uq a et a woll a cn es f ro cnu e tr ainty. hT e ffid eren ec si t ah t we ca onn t a ov id ht e lborp em yb repea it gn t eh

orp ec ss w ti h uort t - ecno a fault has eb en de et tc de , ruo wonk el gd e of sti rp esen ec ac tonn be era .des eW depe dn , ht e er f ero , no a v re y doog fa tlu dom e ,l os ht ta the c buh are as re rp ese atn it ve sa lbissop e

of t .tuor Of c ,esruo if we su e ps e ic al iab t f ro c buh , ro de ngis te ts cases ot detect pa itr cu al r es e ded af u tl ,s then ts a citsit al e mits a noit fo eht tot al lupop ati no fo hsif ro e srorr ac tonn eb j tsu fi ie .d

Hardware uaF lt- sab ed Te gnits Fa tlu -based tes it gn is diw ely u es d f ro se im c udno ct ro a dn ha wdr are metsys va dil a noit a dn evalua noit

htob f ro eva ul ati gn eht uq al yti fo t se t tius es a dn rof eva ul ati gn af ult lot e nar c .e Se im c udno tc ro te gnits has c vno e tn i no a yll b ee n a mi ed ta de et c gnit ra modn e srorr in fa irb cat ,noi rather than d ise ng fa stlu . Rela it ve yl mis p el fa tlu dom e sl have b ee n deve pol ed f ro et gnits

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.in se im c cudno t ro em m yro de iv ec ,s eht ipytotorp c la fa lu st be gni " uts ck- at-0" a dn " uts ck-at-1" (a ga ,et ce ll , ro nip ht at cudorp es t eh same gol aci l va eul rega ldr e ss of i upn st ). A bmun er of m ero compl xe af u tl dom sle have b ee n devel po ed f ro partic lu ar ki sdn of se im c udno c rot devices e( . .g , fail seru fo

s mi ultane suo acc sse ni ud a tropl m me iro e .)s A test v tce ro (an la suogo ot a te ts ius te f ro sof wt are) nac be j gdu ed yb ht e bmun er of htopyh te ical fa stlu it can ed tect, as a f ar tc noi fo a ll ssop i lb e af lu ts dnu er t eh m do e .l nI eva ul a noit of fa lu t lot erance ni ha wdr a ,er ht e usu la a aorpp ch si ot idom fy eht ts ta e ro beha roiv

rather than eht tsys me dnu er tset . uD e to a id ffere cn e ni te onimr l ygo b te w ee n ha wdr are a dn os f wt are te ts i gn , ht e c tpurro i no of ats te ro m ido fic noita of behavi ro si c la led a "fa ,tlu " a dn

ar it f ci ially int udor c gni it is ac ll ed "fa tlu jni ce .noit " niP -lev le fa tlu ejni c noit c sno tsi s of f cro i gn a stuck-at-0, a uts ck-at-1, ro an tni e mr e id ate ov l gat e level (a lev le that si ne hti er a cigol al 0 ron a l igo lac )1 no a nip of a se im c cudno t ro de iv c .e H ae yv noi rad ai ti no is la os su ed to jni ect ra modn af u tl s in a gninnur sys tem. A thi dr a aorpp ch, rg o gniw in ropmi at n ec as ha wdr are com lp e ytix

inc er a ses , u es s sof wt are to idom fy ht e st eta of a nnur i gn s sy et m ro to lumis ate fa stlu in a gninnur s mi ul noita of ha wdr are igol c de ngis . Fa lu t see gnid ac n be su ed ts a citsit ally ni a hton er way: oT e mits ate ht e bmun er of fau tl s er am gnini in a rgorp am. usU la yl we wonk no ly ht e un mber of af stlu that have eb en dete tc ed, a dn on t ht e bmun er t ah t re am i sn . woH ever, again ot ht e e tx e tn th ta ht e fault m do el si a va dil ts a sit t ci la dom el of a tc ual fa lu t o cc rru ence, we ac n es it tam e ht at ht e ra oit of a autc l af u tl s f dnuo to t soh e lits l rem gninia s dluoh be si im lar ot ht e tar io of dees ed faul st f dnuo ot esoht

s it ll rema .gnini

nO ec aga ni , ht e en ec ss a yr a uss m snoitp a er gnilbuort , a dn eno dluow be iwnu es to pl ca e t oo much c difno ence ni an e tamits e of rem ia ni gn faul st . noN e leht e ss , a rp ed ci ti no iw th nwonk w ae kne ss es is be tt er ht an a se ta -of- ht e-pa stn ug e ss , a dn a set fo e its mates derived in reffid ent wa sy is borp ably t eh be ts no e can poh e f ro . While ht e foc su of th si hc a tp er si no fa tlu -based te gnits of fos twa er , re al t de

cet uqinh es ac n be a pp lied to lohw e ys s smet h( a wdr are a dn sof wt are t go ethe )r to evaluate fa tlu to el ra cn e. oS me a eps tc s fo fa tlu -based et ts i gn fo ha wdr a er a er disc ssu ed ni eht dis ebar no page .323 E ex cr si es

disnoC er the C f nu cti no in Fi rug e 61 .4, su ed to dete nimr e hw e ht er a im pss e ll ed drow d fi fe sr f mor a di noitc a yr drow yb ta tsom no e cha ar cte ,r hw ci h may be a del ite ,no an sni e noitr , ro a

utitsbus it no e( . .g , "te tx " is edit d si ta cn e 1 f or m "test" yb a noitutitsbus , dna e tid tsid a cn e 1 f mor "te sts " yb dele noit fo "s" .)

1 2 /* e id t (1 ,1s 2s ) er t snru EURT i ff 1s c na eb nart fs mro ed ot s2 3 * yb i ,gnitresn de el it ,gn ro s tsbu ti ut gni a elgnis c carah et ,r ro 4 * yb a on - po (i ,.e. fi ht ey era alre yda e auq l .) 5 /* 6 ni t e (1tid c ah r ,1s* c ah r )2s* { 7 fi 1s*( == )0 { 8 fi *( s2 == )0 re ut rn TR ;EU 9 */ yrT ni es gnitr a arahc cter ni 1s ro d le gnite in 2s /*

10 if (*( s )0==)1+2 r ute rn TR ;EU 11 re ut rn ESLAF ;

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12 } 13 if 2s*( == )0 { /* ylnO am ct h is yb delet gni tsal c rah morf 1s /* 14 if *( (s1 + )1 == )0 er ut rn TR ;EU 15 re ut rn ESLAF ; 16 } 17 */ N wo ew k won t tah ne hti er gnirts is e ytpm */ 18 if 1s*( == )2s* { 19 re ut rn edi 1s(1t ,1+ 2s ;)1+ 20 } 12

22 */ Mi tams c ;h ylno tsid 1 itilibissop es era tnedi ci la s irt sgn tfa er 23 * sni itre ,gn deleti ,gn ro utitsbus it gn c arah c et r 24 */ 52

26 */ :noitutitsbuS eW " kool ap s "t eht tamsim ched c arah cter /* 27 if s( tr mc ,1+1s(p )1+2s == )0 re ut rn ;EURT 28 */ De el t :noi l koo tsap c arah cter ni s1 /* 29 if s( tr mc ,1+1s(p )2s == )0 re ut rn TR ;EU 30 */ snI er :noit kool tsap c arah cter ni s2 /* 31 if s( tr mc ,1s(p )1+2s == )0 re ut rn TR ;EU 32 er ut rn ESLAF ; 33 }

ugiF re 61 . :4 C f nu c noit ot dete enimr hw ether eno gnirts si iw thin edit natsid ce 1 fo a hton e .r

oppuS es we es ed a af tlu ni l ni e 72 , re alp ic gn 1s +1 yb 1s + 0. sI ht ere a te ts ac se ht at w li l ik ll

siht m tu a tn gnisu ew ak tum a ,noit tub ton su i gn gnorts um at noit ? D si lp ay us ch a set t ac se fi ht e er si ,eno ro e px al ni yhw ht e er si .enon

We have d cse ribed we ka m tatu i no sa c no t gniuni ex ce tu i no pu ot ht e tniop aht t a m tu a tn si lik el ,d then re ts ar it gn ex ce noitu of t eh nigiro la dna mut ta ed rgorp ma f mor ht e begi nn i .gn

W yh od e 'ns t execut noi j su t c no t uni e a tf er llik i gn a tum a tn ? W ah t dluow be nece ss a yr to ma ek c no t uni ed e ex c tu i no ssop i elb ?

vitoM eta the n ee d f ro t eh comp ete tn rgorp a mm er a dn ht e c lpuo i gn e ff ce t htopyh ese s. W uo ld tum a noit ana yl is s s lit l ma ek se esn fi ht e es opyh theses did ton dloh ? W yh ?

Gene ar et mos e i vn la di , va dil - tub - on t- su ef lu , esu f lu , e viuq a el tn a dn viuqenon ale tn m tu a stn f ro t eh rgorp am ni Fi rug e 61 .1 S( ee 7.8. )3 su i gn um at tn po er ta sro f mor rugiF e

7.4 Te ts Ex ce tu i no Wher sae te ts desi ng , even hw en troppus ed yb t oo ls, re riuq es i hgisn t dna i gn e iun ty ni mis ali r

em rusa e ot other f ca e st of sof wt are de ngis , set t ex ce u it no tsum be us f if cien lt y aut mo ated for fre uq e tn er e ex cut noi iw t tuoh til tle muh an vlovni eme tn . hT is cha tp er describes a orpp ca hes f ro c er ita gn ht e nur - mit e s troppu f ro generati gn a dn managi gn t tse da at , c er ita gn sc fa f gnidlo f ro te ts e ex cut ,noi a dn a amotu t ci al yl gnihsiugnitsid bet ew en c erro ct a dn ni c erro ct te ts ac es e ex c tu i .sno

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.in Fr mo eT ts esaC pS ecific ta i no s ot Test saC es If the te ts c sa e eps ci if tac i sno cudorp ed in test de ngis a erl a yd inc dul e c no crete tupni values a dn e epx cted re stlus , as f ro ex ma lp e ni the cate og ry-pa titr i no me doht , then udorp c gni a c lpmo ete et ts case am y be as lpmis e as f li il gn a t me lp ate htiw oht es va ul es. A rom e general test c sa e sp ce ific noita ( .e .g , no e taht cal sl f ro "a ros et d se uq e ecn , el htgn rg eater ht an 2, w ti h ite sm ni sa ce gnidn dro er w ti h on lpud ci a et s") may desi ng eta ma yn lbissop e c no crete test cases, a dn ti m ya be desirab el to gene etar

j su t no e sni at nce ro ynam . hT e er is on lc ear, hs a pr line be wt ee n tset c sa e de ngis a dn te ts ca es genera oit n. A ur le fo uht mb si ht a ,t lihw e te ts ca es de ngis i ovn lv se j mgdu ent a dn c er a ytivit , te ts case genera noit s dluoh be a mecha in cal ts ep. motuA atic gener ita no fo c no crete te ts ca ses f or m erom a artsb tc te ts case eps cificati sno re ud ec s ht e pmi ca t of ms a ll i tn e fr ace cha gn se ni ht e c sruo e fo deve mpol ent. rroC se gnidnop cha gn se ot ht e te ts us ite are sti ll re riuq ed w ti h each rgorp am cha ,egn

tub cha gn es to test case spe ic f ci a snoit are il ke yl to be ms a ll er a dn m ro e ol ac lized ht an cha gn es ot t eh c no c eter test case .s Scaff dlo i gn

ruD i gn um ch of deve pol em tn , ylno a rop it no of ht e f llu sys et m is ava li a lb e f ro testi gn . nI dom e nr deve mpol ent me igolodoht es, ht e parti lla y deve pol ed sys et m is li ek ly ot c sno tsi of no e ro erom

nnur a lb e rgorp a sm a dn may even be c no is dered a ve isr no ro pytotorp e fo the if n la s tsy em f or m ve yr ea ylr in c no ts cur it no , os it is lbissop e at le tsa to e ex c tu e e ca h new trop i no of ht e os f wt are as it si c urtsno cted, ub t the ext nre la tni erfaces of the e ivlov ng sy mets am y on t be i ed al f ro et ;gnits oft ne a dd i noit la c do e m su t be a dd ed. roF exa pm le, even fi the ac aut l bus s tsy em f ro alp c gni an dro er w ti h a ppus eil r si ava li a lb e a dn f lu ly po erati no al, ti si borp ably on t de ris a lb e to lp ace a t suoh a dn ylppus

dro e sr each hgin t as pa tr of an aut mo a it c te ts nur . roM e kil le y a noitrop of the dro er alp ec me tn os f wt are liw l be " bbuts ed tuo " f ro tsom te ts e ex c tu i sno . C do e devel po ed ot fac ili t ta e t tse gni si c la led

sc nidloffa g, yb a an l ygo ot ht e t me rop a yr st ur ct seru e er cted a dnuor a ub i gnidl rud ing c no ts ur tc noi ro am tni ena ecn . cS aff dlo i gn am y inc edul t se t rd ive sr (s sbu it gnitut f ro a m ia n ro ca gnill rgorp a )m , test

ha nr e ss es gnitutitsbus( f ro parts of the depl myo e tn e norivn em ,)tn a dn sbuts bus( its gnitut f ro f tcnu i ano l yti c la led ro su ed yb t eh s wtfo a er dnu er test ,) ni a tidd noi ot rgorp am murtsni e tn ita no a dn

roppus t f ro r ce gnidro a dn am na gnig te ts e ex cut noi . A co mm no e mits a et si th ta lah f of ht e c edo deve pol ed ni a os tf ware jorp tce is sc fa f gnidlo of mos e ki dn , tub ht e am tnuo of cs aff gnidlo ht ta mu ts be c no ts ur cted iw th a s wtfo are jorp ect can va yr diw le y, a dn depe sdn htob no ht e a cilpp ati no mod a ni a dn eht archi et c rut la de ngis a dn iub ld lp an, hw ci h can re ud ec c tso yb e gnisopx a rporpp ai et in et afr sec a dn gnidivorp nece ss a yr f nu c noit ila yt ni a ra noit al dro er. hT e soprup es of sc fa f gnidlo a er to rp o div e c ortno lla ib il yt to ex ce tu e et ts ca es s a dn bo es vr abi ytil to j gdu e the tuo co em of te ts e ex cut noi . moS et emi s sc fa f gnidlo si re riuq ed ot ylpmis am ke a m udo le e ex c tu a elb , tub even ni increme tn al dev le mpo e tn htiw i emm di ta e ni et rg a oit n of ae ch eludom , acs f of ldi gn f ro c no t llor abi ytil a dn sbo e vr a ilib ty am y be re uq ired eb ac su e t eh xe et nr al tni e fr aces fo ht e s tsy em am y ton divorp e us ffi ic ent c ortno l to vird e the ludom e dnu er te ts guorht h t tse c esa s, ro ffus icie tn sbo e vr a ilib ty of t eh

effect. tI am y be de ris a lb e to s itsbu t tu e a separate te ts " vird er" rgorp am f ro the f llu tsys em, ni dro er to edivorp erom rid e tc c ortno l fo an tni erf eca ro ot vomer e depe dn ence no other sysbus et .sm

eG ne cir ve susr pS ce if ci Scaff dlo i gn

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hT e lpmis e ts f mro of cs affoldi gn is a rd iver rgorp ma ht at snur a gnis le, ps e ic fic te ts case. fI , f ro exa elpm , a test case eps ci if c ita no ca sll f ro e ex c tu i gn tem doh ac sll ni a pa tr ci lu ar se uq e ecn , siht si easy ot accomp il hs yb rw ti gni ht e c do e to ma ek ht e met doh cal sl in ht at se uq e ecn . W tir i gn rdnuh e sd

ro t suoh a sdn fo us ch te ts -sp ce ific vird e ,sr no eht other ha dn , may be c osrebmu em a dn a disi cn e tn ive to t hguoroh et ts i gn . At ht e ve yr l ae st no e iw ll wa tn to fac rot uo t os em fo the co mm no vird er c do e into re su a lb e m udo l se . moS te mi es ti si hwhtrow ile to rw eti rom e gene ir c te ts vird e sr that e ess aitn ll y in et rpr et te ts ca es eps ci if ac .snoit

tA l ae ts mos e level of gene ir c acs ff oldi gn troppus ac n be su ed ac ssor a f ia ylr iw de cla ss fo a pp li ac snoit . uS ch troppus pyt aci yll inc dul es, ni dda ti noi to a ts a dn a dr retni fa ec f ro e ex c tu i gn a set of et st ac ses, bas ci roppus t f ro ggol ing test ex ce noitu a dn re stlus . Fi rug e 7.1 sulli trates su e of generic te ts sc fa foldi gn ni t eh FJ el x le ix c la ana zyl er genera .rot

1 bup lic f lani c ssal tnI Cha Sr et { 57 ...

76 ilbup c ov id a tnI(dd e vr la tni e vr lla ) { 681 ... 781 }

1 ap c gak e J lF ex.test ;s 2 3 opmi rt FJ el x. hCtnI a Sr et; 4 opmi rt FJ el x. tnI e vr a ;l 5 opmi rt j .tinu frame krow .Te Cts ase; 11 ... 21 lbup ci c ssal CharCla ss e Ts e ts e tx e sdn T tse Ca es { 52 ...

