Download pdf - Composing your Compositions of Variability Models (Models 2013 presentation)

Mathieu Acher, Benoit Combemale, Philippe Collet, Olivier Barais, Philippe Lahire and Robert B. France

Composing your Composi2ons of Variability Models

2 2

Extensible architect

ures

(eg plugins-‐based)

Configura2on

files

System

Preferences

Configurators

Source code Build

systems

Comparison of *

Structural or behav

orial

models

External Variability Internal Variability Variability @ run.2me

3 3

*systems, viewpoints, concerns, stakeholders

MULTI-‐* VARIABILITIES

4 4 Variability Modeling with Feature Models

5 5

Composing Large, Complex & Mul2ple Feature Models

Scenario 1: Building Feature Models from product descrip2ons

6 Haslinger, et. Al. On extrac*ng feature models from sets of valid feature combina*ons. In FASE’13. Acher et. Al. On extrac*ng feature models from product descrip*ons. In: VaMoS’12

•  Choosing a product among a set of products

user



•  CreaNng a new product

domain analyst



•  Switching to a SoQware Product Line approach from a set of products

product manager



fm1 fm2

fm8 ……….

Scenario 2: Managing complexity in large scale organiza2ons

10 10

Integrated view

Departments


11 Reiser, M.O., Weber, M.: MulN-‐level feature trees: A pragmaNc approach to managing highly complex product families. Requir. Eng. 12(2) (2007)

Views & references between feature models

Figure from [Reiser2007]

When Feature Models have to be composed

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Solu2on

•  Iden2fica2on and implementa2on of 4 solu2ons for composing feature models

•  A reading grid to compose (i.e. choose) your composi2ons

Feature Models

Hierarchy: rooted tree Variability: •  mandatory, •  opNonal, •  Groups: exclusive or inclusive features •  Cross-‐tree constraints

F6

F2

S

F4F1

F5

Optional

Mandatory

Xor-Group

Or-Group

fm1

Feature Models

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F6

F2

S

F4F1

F5

fm1

set of valid configura2ons

configura2on seman2cs

Feature Models

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fm1

(proposi2onal)

φ

Reasoning

F6

F2

S

F4F1

F5

Around 30 reasoning opera*ons available

FM = Feature diagram + Proposi2onal formula Ψ [She et. Al, ICSE 2011]

Illustra2on: merging suppliers

17

F6

F2

S

F3F1

F5 F6

F2

S

F4F1

F5<<implies>>F6

F2

S

F4F1

F5

?

fm1 fm2 fm3

Inten2on: union Matching : ? Merging : ?

configura2on seman2cs

F6

F2

S

F3F1

F5

F4F3 <<implies>> F1

Ѱ = (F1 v F3 v ~F4 v ~F5 v ~F6) ^ (F1 v F3 v F4 v ~F5 v ~F6)

Logic based solu2on

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F6

F2

S

F3F1

F5 F6

F2

S

F4F1

F5<<implies>>F6

F2

S

F4F1

F5

φ1

φ2

φ3 O’ φ

c = ……… ………

Synthesis

M. Acher et. Al . Comparing Approaches for ImplemenNng Feature Model ComposiNon » ECMFA’10

[Andersen et. Al. Efficient Synthesis of Feature Models

SPLC’12]

Matching by name Merging by configura2on

Reference based solu2on

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F6

F2

S

F3F1

F5 F6

F2

S

F4F1

F5<<implies>>F6

F2

S

F4F1

F5

aka operaNonal

F6

F2

S

F4F1

F5 F6

F2

S

F3F1

F5 F6

F2

S

F4F1

F5<<implies>>

F6

F2

S

F3F1

F5

F4

R

InputFMs

fm1

FMr union

fm2 fm3

(F1 ⬄ (fm1.F1 v fm2.F1 v fm3.F1)) âtmost1 (fm1.F1, fm2.F1, fm3.F1) ^ (F6 ⬄ (fm1.F6 v fm2.F6 v fm3.F6)) âtmost1(fm1.F6, fm2.F6, fm3.F6) ^(F4 ⬄ (fm1.F4 v fm3.F4)) âtmost1(fm1.F4, fm3.F4) ^(S ⬄ (fm1.S v fm2.S v fm3.S)) âtmost1(fm1.S, fm2.S, fm3.S) ^(F2 ⬄ (fm1.F2 v fm2.F2 v fm3.F2)) âtmost1(fm1.F2, fm2.F2, fm3.F2) ^ (F5 ⬄ (fm1.F5 v fm2.F5 v fm3.F5)) ^ atmost1(fm1.F5, fm2.F5, fm3.F5) ^ (F3 ⬄ fm2.F3)

Ѱr union =

Matching by name Merging by reference View of interest

Rela2ons through constraints

Current Approaches

fm1 fm2

φc

o =

fmr

fmc

fm1 fm2o =

fmr

fmc

(reference based)

(with local synthesis) (with slicing)

fm1 fm2

φ1 φ2 φc

fmco

o' =

=

(logic based)

fm1 fm2

φc

o =

fmr

fmc

φc'

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RIGID TOO OPEN

Other candidates?

