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1 TEST SETS the general method data models extraction functional criterias data sets data sets before tests selection test execution extraction anonymisation / data generation analysis / validation data sets after tests execution traces test coverage

TEST SETS the general method

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TEST SETS the general method. data models. selection. extraction functional criterias. extraction. data sets. anonymisation / data generation. data sets before tests. test execution. data sets after tests. execution traces. analysis / validation. test coverage. - PowerPoint PPT Presentation

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Page 1: TEST SETS the general method

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TEST SETSthe general method

data models

extraction functional criterias

data sets

data sets before tests

selection

test execution

extraction

anonymisation / data generation

analysis / validation

data sets after tests execution traces

test coverage

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FUNCTIONAL SUBSET

derived from the model

…and the usage graph

SELECTIONfunctional division

The subset is the minimum list of necessary tables

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VOLUMETRIC SUBSET

rules definition for all the functional

subset attributes

SELECTIONvolumetric division

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RULE TYPES

1. Simple criteriax = valeur

2. borders

x <= 0007 0008 <= x <= 0011 0012 <= x

3. Addition or suppression of known records

SELECTIONvolumetric division

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extraction method

F = first record S = 1 amongst N R = random number

combination types U = union I = intersection X = exclusion

processingsequence

nbr of records to be extracted, per type

ResultsPrimary keys

69713694191178948751743735477533857145089472608114583220876524177596810 10001100091001610017

SELECTIONvolumetric division

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data models

extraction functional criterias

data sets

data sets before tests

selection

test execution

extraction

anonymisation / data generation

analysis / validation

data sets after tests execution traces

test coverage

TEST SETSthe general method

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EXTRACTION

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Extraction combination coming from different DB

ABCDEFGH…XYZT

Schema 1

ExtracteurDB 1

69713694191178…10017

Key 1

Key 2

Schema 2

Rule 1

Attribute 1 = ‘X’

Rule 2

Attribute 2 = ‘Y’

DB 2Extracteur

Links element

Data 1

Data 2

EXTRACTION

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data models

extraction functional criterias

data sets

data sets before tests

selection

test execution

extraction

anonymisation / data generation

analysis / validation

data sets after tests execution traces

test coverage

TEST SETSthe general method

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ANONYMISATION

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ListeRS.txt

To define the data anonymisation rules

The values of the attribute “RAISON SOCIAL” are read in the file "D:\Dgi\Database\ListeRS.txt"

RAISON SOCIALE 00001RAISON SOCIALE 00002RAISON SOCIALE 00003RAISON SOCIALE 00004RAISON SOCIALE 00005RAISON SOCIALE 00006RAISON SOCIALE 00007RAISON SOCIALE 00008RAISON SOCIALE 00009…

ANONYMISATION

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ANONYMISATION

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For each record “ SUPPORT JURIDIQUE” create randomly 1 to 3 record TIERS

DATA GENERATION

Generation rules

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Contents generation

DATA GENERATION

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data models

extraction functional criterias

data sets

data sets before tests

selection

test execution

extraction

anonymisation / data generation

analysis / validation

data sets after tests execution traces

test coverage

TEST SETSthe general method

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Use of the extractors to obtain the data sets to be compared

DB before tests

69713694191178948751743735477533857145089472608114583220876524177596810 10001100091001610017

Extractor

DB after tests

ANALYSIS / VALIDATION

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To define comparison criteria’s

Looking for differences

Some attributs might be different

ANALYSIS / VALIDATION

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<SUPPORTJURIDIQUE IDSJU = "53385"  <TIERS IDTIERS = "85524"  <DEFAILLANCE IDDEFAILLANCE="80307"/> > </TIERS> <TIERS IDTIERS = "85523" > </TIERS></SUPPORTJURIDIQUE>

<SUPPORTJURIDIQUE IDSJU = "53385"  <TIERS IDTIERS = "85524"  <DEFAILLANCE IDDEFAILLANCE="80307"/> > </TIERS> </SUPPORTJURIDIQUE>

record TIERS 85523 was cancelled

DB 1 extraction DB 2 extraction

ANALYSIS / VALIDATION

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VALUE DIFFERENCES

Some value difference between attributes were ignored ex: MODIFICATIONDATE

The path to the record is detailed

The critical differences are detected

ANALYSIS / VALIDATION

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data models

extraction functional criterias

data sets

data sets before tests

selection

test execution

extraction

anonymisation / data generation

analysis / validation

data sets after tests execution traces

test coverage

TEST SETSthe general method

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PROGRAMS ARE AUTOMATICALLY INSTRUMENTED

COVERAGE

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RESULTS FROM THE ANALYSIS OF A PROGRAM TRACE FILE

INCLUDING 5.424 ARCS

Arc numberNumber of processes

Procedure name

6 032 15 499 704 SR1CH1S11

6 033 15 499 704 SR1CH1S11 end

… …  …

6 018 3 219 761 SR8

6 017 3 219 761 SR8 end

… … …

6 016 278 110 SR5

6 017 278 110 SR5 end

… … …

6 186 0 OPTI-EMPI

6 187 0 OPTI-EMPI end

The most used arcs

The less used arcs

COVERAGE