26 ilbup c ov id te ts )(1ddA { 27 nI Ct ha Sr et tes = n we hCtnI a Sr e (t en w nI et vr la '( a', ;))'h' 28 set.a dd ( en w tnI e vr a 'o'(l , ;))'z' 29 set.a dd ( en w tnI e vr a 'A'(l , ))'Z' ; 30 set.a dd ( en w tnI e vr a 'h'(l , ;))'o' 31 a ss e uqEtr la (s "{ [ 'A' -' 'Z ]['a'- ]'z' }", se (gnirtSot.t ) ;) 32 } 33

34 ilbup c ov id te ts )(2ddA { 35 nI Ct ha Sr et tes = n we hCtnI a Sr e (t en w nI et vr la '( a', ;))'h' 36 set.a dd ( en w tnI e vr a 'o'(l , ;))'z' 37 set.a dd ( en w tnI e vr a 'A'(l , ))'Z' ; 38 set.a dd ( en w tnI e vr a (l 'i', ;))'n' 39 a ss e uqEtr la (s "{ [ 'A' -' 'Z ]['a'- ]'z' }", se (gnirtSot.t ) ;) 40 } 99 ... 001 }

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ugiF er 7. :1 cxE e tpr of lFJ ex 1.4.1 cruos e c do e (a ediw ly su ed po en- ruos ec cs a nn er genera )rot a dn accompa yn i gn J tinU et ts cases. J tinU is pyt ic la of ba is c tset cs affoldi gn il rb a ir es, vorp idi gn roppus t f ro test ex ce noitu , iggol ng, a dn lpmis e re us lt c eh cki gn ( ssa ert lauqE s ni the e lpmax e). ehT li rtsul ated ve noisr of J tinU su se aJ va r fe el c noit ot fi dn a dn execu et te ts case me sdoht ; al ter ve snoisr

of inUJ t su e Java a tatonn i no ( em tadata) fa ic l ti ie ,s a dn hto er sloot u es ruos ec c do e rp e corp e ross s ro genera rot s.

eT ts Oracles tI si l ti tle u es ot e ex c tu e a test tius e a motu ta ci lla y if e ex cut noi re stlus tsum be am un lla y epsni cted ot

a pp ly a pa f/ss a li c tir eri no . Re gniyl no muh an in et vr ent noi to j gdu e te ts tuo c mo es si ton em re yl e px e visn e, tub la os rnu e il abl .e vE en ht e tsom c sno cie suoitn a dn ha dr - ikrow ng pe nosr ca onn t

am tni ia n the level of tta en it no re uq ired ot di entify no e fa li ru e in a rdnuh ed rgorp am ex ce tu i sno , il ltt e m ro e no e ro ten suoht a dn . hT ta is a j bo f ro a com up et r. oS f wt are ht ta a ilpp es a pa ss f/ ail

cr eti noir to a rgorp ma xe ce noitu is called a te ts orac el , often s troh ened ot aro cle. nI a dd i noit to rapidly alc iss fyi gn a la gr e bmun er of et ts case e ex cu it sno , a motu ated te ts ro ca l se ma ek ti lbissop e ot c al ss ify beha roiv s t ah t xe ceed muh an ca ap ci yt in other wa sy , us ch sa c eh c gnik r ae l-time re snops e aga tsni latency re uq ireme stn ro d ae gnil htiw ov lu im suon uptuo t data ni a m ca hine- er ada lb e ra reht than muh an-r ae da elb f .mro

aC rutp e-rep al y te gnits , a sp ce ial case of this ni ihw ch ht e rp e id cted tuptuo ro behavior si rp ese vr ed f or m an ea ilr er ex ce noitu , is sid cu ess d ni siht cha tp er. A related a orpp ca h si ot capt ru e the uo t tup of a urt sted a tl e nr ate ve noisr fo eht rgorp am dnu er te ts . roF exa elpm , no e may udorp ec uptuo t f or m a t tsur ed lpmi eme tn ita no ht at is f ro s mo e er a nos iusnu et d f ro udorp c noit esu ; ti may t oo wols ro am y depe dn no a c nopmo e tn ht at is on t av ia la lb e in t eh udorp cti no e norivn em .tn tI si ton even n ece ss a yr that ht e al et vitanr e lpmi eme tn ati no be om re re il able than the rgorp am dnu er t tse , as gnol as it si us ffi ic e tn ly id eff re tn taht the f ia l ru se of ht e er la dna etla nr ate ve sr i no are kil le y ot be i dn epe dn e ,tn

a dn ob th are s fu ficient yl r le ia lb e ht at ton oot hcum mit e si wa ts ed determ ni i gn ihw ch no e has f ia del a partic lu ar et ts ca es no hw ci h they sid a rg e .e

ugiF er 7. :2 A et st ha enr ss w ti h a c pmo a nosir - sab ed te ts aro cle orp c sse es te ts cases c gnitsisno of rp( o rg am ,tupni rp e cid t de uo t )tup p ia .sr

A driht a aorpp ch to udorp c gni c lpmo ex (i tupn , uo t )tup pa sri si os m mite es bissop el : It am y be ea reis to udorp ec rgorp am i tupn c rro e gnidnops ot a given tuptuo than civ e ve asr . roF exa elpm , it si s lpmi er to sc elbmar a ros ted a rr ay ht an ot ros t a scra bm el d a rr ay. A com nom mi ps e cr ept noi is ht at a te ts

ro ca le la wa sy re riuq es rp ed ci ted rgorp am uptuo t to compare to the tuptuo cudorp ed in a set t e ex cut noi . nI f ca t, ti si fo et n ssop i lb e to j gdu e tuptuo ro behavi ro iw t tuoh pre id cti gn ti . roF exa ,elpm fi a rgorp am si re riuq ed ot if dn a sub tuor e f or m ts a it no A ot ts ati no B, a te ts aro elc n ee d ton

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.in i dn epe dn e tn ly c tupmo e ht e uor et ot asce tr ain ht ta ti is ni fact a v la di uor et ht at starts at A a dn e sdn ta B. arO cles ht at check re lus st wi uoht t er ef re cn e to a rp e id cted uo t up t are often pa itr al, ni the se esn that ht ey ac n dete tc emos vio ital no s fo ht e ac ut la ps ce if ci ta noi ub t ton hto e sr . hT ey c eh ck cen e ss a yr

tub on t us fficient c snoitidno f ro c rro tce ne ss . roF exa elpm , if the ps ce if aci t noi c la sl f ro if gnidn ht e tpo mi um sub tuor e acc gnidro to mos e me cirt , pa itr al ro ca le a v ila id ty c kceh is ylno a parti la aro c el

beca su e it od es ton c kceh amitpo l yti . S mi i al ylr , hc ce gnik aht t a s tro nituor e cudorp es tros de tuptuo si lpmis e a dn c eh ap, ub t ti si ylno a pa itr la aro elc eb ac su e ht e uo t tup is al os re uq ired ot be a

pe noitatumr of the i .tupn A cheap parti la ro ac el ht at can be su ed f ro a la gr e bmun er of et st cases si often c nibmo ed htiw a rom e e px e sn ive c pmo ar si no -based aro cle that ac n be u es d htiw a s am ll er tes

fo te ts cases f ro ihw ch rp edi tc ed tuptuo has eb en tbo a ni e .d dI ae yll , a lgnis e e rpx e ss i no of a sp ce if ci a noit uow ld se vr e htob sa a krow a ss i mng ent and as a

ruos ec f or m hw ci h esu f lu t tse aro cles were a motu ta ical yl derived. pS ce i if c ita sno are often inc lpmo e et , a dn eht ir i mrofn a ytil ipyt ac lly am kes a motu at ci de vir at noi fo et st ro ca les .elbissopmi

hT e di ea si non ethele ss a wop erf lu eno , a dn hw erever f ro am l ro semif ro am l sp ce if ci a snoit ni( cl du i gn de ngis m do e )sl are ava li a elb , it si htrow - hw li e ot c disno er hw ether te ts ro a lc se can be

derived f mor the .m Self- hC ce ks sa Oracles A rgorp am ro ludom e sp ce if ci a noit desc bir es lla c rro ce t rgorp am behavi sro , os an aro cle b esa d no a ps e ic f ci a noit n ee d ton eb paired wi ht a partic lu ar te ts ac .es

ugiF er 7. :3 When self- hc ce sk are me be dd ed in ht e rgorp am, t tse cases n ee d on t in lc du e rp e cid t de tuptuo s.

Self-ch ce k asser it sno m ya be el tf ni ht e udorp ct noi ver is no of a sys et m, hw ere they vorp ide much be tt er id a tsong ci inf mro a noit than ht e nu c no t llor ed a ilpp c ta i no cra hs the c motsu er may othe esiwr re rop t. If siht si on t ac pec at lb e - f ro tsni ance, if ht e c tso of a mitnur e a noitress c eh ck is oot hgih - m so t t oo sl f ro a ss e noitr orp ec gniss sla o divorp e c ortno ls f ro ac vit ita gn a dn dea itc va gnit a ss er it .sno tI is genera yll c sno ide der doog de ngis rp ca it ec ot make a ss ert noi s a dn s le f-ch ce sk be f er e of edis -

e ff ec st no rgorp am ts a et . Side- fe fect fr ee sa ser it no s are e ss enti la hw en asser it sno may be deac vit ated, beca su e othe siwr e s rppu e gniss a ss e noitr c eh cki gn ac n int udor ce rgorp ma fai rul es t tah a epp ar no ly hw en no e is ton te ts gni . Self-ch ce sk ni the f ro m of sa ser it sno me be dd ed in rgorp ma c do e are esu ful mirp a ylir f ro ch ce ki gn m udo le a dn ysbus ets m-level sp ce if ci ati sno , r ta her naht vo era ll rgorp ma beha roiv . Devi gnis rgorp am a ss e noitr s that c rro e dnops ni a rutan al way to ps e ic f ci a snoit (f mro al ro inf mro a )l sop es owt am in cha ll e gn e :s gnigdirb the gap be wt een c no cre et

execut noi va ul es a dn rtsba ca snoit su ed in iceps fic noita , a dn d ae gnil ni a reas no a lb e way htiw uq antificati no vo er c llo e tc i sno fo va ul e .s

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.in φ si an a artsb tc noi f nu c noit t tah c urtsno c st eht rtsba ca t m do le t py e es( st of key, v la eu pa )sri f mor the c no crete data ts ur tc eru . φ si a igol cal a coss ai noit that en ed on t be lpmi e em n et d hw en er a gninos a uob t rgorp am c rro ec ent ss . oT c er ate a te ts aro c el , ti si esu f lu ot have an ca ut al im lp eme tn ta noi fo φ. oF r this ex ma elp , ew mi thg mi p el ment a eps c ai l bo es vr er me doht t tah cre ta es a s mi p el text au l re rp esen at ti no of ht e es t of k( ey, va ul e) riap s. ssA e tr i sno su ed sa te ts aro c sel can then c rro es dnop direc ylt to ht e eps c fi icati no . Be dis es ilpmis fyi gn lpmi eme tn ita no of aro cles yb lpmi eme tn i gn this

am gnipp cno e a dn gnisu ti ni several a ss e tr i sno , st ur tc gniru test aro c sel ot mi rorr a c erro tc ne ss a mugr ent is rewa dr ed hw en a l eta r cha gn e ot the rgorp am i vn a dil a et s mos e part of ht ta a mugr ent ( .e .g , yb cha gn i gn ht e rt ae mt e tn of ilpud c ta es ro gnisu a fid ef rent data ts ur c rut e ni t eh mi p el me tn a )noit .

nI a noitidd to an a sb t ar c noit f nu cti no , r ae gninos uoba t t eh c erro tc ne ss of ni et nr al urts c rut es usua yll

i ovn lves ts ur c arut l vni ar ai stn , ht ta is, porp e itr es of the da at ts ur tc ru e ht at are rp ese vr ed yb la l po era noit s. curtS t ru la vni ar ai tn s are doog ca didn a et s f ro self ch ce sk lpmi emented as sa ser it .sno hT ey pe tr a ni erid tc yl ot ht e c no crete tad a urts c rut e lpmi eme atn it no , a dn nac be mi lp emen et d iw thin

the ludom e that en ac lusp ates th ta data rutcurts e. roF exa elpm , if a id tc noi a yr st ur ct ru e si mi p el me tn ed sa a red- alb ck t er e ro an LVA rt ee , ht e balance porp erty si an i vn a ir a tn of ht e urts c erut

that can be c eh cked yb an a ss e noitr wi niht eht eludom . Fi rug e 7.4 rtsulli ates an i vn a ir a tn check f dnuo ni t eh ecruos c edo fo t eh E lc pi se rgorp mma gni i vn ar ai .tn

1 ap c gak e gro .ecli tdj.esp . ni et nr al. iu .text; 2 opmi rt ja av .te tx .C rah ca te tIr er ta ;ro 3 opmi rt gro .e .espilc jfa ec .te tx .A ess ;tr 4 */ * 5 A* <c do e>C Srah e uq e cn e</c do e> sab ed mi lp eme noitatn fo 6 * <c do e>C ah r tca re tI re ator</c do e> . 7 * nis@ ce 3.0 8 */ 9 bup lic lc ass Se uq e cn Ce harac et tIr er ta ro i lpm em ne ts hC ar ca te Ir et ra rot { 31 ...

14 p ir etav diov vni a ir ant )( { 15 Assert urTsi. e(f dnI ex => f ;)tsriF 16 Assert urTsi. e(f dnI ex =< fLas ;)t 17 } 94 ...

50 ilbup c Se uq e cn e hC ara tc e Ir et ra hC(rot a Sr e uq ence se uq e ,ecn i tn if ,tsr ni t al )ts 51 ht r wo s llI e ag l mugrA e xEtn ec pt noi { 52 fi ( es uq e ecn == un l )l 53 ht r wo wen uN l tnioPl e xEr ec ;)(noitp

egaP 1 39

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.in 54 fi ( if tsr < 0 || f ri st > last) 55 ht r wo wen llI eg la mugrA e xEtn ec tp i )(no ; 56 fi ( al ts > uqes e ecn .le gn t )(h ) 57 ht r wo wen llI eg la mugrA e xEtn ec tp i )(no ; 58 fSe uq en ec = se uq e cn e; 59 fFi sr t= f ;tsri 60 fLa ts = last; 61 f dnI e =x fi ;tsr 62 vni a ir a )(tn ; 63 }

341 ... 441 bup cil c rah dnItes e (x ni t tisop i )no { 541 if noitisop( => ge Bt egi dnIn e )(x && noitisop =< ge dnIdnEt e )(x ) 641 f =xednI op is ;noit 741 e esl 841 ht r wo wen llI ega ugrAl em xEtn ec tp i )(no ; 941 051 i vn a ir ant )( ; 151 er ut rn c rru ent ;)( 251 } 362 ... 462 }

ugiF er 7. :4 A curts t aru l vni ar ai tn c eh cked yb nur -t mi e ssa e tr i .sno cxE e pr et d f or m ht e E ilc esp rgorp a imm gn e mnorivn ent, ve noisr 3. © 0002 , 5002 IBM C ropro ta noi ; su ed dnu er te smr of t eh

E lc espi buP l ci L eci esn 1v . .0

hT ere si a n ta ru la te noisn b te ew en e rpx e ss ivene ss th ta makes ti e sa ier ot rw eti a dn dnu er ats dn ps e ic f ci a snoit , a dn imil ts no e rpx es is vene ss ot tbo a ni efficie tn impl me e tn a .snoit It is ton much fo a

str cte h to say ht ta rgorp mma gni la ugn ages are j tsu f lamro spe ic f ci a noit la ugn ages in ihw ch e rpx e viss ene ss has eb en soprup e yl mil eti d ot e rusn e ht ta eps cifi ac snoit can be ex ce u et d htiw

rp e id cta lb e a dn sa sit fa tc yro perf mro ance. nA tropmi ant way ni ihw ch ps e fic ic snoita su ed f ro uh m na com inum ca it no a dn re gninosa a uob t rgorp a sm are rom e ex rp e ss ive dna le ss c no ts ra ni ed naht

rgorp a imm gn la ugn ages si ht at ht ey f er e yl nauq it fy vo er c llo ec snoit fo va ul es. roF ex ma elp , a ps e ic f ci a noit fo datab sa e c no is s et ncy mi thg s at te that a cc tnuo ide itn fiers ra e uqinu e; that si , f ro lla

acc tnuo er c sdro ni t eh ad at ,esab ht e er od es ton e tsix a hton er a cc tnuo r ce dro w ti h eht sa em di e tn i if e .r

hT e lborp me of uq a itn fi ac noit vo er la gr e es ts fo va ul es si a va ir a noit no the b cisa melborp fo rgorp am t se ti gn , hw ci h si th ta we ca onn t e hx a vitsu ely ch ce k la l rgorp am beha sroiv . tsnI ae d, ew

select a it yn fr ca noit of elbissop rgorp am beha sroiv ro stupni as re rp ese tn a vit es. hT e same ta tc ic si a pp li ac elb ot uq antific noita in ps e fic ic ta .snoi fI we ac tonn ful yl eva ul ta e ht e spe ic fied porp e ytr , ew can at el a ts sel tce emos e el ments to c eh ck hguoht( at rp esent we wonk of on rgorp am a ss e tr i no p ca kages ht ta s roppu t sa lpm i gn of uq a itn fie )sr . roF e ,elpmax alt hguoh we ca onn t aff dro ot e un em rate a ll lbissop e paths be wt ee n owt stniop in a la gr e map, we am y be a lb e to c pmo are ot a sa lpm e of hto er shtap f dnuo yb the same corp e erud . sA htiw test de ngis , doog as lpm se re riuq e emos

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.in in is thg into eht borp el ,m us ch as er c zingo i gn that fi the trohs e ts p ta h f or m A to C pa ess s hguorht B, ti dluohs eb eht c cno atena it no fo eht s troh est p ta h f mor A ot B a dn t eh trohs e ts pa ht f mor B ot C.

A if an l mi lp ement noita borp el m f ro self-chec sk si that a ss e tr ed porp erties os em times i vlovn e v la ues that are either on t kept ni the rgorp am at a ll s( o- lac led tsohg var ai lb e )s ro va ul es aht t have eb en repla ec d (" eb f ero " va )seul . A s ep cifi ac noit of non in et fr ere cn e be wt ee n t rh ae sd in a c no c tnerru

rgorp am may su e ohg ts var ai selb ot art ck e yrtn a dn e ix t of t rh ae sd f mor a c tir ical es cti .no ehT tsop c dno iti no of an ni - lp ace ros t po e tar i no iw ll ts ate ht ta ht e new v la ue is tros ed a dn a perm tu ati no

of the tupni va eul . hT is permut ta i no rel noita re sref ot ob th eht "b fe ero " dna "after" va ul es of t eh jbo e tc to be s tro ed. A nur -t emi ssa e noitr tsys em m su t am nage hg so t vari lba es a dn re at ined " eb f ero "

v la ues a dn tsum e erusn ht at ht ey ha ev on dis e-eff ce st uo t edis a ss e noitr c eh cki .gn tI am y s ee m rnu ae nos a lb e f ro a rgorp ma ps ce if aci it no ot uq antify vo er an infi tin e c ello itc no , tub in

fact it ac n arise iuq et na rut la yl hw en uq an it f ei rs ra e c nibmo ed iw th neg ta noi . If we say " ht ere is on in et ger rg ae ter than 1 t tah divides k evenl ,y " we have c nibmo ed neg ta noi htiw "there exi sts " to f ro m a ats teme tn l igo ca ll y e viuq ela tn to vinu e sr la ( f" ro a ll ") uq a itn fica it no vo er ht e ni tege sr . eW may be clever e hguon ot real zi e that ti fus fic se to c eh ck tni ege sr be ewt en 2 a dn

√k, tub ht at si on l gno er a direct translati no fo t eh ps ce if ci ita no ts ateme .tn

paC tu er dna Repl ya

oS em it m se ti si fid fic tlu ot ei ht er devise a erp c si e descript noi of e epx c det behavi ro ro ade uq ate yl char ca te zir e c rro ect behavi ro f ro effec it ve s le f- hc ce sk . roF ex ma elp , hw eli ma yn porp er eit s of a

rgorp am w ti h a rg a hp ci la tni erf ca e may be eps c fi ei d ni a ma nn er tius a lb e f ro compar si no -based ro self-check ro a lc e ,s emos porp ert sei are ekil ly ot re riuq e a pe nosr to tni eract iw th eht rgorp am a dn j gdu e i st beha roiv . If no e ca onn t c lpmo ete yl avoid muh an ovni lv eme tn ni te ts case e ex c tu i no , eno can at el a ts a ov id nnu ece ss a yr rep te noiti of this c so t a dn roppo t nu ti y f ro e rorr . hT e rp in ic lp e si s mi ple. ehT tsrif emit hcus a et ts case si e ex c tu ed, the ro ca le f nu c noit si ac irr ed tuo yb a muh an, a dn the tni er ca noit se uq ence si capt ru e .d divorP ed t eh ex ce noitu was j gdu ed yb( ht e uh am n t etse )r to eb c rro ce t, the ca rutp ed l go on w f ro sm an (i upn t, rp e id cted uo t up t) pa ri rof sbus e uq e tn a motu ta de re et nits g. hT e savi sgn f or m a motu eta d er et ts i gn w ti h a ac pt ru ed gol de sdnep no woh ma yn dliub - a dn -te ts cycles ew can tnoc i un e ot esu ti ,ni eb f ro e it is vni alidated yb os me cha gn e to ht e rgorp a .m

tsiD i gnihsiugn b te ween si ing f ci a tn a dn ni is ing f tnaci var ai ti sno f mor rp e cid et d beha ,roiv in dro er ot orp l gno ht e ef ef c vit e l fi et mi e of a capt ru ed ,gol is a am j ro c ah ell gn e f ro ac pt eru /re lp ay et ts i gn . aC rutp i gn eve stn at a rom e a sb t ar ct level s sserppu se i ngisn fi ci a tn cha gn es. roF ex ma elp , if we gol

ylno the a tc lau pix le s fo wi swodn a dn me sun , ht en cha gnign even a pyt fe ca e ro ab c dnuorgk col ro ac n vni a dil a et an e tn i er etius fo e ex c noitu l .sgo

