Managing feature models
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Managing Feature Models. Mathieu Acher. Learning Feature Models with (a.k.a implementing the introductory example). ( FeAture Model scrIpt Language for manIpulation and Automatic Reasoning ) . http:// familiar-project.github.com /. φ. TVL DIMACS.

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Managing feature models

Managing Feature Models

Mathieu Acher


Managing feature models

Learning

Feature Models with

(a.k.a implementing the introductory example)


Managing feature models

(FeAture Model scrIptLanguage for manIpulation and AutomaticReasoning)

http://familiar-project.github.com/

φ

TVL

DIMACS

Mathieu Acher, Philippe Collet, Philippe Lahire, Robert B. France « A Domain-SpecificLanguage for Large-Scale Management of FeatureModels » Science of Computer Programming (SCP), 2013


Managing feature models

{CarEquipment, Comfort, DrivingAndSafety, Healthing}

{AirConditioning, FrontFogLights}

{AutomaticHeadLights, AirConditioning, FrontFogLights}

{AutomaticHeadLights, FrontFogLights, AirConditioningFrontAndRear}

{AirConditioningFrontAndRear}

{AirConditioning}

{AirConditioningFrontAndRear, FrontFogLights}

X


Managing feature models

Much more than that!

Let us have a deeper look


Managing feature models

  • (FeAture Model scrIptLanguage for manIpulation and AutomaticReasoning)

http://familiar-project.github.com/

φ

TVL

DIMACS

importing, exporting, composing, decomposing, editing, configuring, reverse engineering, computing"diffs", refactoring, testing,

and reasoning about (multiple) variabilitymodels

Mathieu Acher, Philippe Collet, Philippe Lahire, Robert B. France « A Domain-SpecificLanguage for Large-Scale Management of FeatureModels » Science of Computer Programming (SCP), 2013


1 automated analysis

#1 Automated Analysis


2 multiple feature models

#2 Multiple Feature Models


Managing feature models

Multi-* variability

*systems, perspectives, or stakeholders

12


Two k ey r equirements

Two Key Requirements

  • #1 Automatedanalysis

    • Akasupporttobetterunderstandandplaywithyourfeaturemodel (TVL model)

  • #2 Managing multiple feature models

    • Composing / Decomposing / Diff and Reasoning about their relationships

    • Combining these operators


Language and environment

languageandenvironment

Language facilities

Environment

Interoperability

// foo.fml

fm1 = FM (“foo1.tvl”)

fm2 = FM (“foo2.m”)

fm3 = merge intersection { fm1 fm2 }

c3 = counting fm3

renameFeature fm3.TV as “OutputTV”

fm5 = aggregate { fm3 FM (“foo4.xml”) }

assert (isValidfm5)

fm6 = slice fm5 including fm5.TV.*

export fm6

True/False

8759

“OutputTV”, “TV”


Managing feature models

features

configuration

select

deselect

asFM

deads

configs

counting

Interoperability

cores

Reasoning

isValid

falseOptionals

compare

cleanup

insert

merge

diff

intersection

sunion

aggregate

map

unmap

extract slicing

De/Composition

fm1 = FM(“foo.tvl”)

fm2 = FM (“foo.m”)

serialize fm4 into SPLOT

serialize fm1 into featureide

fm3 = FM (“foo.xmi”)

fm4 = FM (A : B ….)

setOptional

setMandatory

renameFeature

removeFeature

accessors

copy

Editing

setAlternatives

setOr

modular mechanisms

restricted set of types

Language Facilities

fm1.*

fm1.B

iterator/conditional

assertion


Hello world

helloworld.fml

Hello World


Typed language

basics2.fml

Typed language

  • Domain-specific types

    • Feature Model,

    • Configuration,

    • Feature,

    • Constraint

  • Other types include

    • Set

    • String

    • Boolean,

    • Enum,

    • Integer and Real.

  • A set of operations, called operators, are defined for a given type.