21

Building New Approaches

fm1 fm2

φc

o =

fmr

fmc

fm1 fm2o =

fmr

fmc

(reference based)

(with local synthesis) (with slicing)

fm1 fm2

φ1 φ2 φc

fmco

o' =

=

(logic based)

fm1 fm2

φc

o =

fmr

fmc

φc'

22

Local synthesis solu2on

23

F6

F2

S

F4F1

F5 F6

F2

S

F3F1

F5 F6

F2

S

F4F1

F5<<implies>>

F6

F2

S

F3F1

F5

F4

R

InputFMs

fm1

FMr union

fm2 fm3


Ѱr union =

Merging by reference + local synthesis over {S, F1 to F6}

Over Approxima2on (generaliza2on) with 2 « new »configura2ons

F6

F2

S

F3F1

F5

F4F3 <<implies>> F1

Ѱ = (F1 v F3 v ~F4 v ~F5 v ~F6) ^ (F1 v F3 v F4 v ~F5 v ~F6)Larger Ψ

Slicing solu2on

24

F6

F2

S

F4F1

F5 F6

F2

S

F3F1

F5 F6

F2

S

F4F1

F5<<implies>>

F6

F2

S

F3F1

F5

F4

R

InputFMs

fm1

FMr union

fm2 fm3


Ѱr union =

Merging by reference + slicing with {S, F1 to F6}

M. Acher et. Al . SeparaNon of concerns in feature modeling: support and applicaNons AOSD’12

Slice result = projected set of configura2ons onto the criterion

Same FM as the denota2onal version

F6

F2

S

F3F1

F5

F4F3 <<implies>> F1

Ѱ = (F1 v F3 v ~F4 v ~F5 v ~F6) ^ (F1 v F3 v F4 v ~F5 v ~F6)

How to use it?

25 hgp://familiar-‐project.github.com/

How to use it?

26

Composition of Feature Models

Predefined

unionintersectionreduced product diff

Other

Implementation- accuracy- reasoning- customizability - composability

Denotational Operational

extract slicing local synthesis

Semantics

Boolean Logics

Hybrid

1..4

1. Choose a seman2cs 2. Choose an implementa2on

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Feature Diagram Quality ?

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Denota2onal Opera2onal Local synthesis Slicing

Diagram Quality

Reasoning

Customizability

Traceability

Composability

•  Maximality •  Soundness •  Completeness

Reasoning over resul2ng configura2ons ?

29


Diagram Quality

Reasoning

Customizability

Traceability

Composability •  Performing assisted configuraNon •  AutomaNng analysis over FMs

Customizability?

30


Diagram Quality

Reasoning

Customizability

Traceability

Composability •  Specifying the meaning of the composiNon

Traceability?

31


Diagram Quality

Reasoning

Customizability

Traceability

Composability

•  Mapping to the input FMs

Composability? (union example)

32


Diagram Quality

Reasoning

Customizability

Traceability

Composability

•  AssociaNve, commutaNve

A reading Grid

33


Diagram Quality

Reasoning

Customizability

Traceability

Composability

performance


fm1 fm2

fm8 ……….

•  Reasoning •  High-‐quality Feature Diagram (for transformaNon)

denota2onal


35 35

Integrated view

Departments

•  Traceability •  Complex Mappings

opera2onal

Local synthesis Slicing

Any view

Ongoing and Future Work

•  User assistance in the merging process –  integraNon of ontological knowledge

•  Usage of composiNons in different contexts – and performance studies

•  Explore the relaNons from features to assets – mirorring composiNon both in the bindings and assets

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hhp://familiar-‐project.github.com/


Predefined


Other




Semantics

Boolean Logics

Hybrid

1..4

Seman2cs Matching Merging

blatant adver2sing

38

22-‐24 january 2014 Nice, France hgp://vamos2014.unice.fr

39


hhp://familiar-‐project.github.com/


Predefined


Other




Semantics

Boolean Logics

Hybrid

1..4? Seman2cs

Matching Merging