Ma pp i gn f or m c no crete ts ate to an a sb t ar ct m do el of i tn e ar c it no se uq e cn es si s mo e mit se ssop i elb

tub is gener la yl uq ite timil ed. A rom e f ur itf lu orppa ca h si ac pt gniru upni t a dn tuptuo behavi ro ta mu tl lpi e leve sl of a rtsb a tc i no wi niht ht e lpmi ementa it no . We have ton ed ht e esu f nlu e ss of a layer ni

ihw ch a artsb tc i tupn e stnev e( . .g , es lec noit of an bo je tc ) a er ca rutp ed in p al ce of c no c ter e events l( tfe suom e tub t no de rp e ss ed iw th suom e sop ti noi ed at 532 , )817 . pyT aci yll , ht ere si a mis li ar

a artsb tc layer ni rg a cihp la tuptuo , a dn much of ht e capt ru e r/ e lp ay et ts i gn ac n krow at th si el ve .l S am ll cha gn es to a rgorp ma can ts lli i vn a dil ate a al gr e bmun er of ex ce u it no gol s, ub t ti si um ch

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.in m ro e l ki e yl that an ins ingi fi ac tn ed t lia ac n e hti er eb i rong ed ni compar si sno ,ro even be tt er, t eh a artsb ct tupni a dn uo t tup ac n eb ys ets m ta i ac yll t snar f mro ed ot re elf ct t eh i tn e dn ed cha gn e . E ex cr si es

oV lu im suon tuptuo can be a ba irr er to naive mi p el ment snoita fo compa nosir - b esa d aro cle .s roF exa elpm , emos times we iw sh to ohs w ht ta os em a rtsb ac it no of rgorp ma behavi ro si

rp ese vr ed yb a sof wt are cha egn . hT e n ia ve a orpp ca h is to st ro e a det ia led execut noi l go fo ht e ro i nig la ve noisr as rp e tcid ed tuptuo , a dn c pmo are ht ta to a deta li ed ex ce noitu gol of eht

idom fied ver is no . nU f nutro ta yle , a deta li ed l go of a si lgn e ex ce tu i no si uq ite le gn t yh , a dn ma tni ia ni gn d te lia ed sgol of ma yn te ts c sa e e ex cut snoi may be rpmi ca it cal. gguS e ts erom efficie tn a orpp ca hes to lpmi eme gnitn compar si no -based test aro cles hw en ti si on t lbissop e ot

erots t eh elohw uo t .tup

We have desc bir ed as an di eal ub t usu ally nu ca ih eva elb og la that test ro a lc es c uo ld eb de vir ed aut mo atica yll f mor eht sa em ps ce if ci ta noi ts atement su ed ot er c dro a dn com cinum a et ht e i tn e dn ed beha roiv of a rgorp am ro m udo le. oT hw ta e tx e tn od es the " set t f tsri " a orpp a hc

of extre em rgorp imma gn X( P) ca ih eve ht si og al? D csi ssu a vd a tn ag se a dn li atim t snoi of gnisu te ts ac ses as a eps cif ci a noit ts ateme .tn

O etf n we ac n c sooh e eb tween no -line self-chec sk r( ce gnizingo fa li ru es as they co c )ru a dn udorp ic gn a gol of even st ro s at tes f ro fo f-l ni e c eh cki gn . Wh ta c disno er ta noi s thgim mot vi a et

eno c ecioh ro eht hto er?

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UNIT 8

LP NA IN GN A DN MONI ROT I GN THE PROC ,SSE DO UC EM N IT NG A AN LYSIS DNA TSET

ynA c lpmo ex orp ce ss uqer ires lp a gninn a dn m no ti gniro . hT e uq a il ty orp sec s re uq ires c droo ina noit of ma yn d fi fere tn a tc i iv eit s vo er a pe doir ht ta snaps a full deve mpol e tn cycle a dn be dnoy . lP a nn i gn si en ec ss a yr to dro er, noisivorp , a dn c nidroo ate lla the ac itivit es ht at troppus a uq a ytil og a ,l a dn

m ino t gniro fo a utc al sutats ag ia tsn a p al n si re riuq ed ot st ee r a dn adj tsu eht orp ec .ss Over weiv P al gninn ovni lves sche lud i gn act vi ti ies, a coll a gnit re ruos ec s, and devisi gn o sb e vr a elb , anu mbi suoug

elim nots es aga tsni ihw ch rgorp e ss a dn perf mro a cn e ac n eb rotinom ed. M gnirotino m ae sn a wsn eri gn the uq es it ,no " woH are we gniod ?" uQ ila yt p al gninn si no e a ps ce t of orp ej ct lp a gninn , a dn uq a ytil

orp ec ss es m su t be ylesolc c nidroo a et d htiw other deve mpol e tn corp e ss es. anidrooC t noi gnoma uq la yti a dn deve mpol e tn at sks am y c rtsno ia n dro e gnir (e.g ,. nu ti te ts s are e ex c tu ed after cr ae ti no fo

rgorp am nu ti )s . tI may hs ape ta sks ot fac tili ate drooc in ita no ; f ro e ax mple, viled e yr may be korb en into ms a ll er ni cremen st ot a ll wo early test gni . moS e a eps stc of the orp ej tc p al n, us ch as fee bd a kc a dn de ngis f ro t se ta ilib ty, may be gnol e uq a ll y to ht e uq ali yt p al n a dn hto er sa ep stc of ht e orp cej t p al n .

uQ ila yt lp a nn i gn begins ta ht e ince tp i no fo a orp cej t a dn is devel po ed wi ht ht e vo e llar orp cej t lp a ,n in ats itn a gnit a dn gnidliub no a uq la yti rts eta yg that spans several ejorp c st . Like ht e vo era ll orp ject p al n, ht e uq la ity lp an si devel po ed increme tn la yl , be nnig i gn wi ht the f ae sib li yti st ydu a dn c no t gniuni t guorh h devel mpo e tn a dn de vil e yr . lumroF ta noi of the p al n ovni lves ksir ana sisyl a dn c no t gni ency lp a nn i gn . xE ce tu i no of the lp an i vlovn es m no ti gniro , c rro e vitc e ca noit , a dn lp a gninn f ro bus es uq e tn re el esa s dna orp je tc s. collA a gnit re ilibisnops ty a gnom te ma m me bers si a c ur ci la a dn diff ci tlu part of p al gninn . When no e pe nosr lp a sy mul lpit e lor es, pxe icil tly ide itn fyi gn e hca res ibisnop lity si ts lli e tness ai l f ro e rusn i gn ht at enon a er ne elg cte .d Q au il yt dna Pro ec ss A os f wt are lp an ovni lves am yn tni ert niw ed c no ec snr , f mor sche lud e ot c so t ot su abi ytil and depe dn a ib lity. De ips et the tni e wtr ini gn , it si esu f lu to d itsi hsiugn vidni i laud c no ec snr a dn bo jectives to le ess n the kil e il dooh that they w li l be ne lg e tc ed, ot a oll c ta e re nops sib li iti es, a dn ot make t eh vo era ll lp a nn i gn orp ec ss erom am nag ae .elb

nA a rporpp ai te uq ali yt orp sec s f wollo s a f mro is m li ar ot the vo era ll os f wt are corp e ss in ihw ch ti is

e bm e dd ed. nI a ts tcir (a dn rnu ae sil tic) wate fr lla os f wt are corp e ss , eno dluow f lo l wo the "V m ledo " (Fi rug e 8.1 ni a se uq e tn ai l ma nn er, begi gninn nu ti et ts i gn no ly as mi p el ment noita c mmo e cn ed f gniwollo c lpmo et noi fo the det ia led desi ng hp a es , a dn f gnihsini tinu t tse i gn bef ro e i tn e rg a noit et ts i gn commen ec d. nI ht e PX "te ts fi tsr " tem oh d, nu ti et ts i gn si c lfno a et d iw th bus s tsy me a dn

tsys me et gnits . A cy lc e of test de ngis a dn te ts ex ce tu i no si rw a pp ed a dnuor e ca h ms lla - rg a ni increme tn al deve pol em tn ts ep. hT e or el that i psn e tc i no a dn ep er re iv e sw dluow p al y in hto er

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orp ec ss es si f lli ed ni PX la gr e yl by p ia r rgorp a mm i gn . A t ipy cal rips al corp e ss dom le seil mos e hw ere be wt ee n, htiw cnitsid t p al gninn , de is ng , a dn lpmi ement ta i no ts eps in several ni creme stn

c puo el d iw th a mis ilar unfoldi gn of ana sisyl dna et ts ac itivit es. A neg eral rp in ic elp , ac ssor all os f wt are corp esse ,s si that the c so t of dete tc i gn dna repai gnir a fa tlu ni c er a es s as a f nu c noit of mit e

be ewt en c immo itt gn an e rorr a dn de et itc gn the r se u atl tn faul st . suhT , hw eta ver the i tn e mr edi eta krow udorp c st in a os f wt are lp an, an fe f ci i tne uq la yti lp an w li l ni cl edu a matched tes of

in et mr e aid et v ila da noit dna ver ifi ca it no a tc i iv eit s ht at det ce t tsom af stlu nihtiw a s troh peri do fo the ri udortni c .noit ynA ts ep ni a os ftware orp ec ss ht at si ton p ia red iw th a v ila dati no ro ve ir f ci a noit s et p si an roppo t nu i yt f ro ed fects ot f tse er, a dn a yn im l tse no e ni a orp ject lp an that od es ton inc edul a uq ali yt c eh ck is an roppo t nu ti y f ro a m si l ae di gn a ss es ems tn fo rgorp e .ss

hT e particular ver ifi ca it no ro va dil a noit ets p at each ats ge depe sdn no the na rut e of ht e in et mr e id a et krow udorp ct a dn no ht e a itn c pi ated ed fe tc s. roF exa elpm , a itn ic pated de ef c st in a re uq ireme stn

s at teme tn m hgi t ni c edul inc elpmo et ne ss , ambi iug ty, inc tsisno ency, a dn vo era ibm it no rela vit e to orp cej t og la s a dn re ruos ces. A rev ei w ts ep thgim a rdd e ss mos e of ht e ,es a dn aut amo ted a an l sy es im thg h le p iw th c telpmo ene ss a dn c sno si tency ch ce ki .gn

hT e e ivlov ng coll ce noit of krow udorp c st ac n eb v ei wed as a set of desc ir tp i sno of id fferent pa str

a dn a ps tce s of the os tf raw e ys mets , ta ffid erent vel e sl of d te lia . snoitroP of ht e mi pl me e tn a noit ha ev the esu f lu porp e ytr of be gni exec tu a lb e ni a c vno en it no al se esn , a dn are ht e rt a noitid la s jbu e tc fo et ts i ,gn tub eve yr level of ps e fic ica it no a dn desi ng can be ob th t eh jbus e tc fo ve ir fica noit ca vit i it es

a dn a cruos e of inf ro m noita f ro verif gniy other art afi tc s. A t py ic la tni e mr e id a et a fitr act - sa ,y a tsysbus me i tn erf eca ed if in it no ro a databa es cs he am - iw ll eb ejbus ct to t eh wollof i gn ts e :sp

tnI re lan c no is ts ency ch ce k C eh ck ht e artifa tc rof complia cn e iw th urts c rut i gn lur es that ed fi en

"we ll -f mro ed" artifac st of ht at pyt e. nA mi trop a tn op i tn of el verage is ed f gnini t eh tnys actic a dn sema itn c ur el s ylhguoroht a dn rp esice yl e hguon ht ta am yn c nommo e srorr re tlus ni de et cta lb e

loiv a noit s. hT is si ana suogol ot tnys ax a dn st gnor -t py ing ur el s in rgorp a gnimm la ugn age ,s hw i hc a er ton e hguon ot ug ara tn ee rgorp am c erro c ent ss ub t effect vi e yl ug a dr aga tsni am yn simple e .srorr E tx er lan c no is ts ency hc ce k C eh ck eht art fi act f ro c tsisno ency htiw er al ted art fi a stc . Often siht

aem sn ch ce gnik f ro c no f mro an ec ot a " rp i "ro ro "hi hg er-level" eps cificati no , tub c tsisno e ycn c eh cki gn od es ton de dnep no es uq ential, pot - nwod deve pol em nt - all ht at is re riuq ed si that t eh re al ted inf mro a noit f mor t ow ro more a fitr acts eb d fe ni ed rp ce i es yl e hguon to s roppu t det ce noit of d csi repa cn ei s. tsisnoC ency usu al yl p or eec sd f or m orb ad, nys cat it c che skc to rom e deta li ed a dn e px e visn e sema itn c che kc s, a dn a vari yte of auto am ted a dn ma un al ve ir fi ac noit tec nh i uq se may eb a pp lied .

eG n re ta i no of c ro rec nt e ss c jno ce rut es C erro c nt e ss c ejno c rut es, ihw ch can be et ts uo ct mo es ro other jbo ce tive c tir e air , lay the krowdnuorg f ro ex et nr al c no is s et ncy c skceh of hto er krow udorp c ,st par cit lu a ylr soht e aht t are y te to be deve pol ed ro re siv ed. Gener ita gn c rro ect ssen c ejno c serut f ro other krow udorp c st will fre uq en lt y vitom a et fer ineme tn of ht e c rru e tn udorp ct. roF exa elpm , an in et afr ce ed if in it no may be ela rob ated a dn made m ero erp c si e os aht t mi p el me tn a snoit can eb e ff ec vit le y tes et .d

eT ts dna anA ly is s tS r ta e seig

egaP 1 89

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.in Less sno of pa ts e px e neir ce are an ropmi at tn a ss et of gro a zin ita sno ht at r le y h ae v li y no cet hni ac l ks lli s. A ydob of e lpx ci ti lwonk e gd e, shared a dn re nif ed yb the puorg , is rom e va ul a lb e ht an isla sdn

of i udividn al c pmo ete ecn . grO aniza it no al lwonk e gd e in a hs ared a dn tsys me atic f mro si erom amena lb e to mi vorp e tnem a dn le ss nluv era lb e to gro a zin a noit al cha gn e, ni lc du i gn the ssol of key i dn i udiv la s. Capt gniru ht e l snosse of e px er ei n ec ni a c sno etsi tn a dn re ep ata lb e f mro si e ss ential f ro a ov idi gn e ,srorr ma tni a ni i gn c tsisno ency fo eht orp ec ,ss a dn inc er asi gn deve mpol e tn fe ficienc .y

lC e na r moo

hT e lC ae moorn orp ec ss dom el, int udor ec d yb I MB ni the late s0891 , p ia sr deve pol em tn iw th V&V activ seiti a dn rts e ss es ana sisyl vo er t se ti gn ni the ae ylr hp a ses . eT its gn si le tf f ro sys t me ce itr fic noita . hT e Cl ae oorn m orp ce ss i vlovn se owt c poo e tar i gn maet ,s ht e dev le mpo ent a dn the uq la yti t ae sm , a dn five am j ro a tc i iv eit s: spe ic f ci a noit , lp a nn i gn , de ngis a dn ve ir f ci a noit , auq l yti

ce itr fic ,noita a dn f ee .kcabd nI the spec fi ic noita ca ivit ty, the dev le mpo ent team ed f seni ht e re uq ired behavi ro of the ys ets ,m

lihw e ht e uq al ti y et am ed if n se su age cs ena soir that are tal er su ed f ro de gnivir s tsy me te ts us ites. ehT pl ninna g acti iv ty di e tn i if es ni crement la dev le mpo e tn a dn ce fitr ica noit hp ase .s Af et r p al ,gninn la l a tc i iv eit s a er iterated to cudorp e ni creme tn al re el ases of the ys ets .m aE ch sys t me increment si f yllu depl yo ed a dn ce itr fied eb f ro e ht e fo ll gniwo ts ep. Desi ng a dn c do e dnu e ogr f mro al ins ep cti no ("Cor er c ent ss ev ir fic noita ") eb f ro e ler ea es . enO fo t eh key rp me si es dnu e pr i nn i gn ht e

lC ea oorn m corp sse dom el si ht at ri uorog s d ise ng a dn f lamro epsni c noit udorp ec " en arly af tlu -f eer os f wt a .er "

hT e auq il yt strate yg is an i tn e ell c aut l a tess of an i dn i udiv al gro a zin a noit rp e cs ribi gn a s te fo os lu it no s to borp l sme sp ce ific to that gro aniza noit . Am gno the fac srot taht partic lu a zir e eht ts rate yg

are:

tS ruc ut re and is ze La gr e orga zin ati sno pyt aci yll have hs a pr er di cnits it sno be ewt en dev le mpo e tn a dn uq la yti spuorg , even if te gnits pe nnosr le are a ngiss ed to deve pol em tn et ma s. nI ms aller

gro a zin a oit sn , it si rom e co mm no f ro a si lgn e pe nosr ot es vr e um itl lp e lor es. Where re nops is b li yti is d tsi tubir ed gnoma erom i dn i udiv la s, t eh auq il yt rts ate yg iw ll re eriuq rom e le a rob ta e tta en it no to

egaP 1 99

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.in co nidro ati no a dn com num ci a it no , a dn ni gener la ht ere wi ll be um ch rg tae re r le ian ec no od c mu e stn to ca yrr t eh c lo lec it ev em m yro . nI a ms la ler gro aniz ita no , ro an gro aniz ta noi that has de ov lved res ibisnop il yt to ms a ll , es mi-

a onotu m suo te sma , ht e er si pyt ci a yll el ss em hp a is s no f ro lam c mo m cinu a noit a dn od c mu e stn ub t a rg etae r e hpm a sis no mana gnig a dn bala gnicn t eh tlum i elp or les lp ayed yb each t ae m m bme e .r

Over lla pr co e ss We have la er a yd ton ed ht e in et gniniwtr of uq a il yt orp ec ss w ti h other a ps e stc of an

vo era ll sof wt are orp ce ,ss a dn siht si of c sruo e lfer e tc ed ni ht e uq a il ty tarts e yg . roF ex ma ple, fi an gro a zin a noit f wollo s the lC ea oorn m met ygolodoh , then psni ect snoi w li l eb re uq ired ub t nu ti te gnits

f bro i dd en. nA gro aniz ta noi ht at a pod st the PX tem odoh ygol si kil e yl to of ll wo ht e "test f ri st" a dn pa ri rgorp a gnimm eleme stn of t tah a orpp ca h, a dn in fa tc uow ld f dni a rom e od c mu e tn - eh a yv a aorpp ch a fid ficult f .ti

toN at snoi , ts a dn a dr corp e ss ts e sp , a dn even loot s ac n be er f el cted ni ht e uq ality rts a et yg ot ht e e tx e tn ht ey are c tsisno ent f or m jorp e tc ot jorp e .tc roF exa elpm , if an gro aniz ita no c tsisno e yltn sesu a partic lu ar c bmo ina noit of LMU d ai rg am on at snoit to od c mu ent ysbus ets m ni terfa ec s, then t eh

uq la yti rts ate yg mi thg inc edul de vir ta noi fo te ts desi sng f mor esoht ton a snoit , as we ll as rev ei w a dn an la sisy ets sp t ia rol ed to de et ct the tsom c nommo a dn ropmi at tn de ngis alf ws at that tniop . If a par cit lu ar ve noisr a dn c no if rug ati no c ortno l ys ts me si vow en into corp e ss am nageme tn , the uq a ytil str ta egy lliw kil e esiw e tiolpx ti to troppus a dn e rofn ce uq la yti corp ess ts e .sp

lppA ci ta i no do am in T eh od am ni may mi op es htob pa itr cu al r ilauq ty jbo ce tives (e. .g , virp ca y a dn s ce ytiru ni me cid al r ce sdro orp ec ,)gniss a dn ni os em cases pa tr ci lu ar ts e sp a dn od c mu ent ta i no re riuq ed to tbo a ni ec rtifi ac noit f mor an e tx e nr al a irohtu ty. roF ex ma elp , the RTCA/ OD - B871 s at dn a dr f ro a inoiv cs os ftware re uq ires test gni ot ht e m do ified c tidno i ed/no c noisi c vo era eg M( C D/ C) c tir eri .no