Typed language1

basics2.fml

Typed language


Typed language2

basics2.fml

Typed language


Importing exporting feature models

basics3.fml

Importing/Exporting feature models

Internal notation or by “filename extensions”


Feature accessors 1

ftAccessors.fml

Feature Accessors (1)


Other constructs

ftAccessors2.fml

Other constructs


Configuration

conf.fml

Configuration


Managing feature models

A ^

A  B ^

C => A ^

D => A

φ

FM


Operations for feature models 1

operatorsFM.fml

Operations for Feature Models (1)

φ


Operations for feature models 2

operatorsFM2.fml

Operations for Feature Models (2)

φ


Operations for feature models 3

operatorsFM3.fml

Operations for Feature Models (3)


Managing feature models

SoC support = Composition/Decomposition

for managing

large, complex and multiple

feature models

FORM 1998, Tun et al. 2009 (SPLC), Hartmann 2008 (SPLC), Lee et al. 2010, Czarnecki 2005, Reiser et al. 2007 (RE journal), Hartmann et al. 2009 (SPLC), Thuem et al. 2009 (ICSE), Classen et al. 2009 (SPLC), Mendonca et al. 2010 (SCP), Dunghanaet al. 2010, Hubauxet al. 2011 (SoSyM), Zaid et al. 2010 (ER), She et al., 2011 (ICSE), etc.


Composing feature models 1

aggregateBasics.fml

Composing Feature Models (1)


Composing feature models 2

aggregate1.fml

Composing Feature Models (2)

Previous version


Composing feature models 3

mergeMI.fml

Composing Feature Models (3)

Mathieu Acher, Philippe Collet, Philippe Lahire, Robert B. France « ComparingApproaches for ImplementingFeature Model Composition » ECMFA’10


Comparing feature models

compare.fml

Comparing Feature Models

see also Thuem, Kastner and Batory, ICSE’09


Managing feature models

Combining operators:

an example


Merge intersection available suppliers

Merge Intersection: Available Suppliers

Suppliers?

Products?

A customer has some requirements


In familiar

suppliersExample0.fml

In FAMILIAR


Merge union availability checking

Merge Union: Availability Checking

Yes!

Can suppliers provide all products?

“compare”


In familiar1

suppliersExample.fml

In FAMILIAR


Merging operation implementation issues

Merging operation: implementation issues

How to synthesise a feature model that represents the union of input sets of configurations?


Managing feature models

Merging operation: semantic issues (2)

φ

Union

Intersection

Diff

How to synthesise a feature model that represents the union of input sets of configurations?


Merging operation algorithm

Merging operation: algorithm

φ

φ

φ

φ

1

2

3

123

How to synthesise a feature model that represents the union of input sets of configurations?

+

merged hierarchy

merged propositional formula

Set mandatory features

Detect Xor and Or-groups

Compute “implies/excludes” constraints

see also [Czarnecki SPLC’07 or SPLC’12]


Merging operation back to hierarchy

mergeNonPC.fml

Merging operation: back to hierarchy

?

> configsfm4

res12: (SET) {{C;A};{A;B};{A};{A;B;C}}

Mathieu Acher, Benoit Combemale, Philippe Collet, Olivier Barais, Philippe Lahire, Robert B. France « Composingyour Compositions of VariabilityModels » MODELS’13


Managing feature models

Related Works

  • Well-defined semantics

  • Guarantee semantics properties by construction

  • More compact feature models than reference-based techniques [Schobbens et al., 2007], [Hartmann et al., 2007]

    • Easier to understand

    • Easier to analyze (e.g., compare with another)

  • Applicable to any propositional feature models

    • Full support of propositional constraints

    • Different hierarchies [Van Den Broek et al., SPLC’2010/2012]

  • Syntactical strategies fail [Alves et al., 2006], [Segura et al., 2007]

see also [Acher et al., ECMFA’10 / MODELS’13]


Managing feature models

Another application of composing feature models

(purpose: automated synthesis of feature models)


Managing feature models

Problem: multiple „carmodels“


Managing feature models

Problem: multiple „carmodels“


Managing feature models

Problem: multiple „carmodels“


Managing feature models

Problem: multiple „carmodels“

#1 – top-down: specify constraints (e.g., excludes) of all model lines upfront

Two modeling approaches

#2 – bottom-up: elaborate a feature model for each model line and merge them


Managing feature models

#1 top-down


Managing feature models

#1 bottom-up

FM_1

merge

FM_r

FM_2

FM_3


Managing feature models

#1 bottom-up (FAMILIAR)

audiMerge.fml

FM_1

merge

FM_r

FM_2

FM_3


Managing feature models

Decomposition support

(and its combination with other operators)


Managing feature models

Building “views” of a feature model


Managing feature models

Building “views” of a feature model

  • Problem: given a feature model, how to decompose it into smaller feature models?