ERS T

hT e sof wt are ler ai ilib ty e nign ee red te gnits S( R )TE a orpp ca h, deve depol at T&TA ni ht e ea ylr s0991 , assu em s a rips al dev le mpo e tn corp e ss a dn a mgu ents each c lio fo ht e spir la iw th ri uorog s

et ts i gn acti iv it se . SR TE di e itn fies t ow m ia n t py es of testi :gn deve mpol ent et s it gn , su ed ot f dni a dn rem vo e fa stlu ni sof wt are at lea ts pa itr a yll devel po ed ni - ,esuoh a dn certifica it no te gnits , su ed ot e hti er a cc e tp ro er ject ruostuo ced os f wt a er . ehT SR TE a orpp ca h ni cl edu s seven ma ni ts e sp . owT in ti lai , iuq ck dec isi no -ma gnik ts e sp deter im ne hw ci h tsys me s re riuq e pes arate tes it gn a dn hw ci h t py e of te gnits si n ee ded f ro each s tsy em to be tset ed. hT e f vi e c ro e ste sp are ex ce tu ed in para ll el

htiw each coil fo a ps iral deve pol em tn orp ec .ss

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.in

ehT fi ev c ero ts e sp fo SR TE are: Def ni e N" ec sse ar "y Re bail li yti D ete rm ni e po era it no al edom ls, that is, cnitsid t p ta te nr s of s tsy em su age that re riuq e separate te gnits , cl ssa ify f lia ru es acc dro i gn ot the ri ves e ir ty, a dn e gn in ee r t eh

re ail lib ti y ts rate yg htiw fault rp even it ,no fa tlu vomer la , a dn fa tlu ot el ra ecn ca tivit ei .s Deve pol pO re ta i ano l Pr ifo el s Dev le po ob th vo era ll orp f li es that ps an po erati no la m edo ls a dn

po era noit al orp if les iw t nih gnis le po era noit al m do e .sl P er erap f ro Te gnits epS cify et ts cases a dn orp ec rud e .s E ex c tu e Te sts

tnI re pret uliaF er ataD nI et rpr etati no of f lia ru e d ata depe sdn no the pyt e of tes it gn . nI dev le po em tn et ts i ,gn eht og al si ot tr ca k rgorp e ss a dn c rapmo e rp esent f erulia i tn e sn ti ies iw th ejbo itc ves. nI ec itr fic noita te gnits , ht e og la si to d ete mr ine if a sof wt are c nopmo e tn ro tsys em uohs ld be ca ec detp ro jer ec et .d

E tx re em Pr go r mma i gn ( PX )

hT e e rtx eme rgorp a mm i gn m te ygolodoh (X )P em hp a zis es ilpmis ic yt vo er genera il ty, gl bo al vi nois a dn c mo m inu c noita revo struct ru ed gro a zin ati no , fre uq e tn cha gn es vo er gib re ael se ,s c suounitno et ts i gn a dn ana sisyl vo er separati no of lor es a dn re nops is bi itil es, a dn noc it suoun fee bd ca k vo er

tradi noit al lp a .gninn Cu ts o em r i vlovn eme tn ni an PX orp cej t i dulcn es re riuq eme stn a an ly is s d( evel po em ,tn er fi men e ,tn a dn rp i tiro iz ita no of su er irots es) a dn acce tp a cn e te ts i gn of ve yr fre uq e tn iter ita ve re el ases. lP a gninn si based no zitiroirp ati no of su er ts iro es, hw ich are mi lp eme tn ed ni trohs iterati sno . Te ts c sa se

c rro e gnidnops ot scena soir in su er seirots se evr as pa tr i la eps cifi ac .snoit

Test ca es s us ita lb e f ro b cta h e ex c noitu a er pa tr of ht e s etsy m c edo base dna a er i lpm eme tn ed oirp r to t eh lpmi ement ta i no fo f ae rut es they c eh ck (" tset - if tsr "). Deve pol e sr krow ni pai sr , inc er em tn a yll deve gnipol a dn et gnits a eludom . Pa ri rgorp a mm i gn fe ef c it ve yl c fno al tes a re iv ew acti iv ty w ti h c do i gn . E ca h rel ae se is c eh cked yb nnur i gn all ht e te sts dev esi d pu ot th ta tniop fo deve mpol e ,tn t suh e ss enti lla y em gr i gn inu t et ts gni w ti h tni egr ita no a dn s tsy em te gnits . A failed ca ec tp ance et ts si iv ewed as an idni ac ti no ht at a tidd i no al tinu et sts ra e en ede .d

hguohtlA ht ere are on ts a dn a dr t alpme et s f ro an la sy si a dn t tse rts ate ig es, we ac n identify a few

e el em stn that uohs ld eb part fo a ml so t a yn doog rts ate yg . A strate yg luohs d eps ic fy c nommo uq al yti re riuq ements th ta a pp ly to a ll ro m so t udorp cts, orp mo it gn c vno e tn i sno f ro nu a uougibm ls y ts ta gni a dn em a rus i gn ht em, a dn re ud c gni ht e il doohilek ht at they iw ll be vo e koolr ed ni the uq a il ty p al n f ro a pa tr ci lu ar orp cej .t A str eta yg uohs ld cidni ate a set of od c mu en st ht at si ron am lly udorp ec d gnirud

egaP 2 10

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.in the uq al ti y orp sec s, a dn ht e ri c tno e stn a dn rela spihsnoit . tI dluohs dni icate the ac it itiv es ht ta a er

rp escribed yb ht e vo era ll orp ec ss gro aniz ta noi . Often a s te of ts a dn a dr sloot a dn arp tc i ec s liw l eb rp escribed, us ch as the tni e lpr ay of a ve noisr a dn c fno i rug a noit c no t lor t oo l w ti h rev ei w a dn et ts i gn orp ec rud es. nI a noitidd , a tarts e yg ni lc du es ediug il nes f ro orp ject ts fa f gni a dn a ngiss em tn of lor es

a dn re itilibisnops e .s nA xe ec tpr fo a sa pm le rts a yget od c mu e tn si rp ese tn ed in Cha tp er 42 S( ee 8. .)5

eT ts dna anA ly is s snalP

nA anal sy si a dn te ts lp an de iat sl the ets sp to be kat en in a par cit lu ar orp j tce . A lp an s dluoh a wsn er t eh f wollo i gn uq e :snoits What uq a ytil ac it v ti ies w li l eb ca irr ed tuo ? Wh ta are the depe nedn c ei s a gnom ht e uq a il ty itca v ti i se a dn b te w ee n uq tila y a dn

deve mpol e tn ac itivit es? What re cruos es a er en eded a dn woh iw ll ht ey eb la ol ac ted? woH w li l htob the orp ce ss a dn ht e e lov vi gn orp du tc eb m no ti ro ed ot ma ni iat n an ade uq a et

a ss es ems tn fo uq la ity a dn ae ylr wa nr i gn fo uq a ytil a dn sche elud lborp e ?sm E ca h of these ussi es is rdda e ss ed to mos e e tx ent in ht e uq al ti y rts ate yg , tub mu ts be ela rob ated a dn par cit lu a zir ed. sihT is pyt ci al yl ht e re ibisnops il yt of a uq al ti y am nager, ohw dluohs pa itr c tapi e ni the aitini l ef isa ytilib ts ydu ot ide itn fy uq la yti og als a dn se it mate the c no t noitubir of t tse a dn a an ly is s at sks no orp ject c tso a dn cs he .elud

oT udorp ec a uq la yti nalp ht at adequate yl a rdd e ss es ht e uq e ts i sno a evob , ht e uq a ytil am nager m tsu

ide itn fy the ti e sm a dn fea rut es ot eb verified, ht e re ruos ces a dn ac itivit es ht at a er re uq ired, t eh a aorpp ches ht ta dluohs be f wollo ed, a dn c tir e ir a rof ev la uati gn ht e re .stlus I et sm a dn feat ru es ot eb ve ir fied c ri cumsc bir e the ta gr et of ht e uq ila yt p al n. While ht ere is an vbo i suo c rro e dnops e ecn be ewt en i et sm ot eb de polev ed ro dom i if ed a dn oht es to dnu e ogr et ts i gn , ht ey may fid fer so em hw at in deta li . roF exam elp , vo era ll eva ul ati no fo eht u es r tni erf ca e may be ht e vrup ei w fo a separate

muh an af c rot s puorg . hT e ti e sm to be ver fi ied, rom e vo er, dulcni e ma yn in et r em id ate a afitr c st us ch as re uq ireme stn eps c fi ic ta snoi a dn de ngis od cu em nts, ni a tidd i no ot trop i sno of eht delivered

tsys me . orppA ca h se ot be at ken in ver fi ci ita no a dn va dil a noit may v ra y a gnom ite sm . roF exa ,elpm the lp an am y rp escri eb psni e noitc a dn te gnits f ro a ll ite sm a dn a dd i noit al ts a it c an la sy es f ro itlum - t rh eaded s tsysbu me s. uQ al yti og a sl m tsu be e sserpx ed ni et smr of porp e seitr s ta si fied yb the udorp ct a dn tsum eb f htru er le a rob ated htiw me rt i sc ht at ac n be rotinom ed gnirud the c esruo fo t eh orp j .tce

roF exa elpm , if nwonk fail eru cs enari so are lc sa s fi ied as c citir al, seve er , m do era et , a dn m roni , neht we thgim decide ni a navd ec ht ta a udorp ct ver is no am y e tn er e dn - su er cca ep at cn e te gnits ylno hw en ti has dnu e nogr e ys ets m tes it gn w ti h on uo t ts a gnidn cr ti i ac l ro seve er fail ru e .s

Def gnini uq la yti bo jec vit es a dn orp ec ss gro a zin ati no ni deta li re uq ires ni f mro ati no t ah t is not all avai al elb ni t eh early ts ages fo deve pol em .tn Te ts i smet depe dn no ngised dec si i ;sno det ia led a aorpp ches to ev la ua oit n ac n be d fe ni ed ylno retfa ex ima ning re uq ireme stn a dn de ngis ps e ic f ci a ;snoit ta sks a dn sche lud e can be c pmo el ted no ly after eht de ngis ; new r si ks a dn

c no t gni encies may be i udortn ec d yb ed c snoisi kat en rud i gn deve pol me tn . nO ht e hto er ha dn , na ea ylr p al n si nece ss a yr f ro e ts ima gnit a dn c no t llor i gn c tso a dn sche elud . hT e uq a ytil am nager tsum

egaP 2 20

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.in s at rt iw th an ini it al lp an based no incom lp ete a dn te tn a vit e ni f amro it no , a dn ni c mer en at lly refine t eh p al n sa erom a dn be tt er ni f ro am ti no eb c mo es ava li a elb rud i gn eht jorp e .tc

hT e mirp a yr cat it c avail ba el f ro re ud ic gn ht e cs udeh le ksir of a c citir al depe dn ence si to ed c esopmo a task no the cri it cal pa ht , fac rot i gn uo t tbus a sks that ac n be perf mro ed earlier. roF exa elpm , na ac ec tp ance te ts hp ase tal e ni a jorp tce si il ke yl to have a c tir ic la depe nedn ec no dev le mpo e tn a dn

tsys me tni e rg ita no . nO e ac tonn ent ri e yl r me vo e this depe dn ence, ub t i st op et itn al ot delay orp j ce t com lp eti no si re cud ed yb fa tc gniro te ts de ngis f mor te ts ex ce tu i .no Figure 8.1 wohs s a tl e anr it ve cs he lud es f ro a pmis el ejorp ct aht t s at rts at the be gninnig of aJ un a yr a dn sum t be com lp e det yb ht e e dn of Ma .y nI the pot sche elud , i dn ic eta d as C ITIR C LA SC LUDEH E, ht e t sksa anA yl s si dna de ngis , C do e dna nI et noitarg , De ngis dna ex ce u et sbus y ts em et ts s, a dn De ngis dna ex ce tu e sy ts em et ts s f ro m a cri it c la pa ht taht spa sn ht e rud ati no of ht e e eritn

orp cej t. A delay ni a yn of ht e ac itivit es iw ll r se u tl in al et d ile ve yr . nI siht sche elud , ylno t eh udorP ec resu od c mu enta it no at ks od es ton b le gno to the cr ti ci al pa ht , a dn t suh no ly dela sy of this

at ks ac n eb lot erate .d

ugiF er 8. :1 rhT ee bissop el si lpm e sche lud es wi ht ffid ere tn r si sk a dn re ruos ec all co a .noit ehT ba sr i dn ic ta e eht rud a noit fo ht e ta sks . iD a sdnom cidni ate lim e nots es, a dn a sworr b te ew en bars i idn c eta rp ecedence be wt een ta .sks

nI the lddim e cs he ,elud ma kr ed as MILNU ITED RUOSER C SE , the et ts de ngis a dn ex ce noitu

activ seiti a er es pa tar ed ni to tsid inct ta sks . eT ts desi ng at sks are sche ud el d early, hgir t after sisylana dna de ngis , a dn ylno et ts e ex c tu i no is sche lud ed af et r doC e dna tni egrat noi . nI th si way ht e at sks

De is ng bus s tsy em te sts dna De ngis sy ts em te sts are re vom ed f or m ht e cri cit al ap th, ihw ch won spa sn 61 w ee ks iw th a lot era cn e of 5 ew eks htiw re tceps ot ht e e px ec et d te animr it no of ht e orp ej tc . sihT

sche lud e a muss es e hguon re ruos ces f ro gninnur doC e dna tni egr noita , udorP tc noi fo su er od c mu ent ta ion, Desi ng fo sbus y ts em t stse , a dn eD is ng fo sy ts em te sts .

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hT e MIL I DET OSER URC SE cs he lud e ta ht e tob tom of rugiF e 02 .1 r ae rr a gn es ta sks to me te res ruo ce c rtsno a stni . nI siht case we a muss e ht at te ts de ngis a dn e ex cu it no , a dn udorp tc noi fo resu

od cu em tn ati no hs are t eh same re cruos es a dn uht s ac tonn be e ex c tu ed ni par la lel. eW can see ht a ,t de tips e ht e l mi ti ed paral el li ms , ed c gnisopmo et gnits tca vi i it es a dn sche ud l gni te ts d ise ng earlier resu tl s ni a c ir t ci la pa ht of 71 w ee sk , 4 w ee sk ilrae er than the e epx cted t re m ni ita no of the orp jec .t

itoN ec th ta ni ht e exa elpm , ht e cr ti ical pa ht si mrof ed yb ht e tas sk lanA isy s dna desi ng , De ngis sbus y ts em et sts , De ngis ys ts em te ts s, cudorP e su re od c mu e noitatn , xE e tuc e sbus y ts em tests, a dn

Exec tu e sys et m te sts . nI fact, ht e l mi ti ed ava bali li yti of re cruos es stluser in depe dn encies gnoma De is ng bus ys ts em tests, De ngis ys ts em tes st a dn udorP ec su er od c mu e atn t noi ht at la ts gnol er naht t eh par la lel task C edo dna i tn e targ i no .

hT e com lp eted lp an m tsu ni c dul e fre uq ent im l notse es f ro a ss e ss i gn rp o rg e ss . A ur el of bmuht si that, f ro orp jects of a ey ar ro m ero , im el nots es rof a ss se si gn rgorp e ss s dluoh co c ru at lea ts eve yr t rh ee m no t sh . roF retrohs orp jec ,st a r ae nos a lb e am mumix tni e vr al f ro a ss e mss ent is no e uq a tr er fo

orp cej t rud a .noit Figure 8.2 wohs s a lbissop e sche lud e f ro the ni ti ial an la sisy a dn t tse lp an f ro ht e sub ine ss gol ci fo the hC i knump Web rp ese cn e ni the f mro of a TTNAG id a rg am. nI the in ti i la lp an, ht e am nager has a oll cated t mi e a dn fe f tro ot epsni c snoit of all am j ro a fitr acts, as w lle as t tse desi ng as early as

rp ca cit al a dn gniogno test e ex c noitu rud i gn deve mpol ent. noisiviD of ht e orp cej t otni am j ro par st si refle tc ed ni the lp an, tub f ru ther ela rob ati no fo sksat a oss ciated iw th inu ts a dn ms a ll er bus s tsy e sm m su t aw ia t c rro e dnops i gn ela rob ita no fo the arch ti e tc ru al desi ng . suhT , f ro ex ma ,elp insp ce noit fo the hs gnippo af cili it es doc e a dn the nu it test tius es is s nwoh as a lgnis e a rgg eg ta e ta ks . vE en this in ti ai l lp an od es refle tc ht e usu al Chipm knu de ev l po em tn strate yg of re ug al r " nys ch a dn sta ezilib " peri sdo nup tc ua gnit devel mpo e tn , a dn the ini t lai uq tila y p al n refle tc s ht e Chipm knu tarts e yg of a ngiss i gn re nops is ilib ty rof udorp c gni tinu test tius es to dev le po e ,sr htiw ver ei w yb a em bm er fo t eh uq la yti et a .m

Fi ug re 02 . :2 laitinI sc udeh le f ro uq al ti y a tc vi i it es ni deve mpol e tn fo t eh nisub se s ol cig sysbus et m

fo eht C pih m knu Web rp ese ,ecn rp esen et d as a TTNAG d ai rg a .m

hT e TTNAG d ai rg ma swohs f ruo am in spuorg of an la sisy a dn te ts ca ivit eit s: de ngis i psn e tc i no , c do e i epsn c it no , et ts de ngis , a dn test ex ce ut noi . hT e sid t noitubir fo ac it v seiti vo er it me si c rtsno a ni ed yb res ruo ec s a dn depe dn e cn e a gnom ac itivit es. roF exa elpm , s tsy em t tse xe ce tu i no ts a str

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.in af et r c lpmo ite no of sy ts em te ts de ngis a dn ca onn t f hsini eb f ro e ys ets m tni e rg ita no ht( e sync dna st ba il ez e el ments fo ved le mpo e tn rf ema w ro k) is com lp e .et epsnI c noit ac vit ti i se are c rtsno ia ned yb ps e ic f ci a noit a dn desi ng ca tivit ei s. eT ts de ngis ac it v ti ies are c rtsno a ni ed yb mil ti ed re cruos es. La et

sche gnilud of the de ngis of in et rg a oit n t stse f ro the a rtsinimd ative nisub e ss gol ci sysbus et m si nece ss a yr ot a ov id vo erl pa iw th de ngis fo t tse s f ro t eh s gnippoh f lanoitcnu i yt bus s tsy e .m

hT e TTNAG d ai rg am od es ton hgih il hg t tni e mr ed ai te im el nots e ,s tub we can eas li y di entify t ow ni rpA li a dn uJ ly, t suh di gnidiv the dev le mpo e tn otni t rh ee m ia n hp ases. hT e fi tsr hp ase J( a un a yr to rpA li ) c rro se sdnop to uqer ir eme stn an la isy s a dn arc etih c rut al de ngis ac tivit ei s a dn te nimr ates htiw

the arc tih e rutc al de ngis ba les i en . nI siht hp a es , t eh uq la yti team foc su es no d ise ng psni e noitc a dn no the sed i ng of acce tp an ec a dn ys mets te sts . hT e es c dno hp a es M( ay to uJ l )y c rro e dnops s to

tsysbus me de ngis a dn ot the lpmi eme tn ita no of ht e tsrif com lp ete noisrev of ht e tsys em. tI et rm ni ates w ti h t eh fi sr t ats lib iz ita no fo eht adm ni i rts a vit e sub ine ss gol ci ysbus mets . nI t sih hp a ,es

the auq il yt te ma c lpmo etes the de ngis epsni tc noi a dn ht e de ngis of et ts cases. nI ht e f lani ts a eg , t eh deve mpol e tn team udorp ec s ht e if an l ve noisr , ihw el ht e uq a ytil et am foc su es no code i epsn cti no a dn et ts e ex c .noitu

sbA e cn e of te ts de ngis ca it v ti ies in eht la ts hp a es resu tl s f or m ca er ful di e itn fi ac noit of ac vit i it es t tah

a woll ed early lp a gninn fo critical ta .sks

ksiR Pl ninna g

iR ks si an ni e iv at lb e pa tr of eve yr orp ej c ,t a dn os ksir p al gninn tsum be a part fo eve yr lp a .n R sksi ca tonn eb le nimi ate ,d tub ht ey can eb a ss e ss e ,d c no t llor e ,d a dn rotinom e .d

hT e rud a noit of tni e rg ita no , sys et m, a dn acce tp an ec t tse ex ce tu i no depe sdn ot a la gr e ex et tn no t eh uq la yti of sof wt are rednu et ts . oS f wt are that is ylippols c no ts ur cted ro that dnu e ogr es inade uq a et

an la sisy a dn te ts bef ro e c mtimmo ent ot the c do e b esa lliw wols tes it gn rgorp e ss . vE en if res ibisnop lity f ro d ai song i gn te ts fa ruli es l ei s htiw devel po ers a dn on t htiw the te ts i gn rg puo , a test e ex cut noi se noiss th ta re lus st in ma yn fa ruli es a dn generates ma yn fail ru e stroper si hni eren lt y erom

emit c usno m gni ht an e ex cut gni a tius e of t tse s wi ht ef w ro on f ia l ru es. sihT sche lud e uv lnera ilib ty is y te a on ther reas no to e hpm asize ea lr ier a tc vi i it e ,s in pa itr c lu ar soht e that divorp e ae ylr cidni ati sno of uq la yti borp l sme . psnI e noitc of desi ng a dn c edo w( ti h uq tila y t ae m partic pi at )noi ac n he pl c no t lor iht s ri ks , a dn a osl es vr es to c mo m nu icate ytilauq ts a dn a sdr a dn be ts arp c cit es gnoma t eh et am. If tinu t tse gni si ht e re bisnops i ytil of deve pol e ,sr test suites are part of the inu t d ile verab el a dn

dluohs dnu e ogr epsni c noit f ro c rro tce ne ss , t nhguoroh sse , a dn aut mo a noit . W lih e f lanoitcnu a dn st ur ct ru al c vo erage c iretir a are on pa an cea f ro em gnirusa et st guoroht hne ss , it is er a nos able ot re riuq e th ta dev itai sno f mor bas ci c vo erage c ir et ir a be j itsu fied no a ac se- yb - ac se ba sis . A

tsbus a itn al dev itai no f mor the urts c rut al c vo erage sbo e vr ed ni sim ali r udorp cts may be ud e to ma yn ac su es, i cn gnidul ni ade uq ate te gnits , ni c lpmo ete spec fi ci a snoit , unu s lau de ngis , ro mi p el me tn ati no

d ce si i sno . hT e ludom es that rp ese tn usunu a yll l wo ts ur tc ru al c vo erage uohs ld be epsni cted ot ide itn fy eht ca .esu

hT e c tso fo anal sy si a dn et st si m lu it plied hw en some re riuq emen st de dnam a ve yr hi hg el v le of a russ ance. roF exa ,elpm fi a s tsy em t tah has rp e uoiv ls y b ee n su ed ni biol igo lac re es arch si idom f ei d

ro redeve pol ed f ro ilc cin la esu , no e uohs ld a itn c pi a et aht t all deve pol em tn c ,stso a dn partic lu a ylr

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.in c stso of ana sisyl a dn te ts , w lli be an dro er of ma duting e hi hg er. nI a noitidd ot the r si k of

dnu er tse i am ti gn the c so t a dn sche ud el apmi ct of rts i gn ent uq la yti re uq irem stne , the ksir of f lia i gn to ac ih eve the re riuq ed ped e dn a ib til y ni creases. nO e tropmi a tn at c it c f ro c ortno lli gn siht r si k si si o al gnit c citir la porp erties as far sa lbissop e in s am ll , s mi lp e com nop ents. Of c sruo e ht ese a eps c st

of sys et m s ep cific noita dna arch ti ect aru l desi ng are ton e tn ire yl nihtiw c lortno of eht uq a il ty et a ;m it si c ur cial that at le tsa ht e uq ali yt am nager, a dn ylbissop other em bm e sr of ht e auq il yt tea ,m

par it pic ate ni ps e cific a noit a dn de ngis a itc v ti ies ot sa se ss a dn com cinum eta the apmi ct of desi ng a tl e nr ta vi es no c tso a dn sche .elud M otino r gni eht Pr co e ss

hT e uq ality manager m ino t sro rgorp e ss of uq a ytil ca itivit es, gnidulcni re us tl s as well as sche elud , ot ide itn fy dev ai ti sno f mor ht e uq a il yt p al n as early as op ssible a dn take c rro ce vit e ca ti no . ffE e tc ive m ino t gniro , n ta ru a ll y, ped e sdn no a lp an that is r ae citsil , we ll gro anized, a dn fus ficie tn ly d te ia el d

htiw lc ear, nu a suougibm im el nots es a dn c ir et air . eW as y a orp ce ss is v lbisi e to the e tx e tn aht t it c na eb fe fe tc vi e yl m ino t ro e .d

uS cce ss f lu c pmo el ti no of a lp a nn ed a itc v ti y m su t eb tsid i hsiugn ed morf me er et mr ina noit , as

othe siwr e ti is oot t gnitpme ot eem t an pmi e gnidn d ae nild e yb mo ti gnit mos e lp a nn ed .krow kS i pp i gn p al nn ed ver fi ci ati no a itc v ti ies ro a rdd es is gn them pus erficially can s ee m ot accelerate a

al te orp ject, tub ht e tsoob si no ly a pp are tn ; ht e er la fe fe tc si to noptsop e d te e tc noi of rom e af tlu s ot al ter ats g se ni dev le mpo e tn , hw ere the ri eted c noit a dn er m vo la lliw be far rom e erht eta ni gn to

orp cej t s cu sec .s

ksiR M gana eme tn in eht auQ il yt Pla :n sksiR Gen re ci ot Proc sse M gana ement

hT e uq a ytil lp an m su t di entify op et ntial sksir a dn de nif e a rporpp ai te c ortno l at c it cs. moS e ri sks a dn c no t lor tac cit s are gene ir c ot orp ec ss management, lihw e othe sr are eps c ifi c ot ht e uq a ytil orp ces .s Here we rp vo ide a rb ief vo re v ei w of mos e r si ks gene ir c ot orp ec ss ma gan eme tn . R si sk eps cific ot t eh auq il yt corp e ss a er mmus arized ni eht dis ebar no page .193

P re nnos el ksiR s E lpmax e tnoC r lo Tac it cs

A staff member is lo ts b( ce mo es i ,ll C ssor rt ia n to a diov vo e dr epe dn en ec no udividni la ;s cha gn se empl yo er, e ct ). ro si enc ruo age a dn sche ud le c no it suoun e ud cati no ; vorp ide

dnu e uqr la ified f ro ta ks ht( e orp ject po en c inummo ca noit iw th itinutroppo es f ro st fa f self- p al n a uss med a level of lliks ro a ss es ems nt a dn di e itn f ci ta noi of sk li sl ga sp ea ylr in ht e fa im il a ytir that ht e a iss ng ed m me ber orp ject; vorp ide comp ite tive compe sn a it on a dn did ton have .) orp mot noi op li eic s a dn a rewa gnidr krow e orivn n em tn ot

r iate n st fa f; ni lc edu rt a gnini mit e ni eht jorp e tc cs he .elud

Tec onh l ygo Ri sks E lpmax e tnoC r lo Tac it cs

Many af ults are int udor dec itnA c pi ate a dn sche lud e e rtx a mit e f ro tes it gn fnu ma ili ar in et afr c gni to an fnu am li iar in et afr ec s; vni e ts t ar ini gn mit e f ro STOC c nopmo e stn commercial off- ht e- ehs lf (C STO ) a dn f ro tra ni i gn iw th wen ;sloot nom ti ro , od c mu e tn , a dn com nop ent. lbup ci ezi c nommo e rorr s a dn c erro ct ;smoidi udortni ce

new t loo s in wol er-ri ks p li to orp jec st ro orp tot gnipy

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exer sic es .

Test a dn ana yl is s a motu ta noi t oo ls udortnI ec new sloot ni wol er-r si k p li ot orp cej st ro od ton em et e epx tc a .snoit orp t pyto ing exer ic se ;s itna c pi ate a dn cs he lud e mit e f ro

tra gnini wi ht new t oo l .s

C TO S c nopmo e tn s do on t m ee t nI lc edu C STO com tnenop uq alific ita no t gnitse ea ylr ni uq la yti e epx cta .snoit orp ject lp a ;n int cudor e new C STO com nop e stn in l wo er-

ksir pi tol orp jects ro pytotorp ing exerc si e .s

Sche lud e sksiR E lpmax e tnoC r lo Tac it cs

nI ade uq ate inu t te nits g ael ds to rT ca k a dn rewa dr uq a ytil tinu te gnits as eviden ec d yb nu antic api et d e px e sn e dna dela sy in l wo - af tlu densities in rgetni ati .no

in et rg a noit te .gnits

iD ff ci ytlu of sche ud il gn meet sgni S te isa de t mi es in a w ee kly sche ud le in hw ci h am sek epsni c noit a ttob el n ce k in ins ep itc sno at ke erp ec de cn e vo er hto er m ee ti sgn a dn

deve mpol ent. other krow ; t yr tubirtsid ed a dn a nys c suonorh i psn ce noit cet nh i uq e ,s w ti h a wol er fre uq ency of face- ot -f eca

ins ep itc no meet .sgni

ksiR M gana eme tn in eht auQ il yt Pl :na sksiR pS ce if ci ot lauQ i yt M gana ement Here ew vorp ide a feirb vo e vr ei w of os me sksir eps cif ci to the uq ality orp cess. R sksi gener ci to

orp ec ss manageme tn a er s mu am rized ni eht dis e rab ta pa eg .093

Deve mpol e tn Ri sks E lpmax e tnoC r lo Tac it cs

ooP r uq a il ty os ftware delivered ot rP o div e ae rly wa nr ing and f ee bd ca ;k sche elud et ts i gn puorg ro ni ade uq a et tinu et st ins ep itc no of de is ng , doc e a dn et ts tius e ;s c nno e tc

a dn anal sy si bef ro e co imm tt i gn ot eht deve mpol ent a dn ins ep cti no to ht e reward s tsy e ;m c edo ba .es inc er ase rt ia ni gn t guorh h ins ep cti no ; re riuq e c vo era eg

ro other c tir e air at tinu t tse el ve .l

E ex c snoitu R si sk E lpmax e tnoC r lo Tac it cs

xE ec noitu c so st hi hg er than lp a nn e ;d Minim zi e pa str ht at re riuq e full tsys me ot be e ucex et ;d s ac r ec re cruos es av ia lbal e f ro test gni ins ep tc arc tih e rutc e to ssa e ss a dn vorpmi e te ts ab li ;yti t( est gni re riuq es e px e visn e ro c lpmo ex inc er ase in et mr e id a et bdeef ca ;k i vn se t in acs ff ol gnid .

cam nih es or tsys me s ton ae s li y avai al elb .)

Re iuq reme stn Ri sks E lpmax e tnoC r lo Tac it cs

hgiH a russ ance c itir c la riuqer ement .s Compare lp a nn ed te ts i gn ffe ro t htiw f mro er jorp e stc iw th mis li ar c ir it cality vel el ot a diov dnu eres it gnitam

et s it gn eff ro t; ab al n ec te ts a dn a ylan is ;s si o al te c citir al part ,s c no ec snr a dn itreporp e .s

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nO e key a rgg egate em a rus e is the bmun er of faul st that have eb en reve la ed a dn r me vo ed, ihw ch can be c pmo ared ot data bo t nia ed f mor limis ar past jorp e stc . Fa lu t ed te itc no a dn r me vo al ac n be art cked aga tsni time a dn iw ll pyt ci ylla fol wol a chara tsiretc ci noitubirtsid limis ar to that s nwoh ni rugiF e 8.3. hT e un mber of fa stlu de et cted per t mi e inu t te sdn ot worg ca or ss sever la ys mets ub li sd , ht en ot d ce r ae es ta a much wol er etar usu( a ll y h la f t eh htworg ar et ) litnu it ts a .sezilib

ugiF re 8. :3 A cipyt al di noitubirts fo fa stlu f ro tsys em ub i sdl hguorht it .em

nA nu e epx cted pa tt e nr ni fa tlu detec it no may eb a otpmys m of lborp ems. If detected af ul st pots rg o gniw earlier ht an e px ce ted, no e mi thg poh e ti i dn ic ta se e cx ep it no la yl hi hg uq ila yt , tub it uow ld

be siw e ot c sno ider eht a tl e nr a it ve opyh thesis ht at af u tl dete tc noi fe f stro are i en f ef ct evi . A worg th ra et ht ta rem ia sn hi hg th guor h m ro e than half the p al nn ed ys ets m sdliub si a wa nr i gn ht ta uq a ytil og sla may be m te al te ro on t at a ll , a dn may cidni ate w ae nk e ss es in fa lu t re vom al ro l ca k fo

di cs nilpi e in dev le mpo ent e( . .g , a hsur to a dd fea rut es eb f ro e ed il ve yr , htiw a c sno e uq ent d ee hpm a is s no uq la ity c ortno l .) A ces dno idni ac rot of lborp me s in ht e uq al ti y orp ec ss si faul st that r me nia po en l gno er than e epx cted. uQ la ity lborp e sm are c mrifno ed hw en ht e bmun er of po en fa stlu od se on t sta zilib e at a el vel acce tp a elb ot ts a dlohek e .sr

hT e ca c ru ca y w ti h ihw ch we can rp e id tc fa tlu data a dn id a ong es de iv at snoi f mor e px ce tati no

depe sdn no ht e ts a ib il yt of the sof wt a er deve pol em tn a dn uq ali yt orp ce ss es, a dn no ava li abili yt fo data f or m limis ar orp j ce ts. fiD fere cn se b te w ee n gro a zin ati sno a dn ac ssor a pp l ci a noit od iam sn a er

,ediw os yb far eht most va ul able da at si f mor limis ar orp jec st in no e s' nwo gro a zin ati .no

hT e fa lu it ne ss data in Fi rug e 8.3 are a rgg ega et d yb sever ti y el v sle . hT is he spl in be rett dnu e tsr a dn i gn eht ssecorp . htworG ni the bmun er of dom e tar e fa stlu late ni ht e dev le mpo ent orp ec ss may be a s motpmy of doog su e of detimil r se ruo ec s c cno ent ar et d ni re vom i gn c itir cal a dn

severe fa stlu , ton ps e tn os l iv ng dom re ate elborp sm . A cc ru ate classif ci a noit cs meh ata can mi evorp m ino t gniro a dn am y be su ed ni ve yr la gr e orp jects, hw ere the a nuom t of det ia led inf ro am ti no ca tonn be mus am rized ni vo era ll da at . hT e nogohtro la de ef tc cla iss f ci a noit DO( C) a aorpp ch h sa t ow ma ni s et :sp )1( fa tlu cla ss ifi ac noit a dn )2( fault ana .sisyl

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DO C af lu t c al fiss ic ta noi si nod e ni t ow hp ase :s hw en fa stlu are dete tc ed a dn hw en they are f xi ed. tA detec noit ,emit ew r ce dro eht ca t vi ti y e ex c tu ed hw en the fa tlu si reveale ,d eht irt gg er ht at e opx es d the fault, a dn ht e pe cr e vi ed ro ac ut al i apm ct of the fault no ht e c otsu em r. A lbissop e at onox my f ro activ seiti a dn ggirt e sr is li rtsul ated in ht e dis ebar at page 593 . itoN ec t ah t rt i gg e sr depe dn no t eh activity. ehT dis ebar ta page 693 i ll tartsu es a elbissop ta ymonox fo c su t mo er apmi c .st

hT e deta li ed ni f mro a it no no af lu st a ll swo f ro ynam an la y es s that can divorp e ni f mro a noit no t eh deve mpol ent a dn ht e uq al yti orp ec ss . sA in t eh ac se of ana sisyl of simp el fa nitlu sse da at , t eh in et rpr etati no depe sdn no the orp ec ss a dn ht e udorp ct, a dn s dluoh be based no pa ts e px e ir ence. ehT at onox my of fa ,stlu as well as eht an la sisy of fa nitlu e ss ,atad dluohs be er fined while a pp l gniy ht e

em oht d. When we f tsri a pp ly the DO C tem ,doh we can perf mro s emo rp e nimil a yr ana sisyl gnisu ylno pa tr fo eht c llo e tc ed ni f amro it :no

tsiD r oitubi n of f tlua types v re sus activi it es fiD ef rent uq a il ty ca itivit es ta gr te id ffere tn cla ess s fo

af u tl s. roF exa elpm , a rogl ti h cim (that si , loc )la f ua tl s are ta gr eted rp ima ylir by tinu t tse gni , a dn ew e epx tc a hgih roporp it no of fau tl s de et cted yb tinu te ts i gn ot be ni siht cla ss . If the roporp it no fo a irogl t mh ci fa stlu f dnuo rud i gn tinu te gnits si nu u us a ll y ms a ll , ro a al gr er than ron lam roporp t noi fo a irogl t mh ci fa lu st are f dnuo rud i gn in et rg ita no et s it gn , ht en no e may re nosa a ylb psus ce t th ta tinu et sts have ton eb en we ll de is ng ed. If the xim of faul st f dnuo rud i gn in et rg a noit t se ti gn c tno ai sn an

usunu lla y hgih roporp it no of la rog ti hm ci faul ,st it si a sl o sop is lb e that i tn e rg ta noi gnitset has on t foc su ed ylgnorts e hguon no tni e afr ce fa .stlu

tsiD r noitubi of t ir gg ers vo re mit e ud r gni fie dl t se t Fa stlu c rro e gnidnops to is lpm e u as ge s dluoh arise early rud i gn fi le d te ts , lihw e fa stlu c rro e gnidnops to c lpmo ex su age uohs ld arise la et . nI ob th cases, ht e r ta e fo sid cl ruso e fo new af ul st dluohs as totpmy ic lla y ed c er a .es nU e epx etc d sid t snoitubir of ggirt e sr over t emi may i dn aci te roop s tsy em ro ecca tp ance te ts . If rt i gg e sr that c rro e dnops to s mi ple su age reveal ma yn af stlu al et ni ac ec tp a cn e te ,gnits we may have sohc en a sa lpm e that si on t re rp esen at tive fo the su er lupop ati no . If fa stlu c no it un e worg i gn rud ing acce tp a cn e te ,ts s etsy m et ts i gn am y have fa li e ,d a dn ew may ed c edi to re us me ti eb f ero c no t gniuni w ti h a cc ep at ecn te .gnits

gA e tsid r itubi on vo re target c do e tsoM fa stlu dluohs be loc ta ed in new a dn re tirw et n c edo , hw i el

ef w fa stlu dluohs be f dnuo ni ba es ro re-fixed c edo , cnis e base a dn re-fixed c do e has rla ae yd be ne et ts ed a dn c erro cted. roM e vo er, the noitroporp fo af stlu ni new a dn re rw it et n c do e iw th re eps ct ot

base a dn re-f xi ed c do e dluohs rg a ud a yll increas .e Different ap ett snr may i dn ci a et loh es ni the fa tlu tr ca gnik a dn re vom la orp ce ss ro am y be a ys m otp m fo inade uq ate tset dna a sisylan ht at fa li ed ni rev ae li gn fa lu st ea ylr (in rp evi uo s te ts s of base ro re-f xi ed c do e). roF exa elpm , an ni c er ase of af stlu loca et d in ba es c edo fa ter rop it gn ot a new lp atf ro m may cidni a et inade etauq te sts f ro trop abi .ytil

tsiD r oitubi n of f ua lt c ssal es evo r mit e hT e noitroporp of im iss gn c do e fa lu st uohs ld rg a ud ally d ce r ae es , hw ile ht e rep ec tn age fo extrane suo fa stlu am y ls ylwo inc er sa ,e eb ac su e ssim i gn f tcnu i ano l yti uohs ld be rev ae led iw th su e a dn rep ia red, hw ile extrane uo s c do e ro od c mu ent noita

am y be udorp ec d yb u dp a set . nA ni cr ae gnis n bmu er of m issi gn af u tl s may be a ys mptom of in ats ilib ty of ht e udorp ct, lihw e a ddus en prahs ni c er a es in e rtx ane suo fa stlu may i dn ica et

am tni ena ecn lborp me .s mI pr gnivo ht e Pr co se s

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.in Ma yn cla ss es of af lu st ht ta co c ru fre uq e yltn a er toor ed ni orp ssec a dn deve pol em tn fla sw . oF r exa elpm , a sh la wol arc tih e rutc al de ngis ht at od es on t take into acc nuo t re ruos ce a oll c noita ac n l dae to re ruos ce la ol ac ti no fau tl s. L ca k of e px e ir e cn e htiw the dev le mpo e tn e mnorivn e tn , ihw ch l ae sd ot

im dnus e tsr a dn i sgn be ewt en ana yl ts s a dn rgorp a mm e sr no rare a dn e cx e noitp al cases, can re tlus in af u tl s ni exce tp i no ha ldn i gn . A perf ro am n ec a ssess em nt sys et m that re sdraw af ts er c gnido wi uoht t

rega dr to lauq ti y si il ylek to etomorp wol uq a il ty .edoc

hT e o cc rru e cn e of ma yn us ch fa stlu ac n be re decud yb dom if gniy ht e orp ssec a dn e mnorivn e tn . roF exa elpm , re cruos e noitacolla fa tlu s resu tl gni f or m hs lla wo arc ih tect aru l de ngis ac n be re ud ec d yb int udor ic gn ps ce ific psni ce ti no at sks . Fa stlu ta tubirt a lb e ot ni e px e ir e cn e w ti h the dev le mpo e tn e vn i nor em nt ac n be re ud ec d wi ht foc su ed gniniart es snoiss . Per is yltnets oop r rp o rg mma gni rp ca cit es may re eriuq m ido ficat noi fo eht rewa dr tsys me .