  • Semantics?

    • What’s the hierarchy

    • What’s the set of configurations?


A first try

A first try

Problem: You can select A3 without A5

Hierarchy and Configuration matter!


Slicing operator

Slicing Operator

slicing criterion: anarbitrary set of features, relevant for a feature model user

slice : a new feature model, representing a projectedset of configurations


S licing operator going into details projected set of configurations

Slicing operator: going into detailsprojected set of configurations

fm1 = {

{A,B,C,D,E,P,R,T,U,W},

{A,B,C,F,P,S,T,U,W},

{A,B,C,D,E,P,R,T,W},

{A,B,C,F,P,S,T,V,W},

{A,B,C,F,P,S,T,U,V,W},

{A,B,C,F,P,S,T,W},

{A,B,C,D,E,P,R,T,V,W},

}

fm1 = {

{A,B,C,D,E,P,R,T,U,W},

{A,B,C,F,P,S,T,U,W},

{A,B,C,D,E,P,R,T,W},

{A,B,C,F,P,S,T,V,W},

{A,B,C,F,P,S,T,U,V,W},

{A,B,C,F,P,S,T,W},

{A,B,C,D,E,P,R,T,V,W},

}

fm1p = {

{D,E,T},

{S,T},

{D,E,T},

{S,T},

{S,T},

{S,T},

{D,E,T}

}

fm1p = {

{D,E,T},

{S,T},

}


Managing feature models

Slicing operator: going into detailssynthesizing the corresponding feature model

φ

φ

1

s1

T

existential quantification of features not included in the slicing criterion

+

S

E

D

fm1p = {

{D,E,T},

{S,T}

}

Mathieu Acher, Philippe Collet, Philippe Lahire, Robert B. France « Separation of Concerns in FeatureModeling: Support and Applications » AOSD’12


Managing feature models

Slicing operator with FAMILIAR (1)

slicingOp2.fml


Managing feature models

slicingOp.fml

Slicing with FAMILIAR (2)


Managing feature models

From marketing, customers, product management

From existing software assets (technical variability)

Metzger, Heymans et al. “Disambiguating the Documentation of Variability in Software Product Lines: A Separation of Concerns, Formalization and Automated Analysis“ (RE’07)


Managing feature models

usefulness

From marketing, customers, product management

From existing software assets

realizability


Realizability checking

Realizability checking

aggregate

1

2

slice (“realizable part”)

φ

{{V1,V3,V2,VP1},

{V1,VP1},

{V3,VP1},

{VP1}}

compare

3

  • merge diff (“unrealizable products”)

4

Mathieu Acher, Philippe Collet, Philippe Lahire, Robert B. France « Separation of Concerns in FeatureModeling: Support and Applications » AOSD’12


With familiar

realizibility.fml

With FAMILIAR


Managing feature models

Advanced topics


Revisiting merge aggregate slice

Revisiting Merge: Aggregate + Slice


Revisiting aggregate merge and slice

mergeWithAggregateMI.fml

Revisiting Aggregate, Merge and Slice:

Mathieu Acher, Benoit Combemale, Philippe Collet, Olivier Barais, Philippe Lahire, Robert B. France « Composingyour Compositions of VariabilityModels » MODELS’13


Managing feature models

Mathieu Acher, Benoit Combemale, Philippe Collet, Olivier Barais, Philippe Lahire, Robert B. France « Composingyour Compositions of VariabilityModels » MODELS’13


Managing feature models

A ^

A  B ^

C => A ^

D => A

Feature Model SynthesisProblem

[Czarnecki et al., SPLC’07]

[She et al., ICSE’11]

[Andersen et al., SPLC’12]

φ

FM


Managing feature models

φ

« How to synthesise an

accurate (w.r.t. the set of constraints/configurations)

meaningful (maintainable by a user), and

unique

feature model? » 

http://familiar-project.github.com/


Managing feature models

φ(SAT solvers or

BinaryDecisionDiagrams)