Of et n, foc su ed cha gn es in ht e orp ec ss can dael ot udorp tc im vorp eme nt a dn is ing fi ac tn c tso re ud c oit n. nU f ro t tanu le ,y di entif gniy ht e w ae k a ps ects of a corp e ss can be extrem le y fid f ci tlu , a dn often the re stlus of orp c sse ana yl is s rprus ise even e px e tr manage .sr ehT ana sisyl fo eht af lu t hi yrots ac n pleh sof wt are e gn i en e sr iub ld a fee bd ca k me hc a in ms ot t ar ck r ele va tn af u tl s ot th ie r oor t ac su e ,s

t suh vorp iding tiv al inf mro a noit f ro gnivorpmi the orp ec ss . In os em ac ses, ni f ro am ti no ac n be fed b ca k di er c ylt into ht e rruc ent udorp tc deve mpol e tn , ub t erom tfo en ti he spl fos twa er e gn i reen s mi vorp e ht e deve nempol t of fut ru e udorp cts. roF exa elpm , if a ylan is s of fa stlu rev ae ls fre uq e tn

o cc rru ence of severe me om yr manageme tn fa stlu in C rgorp sma , we m hgi t revise psni ce noit c eh ckl stsi a dn udortni ce nyd cima an la sisy ,sloot ub t ti may be t oo al et ot cha gn e early desi ng d ce si i sno ro es el tc a ffid erent rgorp imma gn al ugn age in the orp ject wrednu ay. roM e f dnu ament la cha gn es may eb ma ed in f erutu orp ej ct .s

tooR c ua es lana ys si R( C )A is a t ce uqinh e f ro i itned fyi gn a dn e il anim it gn orp ec ss fa stlu . ACR saw f tsri dev le po ed in eht un clear wop er yrtsudni a dn later e tx e dn ed ot os ft raw e ana sisyl . tI c stsisno of f ruo m ia n s et sp to se el ct si ing f ci a tn lc a ss es of uaf l st a dn rt ca k ht em b ca k ot ht ie r ro i nig al ca su e :s

ahW t, hW ne , yhW , a dn oH w .

tahW are ht e f tlua s? The og al of this f tsri step si ot ide itn fy a cla ss of im trop ant fa lu ts. Fa stlu a er cate zirog ed yb sever ti y a dn dnik . hT e sever ti y of af u tl s cha ar c et ezir s the impact of the af tlu no ht e

udorp ct. Al hguoht di reff e tn me odoht l igo es esu ls i ylthg d fi fere tn cs a sel a dn term ,s a ll of t eh m ide itn fy a few ts a dn a dr vel e ,sl described ni Table 8.1. T ba el 8. :1 natS dard se ev ir ty el vels for r oo t c sua e ana l sy si ACR( ).

pO en bat el sa ps re hsda ee t

Level De cs r tpi ion E lpmax e

C ir it cal ehT udorp ct si sunu a .elb ehT fa tlu ac su es eht rgorp am ot cra .hs

Severe moS e udorp ct fea serut ac tonn hT e fa lu t i hn i tib s mi gnitrop fi el s saved htiw a be su ed, a dn ht ere si no rp e suoiv ve noisr of t eh rgorp ma , a dn ht ere si on

krow a nuor d. way to c vno ert f li es saved ni ht e dlo f ro mat ot t eh new .eno

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.in T ba el 8. :1 natS dard se ev ir ty el vels for r oo t c sua e ana l sy si ACR( ).

pO en bat el sa ps re hsda ee t

Level De cs r tpi ion E lpmax e

doM erate moS e udorp ct fea serut re eriuq hT e fault stibihni e gnitropx ni oP ts sc tpir f .tamro krow a sdnuor ot esu , a dn re ecud soP cst ript can be udorp ec d gnisu the tnirp i gn

efficiency, r le ia ib lity, ro fac ili ty, tub ht e ssecorp si on t uoivbo s or c vno en ei nce a dn su a lib i yt . od cu em n et d ssol( of basu li )yti a dn re riuq es e artx

s et sp ssol( fo fe ficienc .)y

Co ems cit roniM ni c vno en ei nc .e hT e fa lu t lim ti s the c oh ice fo col ro s for c otsu m zi i gn the rg a ihp cal in et fr a ec , loiv ta gni eht ps e ic f ci a oit n tub ca nisu g lno y im ron

inc vno en ei n ec .

hT e RCA a orpp ca h ot cate zirog i gn af ul ,st ni c rtno ast ot CDO , od es ton su e a rp e ed f ni ed set fo cate irog es. hT e jbo e vitc e of ACR si ton ot c pmo are fid fere tn c al ss se of fa stlu vo er emit , ro to an la zy e a dn el mi ni a et a ll lbissop e fa lu t ,s tub ra ht er ot ide itn fy ht e few m tso mi trop a tn cla ss es fo af u tl s a dn vomer e the ri ca su es. cuS ce ss f lu a pp l taci i no of RCA rgorp e ss iv le y el imi nates ht e ca su es

of the c rru en lt y tsom i tropm a tn faul st , ihw ch sol e tropmi a cn e vo er it em , so a pp l gniy a tats ci rp e ed f ni ed lc a ss if ci ita no uow ld eb su ele .ss M voero er, eht erp ci nois iw th ihw ch ew di e itn fy af stlu

depe sdn no t eh ps e fic ic orp cej t a dn corp e ss a dn var ei s vo er .emit

O CD Cl issa fic ta i no fo Tri gg re s iL ts ed yb Ac vit i yt De is ng Re weiv dna edoC psnI ec noit De is ng noC formance A sid crepancy b te ew en the rev ei wed a tr ifa tc a dn a roirp - ts age a itr fact that se vr es as its eps ci cif ati .no L go ci olF/ w nA a irogl th im c ro l cigo lf a .w Backward ibitapmoC il ty A id fference b te ew en ht e c rru e tn a dn ea lr ei r ver is no s fo an a tr ifa tc t tah c uo ld eb pe cr eived yb t eh c su t mo er as a f lia .eru

tnI re lan oD c mu ne t nA tni e lanr ni c tsisno ency in ht e a tr i af ct e( . .g , inc tsisno ency betw ee n c do e a dn comme stn ) . L ta er la tapmoC ib li yti nA inc pmo atib li yti be wt een ht e a fitr ca t a dn mos e other tsys me ro m eludo

htiw ihw ch ti dluohs i poretn era .et

noC cu err ncy A fa tlu in tni e ar cti no fo c no c rru e tn orp ec ss es ro t erh a .sd L gaugna e Depe dn ency A loiv a noit fo la ugn age- ps ecif ci ,selur s at dn a ,sdr ro be ts arp c it ce .s Side Eff ce st A op et itn la dnu e ris ed tni e ar cti no be wt ee n ht e rev ei wed art afi ct a dn mos e hto er pa tr fo t eh s tsy e .m

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aR er oitautiS n nA ni a irporpp eta re snops e ot a is tu ita no th ta si on t a citn ipated in the a fitr a tc . rrE( or ha dn l gni as eps cified ni a rp i ro art fi ca t de ngis c namrofno ce, ton erar sit au t noi .)

tS ruc ut ral hW( ite-B )xo Te ts Si lpm e htaP ehT fault is d te ected yb a te ts case de vir ed to c vo er a si elgn rgorp am el eme .tn Co lpm ex htaP hT e fa lu t is dete tc ed yb a test case derived to c vo er a combina noit of rgorp ma e el em stn .

nuF cti ano l (B al ck-B )xo Te ts

voC re ga e hT e fa lu t si ed et c et d yb a te ts case de vir ed f ro test gni a is lgn e p or ec rud e e( . .g , C f nu c noit ro aJ av me ,)doht wi tuoht c disno e gnir combina noit fo values f ro op iss elb rap ema te .sr

aV ir oita n hT e fa tlu si d te ected yb a et ts c sa e de vir ed ot exerc si e a par cit lu ar com nib ati no fo

paramete sr f ro a gnis el orp ce .erud Se uq en ic ng hT e fa tlu si d te ected yb a et ts ca es vired ed f ro test gni a se uq ence fo corp e erud ca ll .s

tnI re ac oit n ehT fa tlu is detected yb a te ts ca es red ived f ro t tse gni orp ec erud tni e ar c .snoit

tsyS em eT ts

oW rkl tS/dao er ss ehT uaf tl si eted cted gnirud ow olkr ad ro stre ss te .gnits Rec vo re /y E cx e oitp n ehT fa tlu is de et cted hw i el tes it gn e cx e snoitp a dn vocer ery orp ec rud e .s

atS rtup/Re ats tr hT e af ult is de et cted hw ile te gnits ini ait lizat noi c idno it sno gnirud ts a tr pu ro after ylbissop faul yt .snwodtuhs

Hardware noC f ugi r ta i no ehT fa tlu si d te ected hw i el te ts gni eps ci if c ha wdr a er c no f rugi ta .snoi

oS ftw era noC f ugi r noita ehT fa tlu si de et etc d ihw le tes it gn ps eci cif os ft raw e c no fi rug ita .sno

lB ocked eT ts Fa eruli co c rru ed in s tte gni pu eht set t cs enari .o

O CD Cl issa fic ta i no fo tsuC om re apmI ct nI s ballat li ti y bA li yti of the c su t mo er ot ecalp ht e os f wt are otni actual use. U( as bi il ty of the

in ats ll ed os ftwa er si ton ni c dul ed.)

tnI egr yti /Secur ti y orP tect noi of rgorp a sm a dn tad a f mor e ti her accide tn al ro malic oi su de noitcurts ro tla era it ,no a dn f mor nu aut roh ized sid lc os .eru

Perfor nam ce ehT pe cr e vi ed a dn a tc lau pmi act of the sof wt are no ht e mit e re uq ired f ro eht c motsu er a dn c motsu er dne su e sr ot c etelpmo the ri ta .sks Mai tn e nan ce hT e a lib i yt ot c rro ce t, adapt, ro e hn a cn e ht e os tf ware metsys iuq ckly a dn at mi in m la c so t .

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.in S re vice ba i ytil Time yl ed et ct noi a dn d ai ong is s fo fa ruli e ,s iw th minim al c su t mo er pmi a .tc M gi r oita n Ease of rgpu a gnid ot a new s tsy me rel ae se htiw minim al noitpursid ot ex tsi gni c motsu er da at a dn po erat .snoi

oD uc me tatn ion De rg ee to ihw ch vorp ided od cu em stn (in la l f smro , inc dul i gn el tce inor c) com lp e ylet a dn c erro c ylt desc ebir t eh st ur ct eru a dn i tn e dn ed su es fo eht wtfos a .er

libasU ity hT e de rg ee ot hw ci h ht e os tf ware a dn acc pmo a yn i gn od c mu e tn s can eb dnu e dootsr a dn e ff ec vit le y me yolp ed yb t eh e dn su e .r

tS a adn rds hT e de rg ee to ihw ch eht os tf wa er c pmo lies w ti h a cilpp a elb ts a dn a .sdr Re il liba ity ehT a ytilib of the os ftware ot rep f mro ti s tni e dn ed f cnu it no w ti uoh t pnu al nn ed in et noitpurr ro fail .eru Acce ss i tilib y hT e de erg e ot ihw ch pe snosr htiw d asi ilib eit s ac n tbo ia n ht e f lu l benefit of t eh os f wt a er s tsy e .m

libapaC ity hT e de rg ee to ihw ch ht e wtfos are perf ro sm sti i tn e dn ed f nu c snoit c sno si te yltn htiw

od cu em tn ed sys et m re riuq ement .s Re iuq reme tn s hT e deg er e ot ihw ch ht e ys ets m, ni complyi gn w ti h od c mu ent re riuq ements, a tc ua yll

em ets c motsu er e epx cta snoit

O CD Cl issa fic ta i no fo Def ce t T py es f ro Targets ngiseD dna C do e A mngiss e tn / nI i oitazilait n A va ir ab el saw on t a ngiss ed ht e c rro ce t initi la v la ue ro was ton a ngiss ed a yn ini tial va .eul

hC ce ki gn orP ec erud para em et sr ro var ai lb es we er ton porp e ylr v la di ated b fe ero .esu

oglA ir mht M/ e ht od A c rro ce ssent ro efficiency lborp me t tah can be f xi ed yb r mie p el men it gn a si lgn e corp e erud ro loc la da at urts c ,erut iw t tuoh a de ngis cha .egn

nuF cti no /Cl ssa bO/ j ce t A cha gn e ot ht e od c mu e tn ed desi ng si re uq ired to c no f ro m to udorp ct re riuq ements ro tni e fr a ec spe ic f ci ta .snoi

iT gnim / nyS chr zino a it no hT e lpmi ement ta ion timo s en cessary ys nc norh iza it on of hs a der res ruo ec ,s ro loiv ates eht rp escribed nys c zinorh a noit otorp c .lo

tnI fre ace O/ jb ce t-Orie tn ed eM gass es ludoM e tni e afr ec s are incompa lbit e; t sih can cni l edu tnys ca it cally c pmo at bi le in et afr ec s ht at ffid er in mes ant ci tni e rpr etati no fo oc mm nu icated da .at

Rel hsnoita ip toP en it ylla lborp ema it c i tn e ar itc sno ma gno orp ec rud e ,s ylbissop ovni lving id ffere tn a snoitpmuss tub ton ivlovni ng in et rf eca ni c apmo t bi li .yti A doog CR A classi cif a noit s dluoh fo ll wo ht e venu en sid t noitubir of af lu ts ac ssor cate rog ei s. fI , f ro exa ,elpm t eh c rru e tn orp ec ss a dn t eh rgorp a mm i gn ts y el a dn e vn i nor em tn re us lt ni ma yn tni erf eca af u tl s, ew may a pod t a finer c al iss f ci a noit f ro tni e fr ace fa stlu a dn a coa sr e- rg a ni c al ss if ci ita no fo

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.in other ki sdn fo fau tl .s eW am y al et r ht e cla iss ficat noi sch me e in f rutu e orp cej st as a re us tl of ha gniv ide itn fied a dn re vom ed eht ca su es fo ma yn tni erface fa lu t .s

alC iss fica noit of fa stlu uohs ld be suff ci ien lt y rp ce si e ot lla wo di e itn fyi gn no e ro t ow tsom si ing f ci a tn cla ss es of f lua st c no is deri gn seve ir ty, fre uq ency, a dn c tso fo rep ia r. tI si ropmi at tn ot k ee p ni dnim ht at sever ti y a dn repa ri c tso are ton rid ce tly r ale ted. We may have c mso e it c faults t tah are ve yr e px en is ve to repa ri , a dn cri it c la faul st taht ac n be e isa yl repa ri ed. When es lect gni ht e at gr et c al ss of fa stlu , we en ed ot c disno er lla the f ca .srot eW im thg , f ro exam elp , decide to focus no a cla ss of dom erat le y severe fa stlu ht at o cc ru ve yr fre neuq t yl a dn a er ve yr e px e visn e to r me evo , vni e ts i gn ef wer res cruo es ni rp e tnev i gn a m ero seve er cla ss fo fa stlu t tah co c ru rare yl a dn a er ea ylis rep ia re .d

hW en did f ua l st o cc ru , dna when were ht ey f uo n ?d tI si pyt ci al fo m ruta e s wtfo a er corp e ss es ot

c llo ect fa lu t atad fus fic tnei to dete enimr hw en e hca fau tl was det ce ted e( . .g , in tni e rg ita no te ts ro in a ngised i epsn cti )no . nI a tidd i no , f ro ht e cla ss fo faul st ide itn fied ni the f tsri ts ep, we ta te tpm ot dete nimr e hw en soht e stluaf were i cudortn ed g.e( ,. saw a partic lu ar fa tlu udortni ec d ni c do i ,gn or did ti r se ult f mor an e rorr ni arc ih te tc laru desi ng ? .)

yhW did f tlua s o cc ur? nI ht si c ro e CR A s et p, we atte tpm ot art ec re rp e es n at tive af lu ts b ca k ot ca su es, iw th ht e bo ej ct vi e of ide itn fyi gn a " toor " cause a oss ciated htiw am yn fa stlu ni ht e cla ss .

nA la sisy orp cee sd ti e ar t vi le y by ta t me tp i gn ot e lpx a ni ht e e rorr ht at el d to the fault, then the ca esu of ht ta e rorr , ht e ca su e of ht at ca esu , a dn os no . hT e lur e of ht bmu "a ks yhw six mit es" od es ton

divorp e a erp c si e gnippots ur el f ro ht e ana yl is s, tub ggus es st ht ta es veral ets sp may be n ee ded ot f dni a ca esu ni c nommo a gnom a al egr fr ca ti no fo eht af tlu cla ss dnu er c no is dera .noit