The knowledgecanbe:

inconsistent(e.g., rootfeaturespecifiedis not possible)

consistent and incomplete (i.e., synthesisalgorithmneedsadditional information)

consistent, « partial » (e.g., not all the hierarchyisspecified) and actuallycomplete

Mathieu Acher, Patrick Heymans, Anthony Cleve, Jean-Luc Hainaut, Benoit Baudry « Support for Reverse Engineering and MaintainingFeatureModels » VaMoS’13


1 reverse engineering scenarios

#1 Reverse Engineering Scenarios

  • [Haslinger et al., WCRE’11], [Acher et al., VaMoS’12]

φ


2 refactoring

#2 Refactoring

  • [Alves et al., GPCE’06], [Thuem et al., ICSE’09]

φ


3 re engineering feature models of repository

#3 Re-Engineering FeatureModels of repository

  • For each FM weexecute the following FAMILIAR script…

  • … And we «compare» syntactically fm1 and fm2

    • semanticalcomparisonis not needed: we know thatthey are refactoring by construction (good test case though ;-))

  • Results:

    • 147 synthesisedFMs (69 %) wereexactly the same as input FMs ;

    • 40 synthesisedFMs (19%) were corrections of input FMs;

    • 24 synthesisedFMs (12%) weredifferent (knowledgeneeded)

      • anotherset of cross-treeconstraintswassynthesised.

      • featuregroup conflicts in six cases

        Specification of the hierarchyis the main issue

  • φ


    Managing feature models

    • #1 Breathingknowledgeintofeature model synthesis

    • formalspecification(consistency and completeness)

    • concretesyntax and toolingsuport

    • #2 Practical applications

    • reverse engineering, refactoring/re-engineering of featuremodels

    FAMILIAR

    φ

    « Giveme a formula

    and someknowledge,

    I willsynthesise

    an accurate,

    meaningful,

    unique

    feature model » 

    Automated support ishighlyneeded (ongoingwork)

    http://familiar-project.github.com/


    Managing feature models

    State-of-the-art support for assistingusers:

    http://tinyurl.com/OntoFMExperiments

    OntologicalHeuristics

    (interactive synthesis)

    Clusters of conceptually

    similarfeatures

    Rankinglist

    Logical clusters

    FAMILIAR console

    « Breathing Ontological Knowledge into Feature Model Management »

    Guillaume Bécan, Mathieu Acher, Benoit Baudry, Sana Ben Nasr


    Managing feature models

    • 300+ Products Comparison Matrices in Wikipedia

    « From Comparison Matrix to Variability Model: The Wikipedia Case Study »

    Nicolas Sannier, Mathieu Acher, and Benoit Baudry (ASE’2013)


    From products to feature models

    From Products to Feature Models


    Manual extraction of a feature model from product description s is not possible

    Manual extraction of a feature model from product description(s) is not possible

    • 640 configurations

    • (634 counter examples)

    • Exact set of configurations, each configuration corresponding to at least one product


    Automation

    Automation

    • Each product description is encoded as a feature model

    • Feature models {fm1, fm2,…,fm8} are merged

    fm1

    fm2

    fm3

    fm4

    fm5

    fm6

    fm7

    fm8


    Each product description is encoded as a feature model

    Each product description is encoded as a feature model


    Merging of feature models

    Merging of feature models

    φ

    φ

    φ

    φ

    2

    3

    1

    m

    +

    merged propositional formula

    merged hierarchy

    Set mandatory features

    Detect Xor and Or-groups

    Compute “implies/excludes”


    Feature models in the real

    Feature models in the real

    • SPLOT repository

      • more than 200 feature models reported from the literature (various domains)

    • Linux feature model

      • worst case: more than 6300 features!

      • eCos, FreeBSD, BusyBox, etc.

    • Automotive industry

      • thousands of features

    • Wiki matrix

      • wiki engines: ~ 2000 features

    • Reverse engineering procedures are emerging


    Managing feature models

    (ongoing) Comprehensive model-based product line support

    Reverse engineering

    Automated Analysis

    Languages, API/DSLs

    Evaluation (European projects, long-term collaboration with Thales, open source systems)

    83


    Managing feature models

    ?

    84


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