The 2/08 0 ro raP eto uR le Fa tlu cla iss ficati no in toor ac su e ana sisyl si j itsu fied yb ht e os -called 2/08 0 ro raP eto ur le. ehT Pare ot ur le si named f ro ht e tI la ian ec mono si t liV fre od Pare ot , ohw in the ae ylr etenin e htn cent yru

soporp ed a m hta ema cit al wop er al w f lumro a to desc bir e the nu eq lau dist noitubir of ew tla h in sih c nuo t ,yr bo es vr i gn that %02 fo eht pe elpo nwo ed %08 fo eht w ae lt .h Pare ot bo es vr ed that ni ma yn upop ital sno , a few )%02( are v lati a dn ma yn )%08( are rt iv ai l. nI af lu t an la sisy , the Par te o ur le op ts ulates that %02 fo the c edo is r se elbisnop f ro %08 of the faul .st

hguohtlA roporp it no s yam vary, eht elur ca rutp es owt mi trop a tn fa stc : 1. Fa lu st te dn ot a cc talumu e in a few ludom es, os di e itn fyi gn top en it ally f ua tl y m ludo es can

mi vorp e eht c tso ef ef c vit ene ss fo fault ed tec .noit 2. oS em class se of fau tl s rp e mod ina et , os re vom i gn the ca su es of a rp e mod inant c al ss of af ul st

ac n have a am j ro pmi a tc no t eh auq til y fo eht corp e ss a dn fo t eh res lu it gn udorp c .t

ehT rp e od im na ecn fo a few cla ss es fo fa lu ts j fitsu ei s foc gnisu no eno cla ss at a it .em

rT ca gni the ca su es of faul st re riuq es e px e ir e ecn , j mgdu e tn , a dn lwonk e gd e of the deve mpol e tn orp ec ss. eW i rtsull ate wi ht a sim lp e exa elpm . I am nig e ht ta ht e fi tsr R AC ts ep di entified me yrom

el ka s sa ht e m tso is ing fi ac tn cla ss of fau stl , c gninibmo a dom erate fre uq ency of co c rru e cn e htiw severe pmi ca t a dn hgih tsoc ot id a song e a dn repa ri . hT e puorg ac gniyrr uo t CR A liw l t yr ot di e itn fy the ac su e of me yrom skael a dn may c no lc edu t ah t am yn fo t eh m res lu t f mor tegrof it gn ot rele esa mem ro y ni exce tp i no ldnah e sr . hT e CR A puorg may tra ec siht lborp em ni ex ec pt noi ha dn il gn ot

egaP 2 41

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cal k of ni f mro ta noi : gorP r mma e sr c 'na t ea is ly ted re nim e tahw en e sd ot eb cle na ed pu in ex ec pt noi ldnah ers. hT e RCA puorg liw l a ks hw y cno e rom e a dn may og b ca k ot a de ngis e rorr : hT e re uos ecr ganam eme tn scheme as mus es amron l lf ow fo c lortno a dn t suh od es ton divorp e e hguon ni f mro a noit

to diug e i lpm eme tn at noi of e cx ep it no ha ldn e .sr niF la yl , the RCA org up may ide itn fy the oor t borp el m ni an ea ylr de ngis lborp e :m Exce tp i lano c dno i snoit were na tfa ert thguoh dealt tiw h tal e ni

de gis n . E ca h ts ep re uq i er s ni f ro itam no a uob t ht e cla ss fo af lu st a dn a tuob the dev le mpo e tn corp e ss t tah nac be ca riuq ed t hguorh insp ce noit of ht e od c mu e tn a noit a dn tni e ivr e sw htiw dev le po ers a dn et ts e sr , tub t eh key ot ccus se s si c suoiru borp i gn hguorht several el v le s fo ac esu a dn fe fec .t How c uo ld f ua l st be rp eve tn e ?d hT e final ts ep of RCA si gnivorpmi the orp sec s yb r me gnivo or ot ca su es ro maki gn ea ylr de et c noit il ke yl . hT e m ae rus es taken may have a ronim pmi a tc no eht deve mpol ent orp ec ss g.e( ,. a dd i gn c no is de tar i no of ex ec tp i no al c tidno i sno ot a de ngis epsni tc oi n c eh ckl )tsi , ro may vlovni e a tsbus a tn i la idom fica it no fo the orp ec ss (e. .g , am gnik e ilpx c ti c disno erati no of ex ec p it no al c snoitidno a part of la l re riuq ements an la sy si a dn de ngis ets )sp . sA in tr ca gni ca su e ,s rp esc gnibir rp eve tn a vit e ro eted c noit em a rus es re riuq se j mgdu ent, kee gnip ni m dni that ht e og al si on t pe efr cti no tub c tso -e ff ect vi e vorpmi eme tn . CDO a dn R AC a er t ow exam lp es fo fee bd ca k a dn mi vorp e ,tnem ihw ch a er an ropmi t tna id em sn i no fo tsom doog os ftwa er corp e ss e .s The lauQ i yt Te ma

hT e uq a ytil plan tsum a ss i ng or el s a dn r se ibisnop il eit s to pe po le. sA iw ht hto er a eps c st of p al ,gninn a ngiss em tn of re bisnops i ytil o cc sru at a rts ateg ci lev le a dn a tact ci al leve .l ehT at c cit al leve ,l re rp esen et d erid c ylt ni the ejorp ct lp an, a ngiss s ilibisnopser ty ot i dn ivi ud a sl in acc dro an ec w ti h the general rts ate yg . It ovni lves balanc gni level of fe f ro t ac or ss time a dn ca er fu ll y am nagi gn pers no al in et ar tc noi s. hT e st ar et ig c level of gro aniz ta noi si re rp esen et d ton ylno in ht e uq a il ty ts rate yg

od cu em ,tn tub ni t eh st ur tc eru fo t eh gro aniza noit sti e .fl

hT e rts eta yg f ro a ss ign gni re snops ib li yti am y be par lt y rd iven by ex et nr al re uq i er em stn . oF r exa elpm , i dn epe dn e tn uq ila yt et a sm may be riuqer ed yb ce itr f ci a noit agencies ro yb a c il e tn

gro a zin a oit n. iddA it no la jbo e vitc es ni lc du e e gnirusn us fficient acc tnuo abili yt ht ta qu la ity tas sk a er ton ae ylis vo erl koo e ;d enc ruo agi gn bo j ce vit e j mgdu ent of uq al ti y a dn rp eve tn i gn it f mor be gni vbus e tr ed yb sche elud rp e russ e; f tso e gnir hs ared co imm t em tn to auq il yt gnoma all et ma em bm e ;sr

a dn dev le po i gn a dn c cinummo ati gn hs ared lwonk edge a dn va ul es rega dr i gn uq a il t .y E ca h of ht e lbissop e gro a zin ati sno of lauq ti y lor es mak se mos e ejbo c vit es sae ier ot ac ih eve a dn

mos e rom e ch la le gnign . noC f il tc of no e dnik ro a on ther is ni e tiv able, a dn ther fe ro e in gro a zin ing the et ma it si ropmi t na t to er c zingo e eht c no fli stc a dn take m ae rus es to c no t lor a vd e esr c sno e uq ences. If an udividni la lp a sy t ow lor es in top ential c no f il ct e( . .g , a deve pol er snopser i lb e f ro de vil e gnir a tinu no sche ud el si a osl re nops is lb e rof tni e rg ita no et s it gn ht at c dluo rev ae l fa stlu t tah delay de vil e )yr , ht ere tsum be c tnuo e emr a rus es to c ortno l the ri ks s hni ere tn in aht t c no f il ct. If or l se are a ngiss ed ot ffid ere tn vidni i ud a sl , ht en the c rro e dnops i gn ksir si c no flict be wt ee n ht e i dn ivi ud a sl ( .e ,.g if a deve pol er a dn a tester od ton ade uq ate yl share vitom ita no to deliver a uq a ytil udorp ct no sche lud )e .

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nA i dn epe dn ent a dn a suomonotu et s it gn et am il es at no e e dn of the sp ce urt m of op iss lb e et am gro a zin a noit s. nO e ac n am ke ht ta t ae m gro a zin a noit lla y dni epe dn ent os that, f ro exa elpm , a orp ject am nager htiw sche lud e rp e ss ru se can neither pyb a ss uq a ytil itca v ti i se ro ts a dn a sdr , ron r llae oc ta e

pe po el f or m te gnits ot dev le po em tn , ron op ts nop e uq la ity ac tivit ei s nu til t oo late in ht e orp j .tce Separ ta gni uq ila yt or el s orf m deve pol ment lor es im n mi zi es the ksir of c no fli tc eb t ew en lor se lp a ey d

yb an i dn ivi ud a ,l a dn uht s am kes m so t se sn e f ro lor se ni ihw ch dni epe nedn ec si para tnuom , us ch sa f ni al ys ets m a dn acce natp ce te gnits . nA dni epe dn ent et am de tov ed ot uq ali yt ca itivit es a osl has an a vd a tn age ni iub ldi gn ps ecific e px er it ,es us ch as te ts desi ng . hT e irp am yr ksir ari gnis f mor separati no si ni c no lf ict be ewt en og a sl fo eht dni epe dn e tn uq la ity et am a dn eht dev le po e .sr When uq a ytil tas sk are rtsid i ub et d a gnom spuorg ro orga zin a snoit , ht e nalp dluohs inc dul e eps cif ci c eh cks to ens ru e cus c sse ful compl noite of uq ali yt ca tivit ei s. roF ex ma elp , hw en m ludo e te gnits is perf mro ed yb deve pol e sr a dn inte rg ati no a dn tsys me tes it gn si perf mro ed yb an i dn epe dn ent auq l yti et ma , ht e ytilauq et am s dluoh ch ce k ht e c lpmo e net e ss of ludom e te sts pe rofr em d yb develo ep sr , f ro

exa elpm , yb re riuq i gn tas si fa noitc of c vo erage c tir eria ro i epsn itc gn ludom e test tius e .s If te gnits is perf mro ed yb an i dn epe dn e tn gro aniz ita no dnu er c no t ar ct, ht e c rtno ca t s dluoh ca er f lu ly descri eb t eh et ts i gn corp e ss a dn sti re us l st a dn od c mu e tatn i ,no a dn the c il e tn gro a zin a noit uohs ld ve ir fy

sa fsit ca yrot com lp eti no fo t eh c no tracted ta .sks tI am y be l go i ts ci a yll elbissopmi to m ia tn a ni na i dn epe dn e tn uq a ytil puorg , epse cial yl ni ms all

orp cej st a dn gro aniz ta noi s, hw ere flexi ytilib ni ssa i mng en st is e ss en it la rof re ruos ec manageme .tn disA e f mor the l tsigo ical ussi es, noisivid of r se libisnop ti y cre ta es a idd ti no al krow ni c inummo c noita

a dn c droo ina noit . yllaniF , uq a ytil itca v iti se often dema dn d ee p wonk el gd e of the orp jec ,t par cit lu a ylr at de liat ed vel le s (e. .g , inu t a dn ea ylr tni e rg ati no et ts ). nA distuo er w lli have le ss hgisni t into woh a dn hw at to test, a dn may be nu a lb e to e ff ec vit le y ac yrr uo t ht e c ur cial ae ilr er ac it v ti ie ,s us ch as e ts a ilb shi gn a cc ep at cn e c tir e ir a a dn rev ei gniw arc etih c rut al de ngis f ro et ts abi ytil . roF la l

these er a snos , even zinagro ati sno ht at r le y no an dni epe dn e tn ve ir fi ac ti no and va dil ita no VI( &V) puorg f ro f ni la cudorp t uq la ifi ac noit a coll ate hto er res libisnop ti ies ot deve pol e sr a dn ot auq l yti

orp ef noiss a sl krow i gn rom e cl so e yl htiw t eh dev le po em tn et a .m

hT e m ro e devel mpo ent dna uq al yti or el s are c nibmo ed a dn i tn er im xed, the rom e mi trop ant ti is ot iub ld into the lp an c eh c sk a dn ba al nc se ot be ec tr a ni ht at uq a il ty ca ti iv ties a dn ejbo ctive a ss e ss me tn

are ton ae is ly t sso ed a dis e sa d ae ld i sen mool . roF exa elpm , PX rp ca ecit s kil e "te ts f ri st" t go e eht r htiw pa ri rgorp a mm i gn ( is debar no page )183 ug a dr aga tsni mos e of eht hni ere tn r si sk of gnixim

lor se . Separate lor se od on t necessar li y ylpmi seg er ga noit of auq l yti ca ti iv eit s to tsid inct i dn i udiv a .sl tI si op iss b el to a iss ng htob deve mpol ent a dn uq ila yt re nops is bi ytil ot ved e ol pe sr , ub t ssa i ng owt

i dn i udiv la s di nits tc re ilibisnops ties f ro ae ch deve mpol ent krow udorp c .t P ee r rev ei w si an ex ma elp of m gnixi or el s hw ile m ia tn a gnini i dn epe dn ence no an ti em- yb - eti m ba sis . tI si osla lbissop e f ro deve pol e sr a dn tes et sr ot pa tr i pic ate t go te her ni emos ac itivit e .s Ma yn var itai sno a dn dirbyh m do e sl of gro a noitazin ac n be de ngis ed. moS e gro aniz ta snoi ha ev

tbo a ni ed a doog bala cn e of be en fits yb tor a it gn r se ibisnop til ies. roF xe ma elp , a deve pol er yam m vo e into a lor e mirp a ir ly re nops is b el f ro uq a il ty ni no e orp j tce a dn vom e b ca k otni a re lug ar deve mpol e tn lor e ni eht ne tx . nI gro aniz ta snoi la gr e e hguon ot have a cnitsid t ilauq ty ro te gnits

puorg , an a rporpp ai te lab a cn e b te w ee n i dn epe nedn ce a dn tni e rg ita no cipyt a yll var ei s ca ssor el vels

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.in of orp ject gro aniz ita .no At os em el v le s, an a rporpp ai te balance c na be urts ck yb giving res ibisnop lity f ro an ac ivit ty (e.g ,. tinu t tse )gni to deve pol e sr ohw wonk ht e c do e be ts , tub w ti h a separa et vo e sr i thg re ilibisnops ty hs ared yb me bm e sr of ht e uq a il yt team. roF e ax m elp , tinu te sts

am y be d ise ng ed a dn pmi el men et d yb dev le po e sr , tub rev ei wed yb a m bme er fo the uq a il yt te ma for e ff ec vit e a amotu it no p( a tr ci lu a ylr , ius at lib ti y f ro ua t mo ated re rg e noiss t tse e ex cut noi as ht e tcudorp e ov lve )s as we ll sa t nhguoroh e ss . ehT lab ance ti sp f htru er wot a dr i dn epe dn e cn e at hgih er lev sle fo rg a lun a ytir , us ch sa in tsys me a dn ca ec tp an ec te gnits , hw ere ta el a ts os em te ts s s dluoh be de ngis ed

i dn epe dn e tn ly yb em srebm fo eht uq la ity et a .m

ruostuO c gni et ts a dn a sisylan itca v iti es si s mo e mit se mo it vated yb the per ec noitp ht at te ts i gn si le ss cet cinh lla y dema dn i gn ht an deve mpol e tn a dn nac be ca irr ed tuo yb wol er-p ia d a dn wol er- iks l del

i dn i udiv la s. hT is c no f esu s et ts e ex c tu i no , ihw ch uohs ld in fact be rts a wrofthgi a dr , w ti h ana sisyl a dn et ts de ngis , ihw ch are as dema dn i gn as d ise ng a dn rp o rg a gnimm ta sks ni deve mpol e tn . Of c ,esruo el ss lliks ed i dn i udiv la s ac n de ngis a dn ca yrr tuo te sts , j tsu as el ss lliks ed dni i udiv als c na de ngis a dn rw ti e rgorp ma ,s tub ni htob c sa es t eh re stlus a er kilnu e yl to eb s ta fsi act .yro

ruostuO c gni can be a nosaer a lb e a orpp ca h hw en sti bo jectives are on t mere yl m zimini i gn c tso , tub

am zimix i gn dni epe dn e ecn . roF exa elpm , an i pedn e dn e tn j mgdu e tn of uq la yti may be pa itr c lu arly v la ua lb e f ro fi lan sys t me a dn ac ec p at nce et gnits , a dn may be e ss e itn al f ro em a rus i gn a cudorp t aga tsni an dni epe dn e tn uq al ti y ts a dn a dr e( . .g , auq fil gniy a udorp tc f ro em d ci al ro a inoiv c su e). tsuJ as an gro a in zati no w ti h xim ed lor es re riuq es eps c ai l atte tn i no ot a ov id ht e c no f il c st eb t ew en lor es p al yed yb an i udividn a ,l radic la separati no of nopser is bi ytil re uq ires s ep c ai l tta en it no to c lortno c lfno i stc be wt een t eh auq il yt a ss e mss e tn et am a dn eht dev le mpo e tn et a .m

hT e p al n tsum elc arly define lim e nots es a dn de vil e yr f ro uo t ruos ec d ca tivit ei s, as w lle as c eh c sk no the uq al ti y of de vil e yr in ob th rid ect snoi : Te ts gro a zin a snoit usu ally perf mro iuq ck ch ce sk ot ve ir fy the c tsisno ency of the s fo tware to be te ts ed w ti h pser e tc to mos e mi amin l "t se ta lib ti y" re uq i mer e ;stn c il ents usu lla y c eh ck ht e c lpmo e et ness a dn c sno si tency fo te ts re stlus . roF exa elpm , te ts

gro a zin a snoit may a ks rof ht e resu tl s of ins ep c snoit no ht e de vil ered itra fact bef ro e ht ey s at rt et ts i gn , a dn may ni cl edu s mo e iuq ck et sts ot ver fi y ht e atsni lla ib il yt a dn te ts a ib il yt of the a fitr a .tc lC ie stn may c eh ck th ta tes st satisfy eps ci eif d f nu c noit al a dn urts c rut al c vo erage c tir e air , a dn may

ins ep ct the te ts od c mu e tn a noit ot c eh ck i st ytilauq . htlA hguo the c rtno act dluohs deta li eht rela noit be ewt en ht e dev le tnempo a dn ht e tes it gn org ups, lu t mi ta yle , uo t gnicruos r eile s no utum la urt ts be ewt en gro a zin ati .sno D co tnemu i gn nA a yl s si a dn eT st

Ma rut e os ftware orp ec ss se ni cl du e od c mu e tn a noit ts a dn a sdr f ro a ll ht e a tc vi i it es of the sof wt a er orp ec ss , i gnidulcn t se t dna an la sisy ac itivit es. oD c mu en at noit ac n eb psni ce ted to ve ir fy rgorp e ss

aga tsni sche ud le a dn uq tila y og a sl a dn ot identify lborp ems, roppus it gn orp ec ss vi libis ti ,y m ino t ,gniro a dn re cilp a lib ti .y Over weiv

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oD c mu e tn ati no si an tropmi ant eleme tn of ht e os ftware deve mpol e tn corp e ss , inc dul i gn ht e uq al yti orp ec ss. Comp el te a dn we ll - ts ur c rut ed od c mu e stn inc er ase the re su a ib l yti of te ts suites iw thin a dn

ac ssor ejorp c st . oD c nemu ts are e ss e itn al f ro ma ni iat ni gn a ydob of lwonk e gd e ht ta can be re su ed ac ssor orp jec st . snoC i tnets od cu em stn vorp ide a bas si f ro m no ti gniro a dn as es gniss ht e orp ces ,s

htob in et nr a yll a dn f ro e tx e nr la aut roh i it se rehw e cer it cif ati no si de deris . niF ally, od c mu e tn ati no inc dul es mus am zir i gn a dn rp esent gni data ht at f ro ms t eh ba sis f ro corp e ss vorpmi eme .tn Te ts a dn an la sisy od c mu e tn a noit ni c sedul us mma yr od c mu en st de is ng ed rp ami ylir rof uh am n c rpmo ehe noisn a dn deta sli acce ss i elb to eht muh an rev ei wer tub d ise ng ed rp ami ylir f ro a otu am ted ana yl is .s

oD c mu ents are vid ided ni to t erh e m ia n cate rog ei :s lp a nn i gn , ps e cific a noit , a dn re rop it gn . Pl gninna od c mu ents desc bir e ht e gro a zin a noit of the uq a il ty orp ec ss and in lc du e rts ate ig es a dn lp a sn f ro the

di siv i no ro ht e c pmo a yn , a dn p al sn f ro i dn ivi ud al orp jec st . epS c fi ic ta i no od c mu e stn desc bir e te ts us eti s a dn et ts ca ses . A oc mp el te s te of a an l sy si a dn et ts ps ce if ci ati no od cu em n st ni lc du e et ts desi ng ps e ic f ci a noit s, te ts case ps ec fi ci ati no , c eh c lk ist ,s a dn an la y is s orp ec rud e spec fi ci ati sno . Re gnitrop od c mu ents ni cl edu d te slia a dn mmus a yr fo a an l sy si a dn te ts re .stlus

rO zinag i gn oD c mu e tn s

nI a ams ll orp je tc htiw a fus ficie tn ly ams ll s te of od c mu ent ,s ht e a rr a gn eme tn fo hto er orp cej t

ar it af c st e( . .g , re stnemeriuq a dn de ngis od c mu e tn s) got te her htiw ts a dn a dr c tno ent e( . .g , ma gnipp fo tsysbus me et ts tius es ot ht e iub ld cs he lud e) divorp es fus f ci ei tn gro a zin ati no to navigate hguorht t eh

c llo ec noit of t tse a dn anal sisy od cume tn a noit . nI al gr er orp jec st , ti si c nommo rp ca cit e to udorp ce a dn re ug al ylr dpu a et a bolg al ug i ed f ro naviga gnit a gnom udividni al od cu em .stn Na im gn c vno e tn i sno he pl ni uq ci ylk ide itn fyi gn doc mu e stn . A pyt aci l sta dn a dr f ro od c mu ent man es

uow ld ni lc du e ke sdrowy i dn ica it gn the gene lar cs po e of ht e od c mu e tn , ti s na erut , the eps cif ci od cu em ,tn a dn its ve ,noisr as in rugiF e 8. .4

ugiF re 8. :4 aS mple od c mu e tn na im gn c vno e ,snoitn c ilpmo a tn wi ht EEEI ts a dn a .sdr

ihC p knum oD uc me tn eT pm l ta e oD cume tn Ti elt

ppA r avo ls

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ssi ued yb man e is ang t eru ad te

a vorpp ed yb man e is ang t eru ad te

d tsi tubir i no sta sut ( tni re lan esu ,ylno re tcirts ed, )…

d tsi tubir i no l tsi p( e po el ot w moh eht mucod e tn tsum eb sent)

iH ots ry

ve oisr n desc noitpir

T ba el fo tnoC e stn Li ts fo se .snoitc

Su amm ry zirammuS e ht e c tno en st of ht e od c mu e tn . hT e rammus y dluohs cle lra y nialpxe the rel ve ecna

of t eh od c mu e tn to i st elbissop su e .s G slao fo eht od c mu e tn D cse ebir eht up r esop fo iht s od c mu e :tn ohW s dluoh re da ,ti dna why?

Re iuq red od c mu e stn and re ef re cn es orP iv ed a refere cn e to hto er od c mu en st dna itra f ca ts n ee ded f ro dnu e dnatsr i gn dna

xe plo iti gn siht od c mu e .tn orP v edi a r ta i elano rof eht vorp ided refere cn e .s

lG o ss ary orP iv ed a glo ss ary fo te mr s re uq eri d ot dnu e sr t dna t sih od c mu e .tn

S ce noit 1 …

S ce noit N …

eT ts tS r ta e yg oD c mu ne t

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sisylanA dna Test nalP While ht e f mro at of an na a yl is s a dn test tarts e yg vary f or m compa yn ot compa yn , the st ur ct ru e of na an la sisy a dn et ts p al n si m ero ts a dn a zidr ed

hT e vo erall uq a il ty lp an usu a yll c rpmo ises seve ar l vidni i ud al lp ans of imil et d sc epo . aE ch te ts a dn an la sisy lp an s dluoh i idn ac te the ite sm to be veri if ed hguorht an la sisy ro et s it gn . hT ey may ni clu ed ps e ic f ci a snoit ro od cu em stn to be i epsn cte ,d c do e ot be anal zy ed ro et s et d, a dn tni e afr ce ps e ic f ci a snoit ot dnu e ogr c no is ts ency ana sisyl . hT ey am y re ef r ot ht e ohw el ys ets m ro pa tr of it - il ke a ysbus ets m ro a es t of nu ti s. Where the orp je tc p al n inc dul es lp a nn ed dev le mpo e tn increment ,s

t eh ana sisyl a dn te ts lp an cidni a set eht a cilpp a elb ve sr i sno fo eti ms ot eb verifie .d

roF cae h it me , the lp an uohs ld i dn ci a et a yn ps eci la ha wdr are ro e tx e nr al fos tware re riuq ed f ro et ts i gn . roF exa ,elpm t eh lp an mi thg cidni ate ht ta no e tius e of ysbus ts me stset f ro a es c ytiru p ca kage

can eb ex ce u et d wi ht a os ftware alumis t noi of a s am tr ca dr er ade ,r hw ile a on ther tius e re uq ires acc sse to ht e cisyhp al de iv ce. Fina yll , f ro each item, ht e lp an uohs ld referen ec related

od cu em tn ati no , us ch as re uq ireme stn a dn de ngis ps ecif ci a snoit , a dn su er, tsni allat oi ,n a dn po era snoit diug e .s

nA E cx re tp fo eht hC pi mu kn anA l sy si dna Te ts tS r ta e yg

oD uc me tn 50PC - 41 . :30 anA ly is s dna eT ts tS r ta e yg …

lppA ci lba e adnatS r sd dna Pr co edu er s

trA fi act ilppA c elba St sdradna dna diuG elines

eW b a pp lic noita Acc isse bi :ytil W3C-W IA …

eR su a elb com nop e tn (i tn e nr a yll deve pol e )d psnI ce noit orp ce rud e: [WB 21 - 30 . ]21

txE e nr al c nopmo e tn uQ ali if ca it no orp ce rud e: [WB 21 - 22 . ]40

oD uc me tatn ion atS ndar sd

orP ject od c mu e stn m tsu be arc vih ed acc gnidro ot the sta dn a dr knumpihC archive corp e erud [WB 20 - 10 . 20 ]. S at dn a dr re uq ired od c mu e stn edulcni

oD c mu ent C no et tn & nagrO i az t noi adnatS rd

uQ ila yt lp an [WB 60 - 10 . ]30

Te ts ed is ng eps cific ta i sno [WB 70 - 10 . ]10 p( er te ts ius et )

Te ts ca es eps cificati sno [WB 80 - 10 . ]70 p( er te ts ius et )

Te ts l sgo [WB 01 - 20 . ]31

Te ts mmus a yr re rop ts [WB 11 - 10 . ]11

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.in I psn ce noit re rop ts [WB 21 - 90 . ]10

sisylanA dna Test Ac tivit ei s T oo ls

hT e f lo l gniwo t oo ls a er a vorpp ed a dn dluohs be u es d ni a ll devel mpo ent ejorp c st . cxE e tp i sno re eriuq c no fi rug ita no co mm tti ee a vorpp al a dn tsum eb od cu em tn ed ni t eh jorp ce t p al .n

Fa tlu iggol ng Chipm knu B Tg [WB 01 - 32 . ]10

…

atS ff dna loR es

A dev le mpo e tn krow tinu c sisno st of inu t s ruo ce c ,edo cni l du i gn nu it test cases, ,sbuts a dn ha nr e ss es, a dn tinu te ts od cu em n at ti no . A nu ti may be co imm tt ed to ht e orp cej t bas nile e hw en t eh

ruos ec c edo , te ts cases, dna et ts r se ults have pa ss ed ep er rev ei w. A et st dna an la y is s lp an may on t a rdd e ss a ll a eps c st of os ftware uq ali yt a dn et gnits itca v ti ies. tI uohs dl i dn ci a et ht e fe ta ru es ot eb ve ir fied a dn soht e that are ex lc du ed f mor c sno i red a it no usu( la yl eb ca su e re ilibisnops ty f ro them is p al ced le se hw ere). roF xe ma elp , if the ti me ot be ve ir fied ni c dul es a rg a hp ci al su er tni erf eca , t eh et ts a dn ana sisyl lp an hgim t ts a et ht ta it ed als ylno iw th f nu itc no al porp e itr es a dn on t htiw su a lib ti ,y

ihw ch is to be ve ir eif d separate yl yb a su abili yt a dn muh an tni e cafr e desi ng et am. cilpxE it i dn ic ita no of fe ruta es on t ot be et ets d, as well sa t oh es inc dul ed in an na a syl si a dn test p al n, si mi trop ant f ro as es gniss c lpmo ete ne ss of ht e vo er la l s te of a sisylan a dn t tse ac it v ti ies. noitpmussA

that a feat eru on t c disno ered in the c rru e tn lp an is c vo ered at a on ther op int si a am j ro ca su e fo im gniss verifica it no ni gral e jorp ect .s

hT e uq a ytil lp an must cl ae ylr idni ac te criteria f ro ed ic di gn eht ccus e ss ro f ia erul of each lp a nn ed

activ ti y, as w le l as the idnoc it sno f ro s psu e dn i gn a dn re gnimus an la sisy a dn et ts . hT e c ro e of an an la sisy a dn t se t lp an is a det ia led sche ud le fo tas .sk ehT sche lud e si usu ally sulli trated iw th

TTNAG a dn TREP d ai rg a sm gniwohs the rela noit ma gno t sa ks as well as rieht r ale ti no ot other orp cej t lim e senots . hT e sche lud e ni cl du es ht e allo ac noit of timil ed re cruos es p( artic lu arly st fa f) a dn

i dn ic ta se re ilibisnops ty rof rere ruos ec s a dn re nops is bi itil es .stlus A uq a il yt lp an od c mu e tn dluohs a osl ni lc du e an e px l ci ti ri ks lp an iw th c no t gni encies. sA far as

elbissop , c no it gn encies dluohs ni lc du e nu ambi suoug rt i gg e sr (e. .g , a tad e no hw ci h a c no it gn ency is activated if a pa tr ci lu ar ta ks has on t eb comp el )det as we ll as r ce vo e yr pro ec rud es. Fina yll , ht e te ts a dn an la y is s lp an s dluoh cidni ate cs aff gnidlo , aro lc se , a dn a yn hto er os f wt are ro ha wdr are roppus t re riuq ed f ro te ts a dn ana sisyl ac itivit e .s

Te ts De ngis pS ce ific noita oD c mu e tn s De is ng od c mu ent noita rof et st ius et s a dn t tse ca es s se vr e e ss enti lla y ht e as me soprup e as rehto os f wt are desi ng od c nemu at noit , gnidiug f htru er dev le mpo e tn a dn rp epa gnir f ro m ia n et na ecn . seT t

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Vtusolution.in

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.in us eti desi ng tsum ni lc du e all the ni f mro ita no needed f ro itini al s le tce i no of et ts ac ses a dn am tni ena cn e of ht e te ts ius et vo er ,emit i gnidulcn r ita no la e a dn a citn ipated e noitulov . pS e cific a noit

fo i dn i udiv al te ts c sa es ni lc sedu ,esoprup su age, a dn antic api et d cha gn e .s

A adnatS rd rO noitazinag fo na sisylanA dna Te ts Pl na

sisylanA dna te ts ite :sm hT e i et sm ot be te ts ed ro ana zyl ed. hT e desc tpir i no of each ti me idni ac tes ve noisr a dn

in ats l al noit orp ec rud es that may eb re uq ire .d Fe uta er s ot eb te ts e :d ehT feat ru es c disno ered ni eht lp a .n

Fe uta er s ton ot eb te :dets F ae rut es ton c disno ered ni eht c rru e tn pla .n

ppA r ao c :h hT e vo era ll a an l sisy a dn te ts a aorpp ch, fus ficie tn ly detailed to perm ti nedi it f ci ita no of t eh

maj ro t se t a dn an la isy s sksat a dn e mits at noi fo emit a dn res cruo e .s

liaF/ssaP c ir et r :ai Ru el s ht at dete enimr eht tats su fo an art fi ca t s ejbu cted ot ana sisyl a dn te .ts

uS spe noisn dna er mus p noit cr eti ir :a C dno iti no s to rt i gg er su ps e sn i no of te ts a dn a sisylan ca itivit es e( . .g , an e cx e viss e af eruli

r eta ) a dn c snoitidno f ro r se tart gni ro re us m gni an ca tiv ti y.

sksiR dna c gnitno en eic :s R sksi f ro e es en hw en de ingis ng the p al n a dn a c gnitno ency p al n f ro each of the ide itn fi de

sksir . De il ev r ba el s: A tsil a ll T&A artifac st dna od c mu ents ht at tsum eb cudorp e .d

Task dna sche lud e: A c elpmo et desc noitpir of ana sisyl a dn te ts sksat , ler a snoit a gnom ht em, a dn re al t snoi

be ewt en A&T a dn deve mpol e tn ta sks , htiw er s ruo ec a oll ac noit a dn rtsnoc a stni . A ta ks cs he elud usu ally ni c dul es TTNAG a dn EP RT id a rg a .sm

atS ff dna res nop is lib i it e :s S at ff re riuq ed f ro perf gnimro ana yl sis a dn te ts ca tivit ei s, ht e re uq ired iks ll s, a dn ht e

allo ac noit of re snops i itilib es ma gno spuorg a dn vidni i ud a .sl ollA ac noit of re cruos es to ta sks is described in t eh sche .elud

E vn ri mno e latn n ee :sd Ha wdr a er a dn os ftwa er re uq ired ot perf mro a sisylan ro te ts i gn a itc v ti ie .s

Test desi ng eps cifi ac ti no od c mu e stn desc ebir c lpmo ete te ts tius es .e.i( , se st of et ts ac ses that f suco no pa itr c lu ar a ps ects, eleme stn , ro hp ases of a os f wt are ejorp c )t . hT ey am y be divided into ,tinu

egaP 2 22

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.in in et rg a noit , ys ets m, a dn ac ec p at n ec te ts suites, if we gro a zin e them yb the rg a lun ar ti y of ht e te ts s, ro f tcnu i lano , st rutcur al, a dn perf mro a cn e t tse us i set , if ht e mirp a yr gro anizat noi si based no te ts

jbo e tc iv se . A large ejorp ct may inc dul e ma yn test de ngis eps cifi ac snoit rof et st s iu t se fo id ffere tn ki sdn a dn rg a lun a ir ty, a dn f ro id ffere tn ve snoisr ro c no f rugi a snoit of the sys et m a dn its co pm no e .stn Test ed is ng sp ce if ci ati sno ide itn fy ht e fe ta seru they are tni e dn ed to ve ir fy a dn eht a aorpp ch su ed ot select test ac ses. eF at seru to eb t tse ed s dluoh eb c ssor -refere cn ed ot re vel ant parts of a s wtfo a er ps e ic f ci a noit ro desi ng od c mu e tn . A te ts de ngis ps ce if ci a noit sla o ni c dul es desc noitpir of the te gnits orp ec erud a dn pa f/ss a li criteri .a ehT corp e rud e i cidn ates ts e sp re riuq ed ot tes pu eht te ts i gn

e vn i nor em nt a dn perf mro eht te sts , a dn inc sedul refere cn es ot acs ff ol gnid a dn ro ca le .s Pa /ss af il cr eti ir a ugnitsid i hs suc ssec f mor fa ruli e of a et ts su ti e as a elohw . nI eht s mi ple ts ca es a set t etius e ex cut noi may eb dete nimr ed ot have f ia led fi a yn udividni al te ts case e ucex t noi af ils, tub ni sys t me a dn acce tp a cn e test gni it si c nommo to es t a relot a cn e lev le aht t may depe dn no ht e un mber a dn severi yt fo f ia l ru e .s

eT ts a dn anA ly is s Re op r st

eR rop st of test a dn a sylan si re lus st se vr e htob deve pol e sr a dn te ts de ngis e sr . hT ey ide tn fi y po en af u tl s f ro deve pol e sr a dn a di ni sche gnilud fixes a dn rev isi sno . hT ey h le p et ts desi ng e sr sa se ss a dn

ref ni e the ri a aorpp ch, f ro exa lpm e, gniton hw en mos e class of fa stlu is e cs a gnip ae ylr test a dn an la sisy a dn s gniwoh pu ylno ni tsysbus em a dn ys ets m tes it gn

nuF cti ano l eT ts De ngis pS ce ific ta i no fo c eh ck noc fi ug r ta i no

eT ts uS ite dI e tn ifier

BW 70 - 51 . 10 Fe uta er s ot Be eT ts ed

nuF c oit nal t se t f ro c eh ck c fno i rug a ,noit udom le ps ecif ci a noit BW 20 - 51 . 23 .[a]

ppA r ao ch Combi rotan ai l f noitcnu al et st of feat ru e param ete sr , e mun erated yb cate yrog - pa itr it no tem doh vo er parameter ta elb no page 3 fo this od c mu e tn .[b] Pr co e ud er De is ng ed f ro c lanoitidno ni c noisul ni ni thg ly te ts nur . iuB ld ta gr et 20T 51 23 11 inc sedul nUJ it ha nr e ss a dn aro c sel , w ti h et ts re rop ts di er cted ot ts a dn a dr te ts gol . Test e mnorivn e tn ni lc du es ta elb

DM B 51 23 30 f ro loadi gn ini lait tset ad at ba es .etats

eT ts c sa es[c]

BW 70 - 51 . 10 .C 10 am lf ro em d dom el un m reb

BW 70 - 51 . 10 .C 20 m do el un mber ton ni BD

… …

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BW 70 - va dil m edo l un mber htiw a ll legal re riuq ed stols a dn some legal lanoitpo

51 . 10 . 90C [d] stols

… …

BW 70 - 51 . 10 .C 91 e pm ty m do el BD

BW 70 - 51 . 10 .C 32 m do el BD w ti h a gnis le le eme tn

BW 70 - 51 . 10 .C 42 e pm ty c nopmo e tn BD

BW 70 - 51 . 10 .C 92 c nopmo e tn BD w ti h a si lgn e le eme tn

liaF/ssaP C ir t re noi

uS cce ss f lu c lpmo te noi uqer ires c erro ct e ex c noitu fo all te ts cases wi ht on loiv a snoit ni te ts .gol

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oN . fo po it lano slots f ro s le ected m do el S#( M SO ) ma yn

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oN . fo re uq ired c nenopmo ts htiw es el c noit

≠ e ytpm = .oN fo re riuq ed slots

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≠ e ytpm < .oN fo noitpo al sl sto

eR riuq ed com nop e tn se el cti no la l v la di

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oN . fo m do e sl in BD ma yn

oN . fo c nopmo e stn in DB ma yn Te ts case:

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R MA 215 BM

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tsys tame ica ll y, ra ht er ht an j gnipmu e rr ta ci a yll orf m borp el m ot elborp m a dn wast gni emit no cilpud ate re strop . hT ey tsum be rp i tiro ized so ht at fe f ro t si ton s uq a dn ered no faul st of rel ita ve yl

im ron im trop ance elihw c itir ac l faul st a er ne lg ce ted ro even f ogro ett .n

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Vtusolution - VTU Solution - Get Vtu notes & question …vtusolution.in/uploads/9/9/9/3/99939970/software_testing...vtusolution.in S sytem te stin g guid eli ne s, ASF (Ato icm System