Reverse Engineering Feature Modelsfrom Software Configurations

R. AL-msie’deen, M. Huchard,A.-D. Seriai, C. Urtado, S. Vauttier

LIRMM, CNRS et Université de MontpellierLGI2P, Ecole des Mines d’Alès

France

Oct. 7-10, 2014

R. AL-msie’deen et al. (LIRMM-LGI2P) Feature Models Oct. 7-10, 2014 1 / 34

1 Context

2 FCA and feature model

3 Algorithm

4 Case study

5 Conclusion

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Context

1 Context

2 FCA and feature model

3 Algorithm

4 Case study

5 Conclusion

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Context

Product Line Engineering

Feature model

Assets Variable Architecture

Derived Products

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Context

Building a productFeature selection

Selected ImplementedAssets Architecture

Product

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Context

Product Line Reverse Engineering

Similar Productsdeveloped in undisciplined manner

Which Feature model?

Which Assets?Which Architecture?

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Context

Software Product Line

Feature model

Assets

Variable architecture

Software systems

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Context

Software Product Line Reverse Engineering (REVPLINE)

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Context

Focus: building the feature model

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Context

Feature model

Classical FODA model: A tree

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Context

Existing approaches

Acher et al., semi-automatic approachLopez-Herrejon et al., genetic algorithmShe et al., heuristics based on textual description and given featuredependenciesZiadi et al., ad hoc algorithm, simpl. intents of Attribute ConceptsLoesch et al., FCA for understanding variabilityYang et al., FCA, pruning and merging similar concepts in the conceptlattice, uses concept specialization for subfeature relationshipsRyssel et al., FCA (AC-poset), computes FM and compleximplications, uses specialization between concepts for sub featurerelationships

→ Find automatically a simple FM model, with cross-tree constraints, nohypothetic subfeature relationships

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FCA and feature model

1 Context

2 FCA and feature model

3 Algorithm

4 Case study

5 Conclusion

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FCA and feature model

Using existing similar software products

Cell_

Pho

ne

Wireless

Infrared

Bluetoo

th

Accu_

Cell

Stron

g

Medium

Weak

Display

Gam

es

Multi_Player

Single_

Player

Artificial_Opp

onent

P-1 × × × × × × × ×P-2 × × × × × × × ×P-3 × × × × × × × × ×P-4 × × × × × × × ×P-5 × × × × × × ×P-6 × × × × × × ×P-7 × × × × × × × × ×P-8 × × × × × × × × ×P-9 × × × × × × × × × ×P-10 × × × × × × ×P-11 × × × × × × × × ×P-12 × × × × × × × × ×P-13 × × × × × × × × × ×P-14 × × × × × × × × × ×P-15 × × × × × × × × × ×P-16 × × × × × × × × × × ×

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FCA and feature model

What reveals FCA: Mandatory features

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FCA and feature model

Features always appearing together

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FCA and feature model

Implications, with different possible semantics

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FCA and feature model

Mutually exclusive, with different possible semantics

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FCA and feature model

Mutually exclusive, with different possible semantics

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FCA and feature model

From FCA information to the feature model

Used similar software systems are only examples of possible productsand may contain flawsOnly hypotheses can be found in the concept lattice or AOC-posetFor each extracted knowledge, one has to choose wether:

it will be in the tree orit will be encoded into cross-tree constraints

Choose the form of the tree (which kind of nodes, how many levels)

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Algorithm

1 Context

2 FCA and feature model

3 Algorithm

4 Case study

5 Conclusion

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Algorithm

Extracting base features

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Algorithm

Extracting AND feature nodes

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Algorithm

Extracting XOR feature node

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Algorithm

Extracting OR feature node

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Algorithm

Extracting require cross-tree constraints

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Algorithm

Extracting exclude cross-tree constraints

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Algorithm

The resulting feature model

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Case study

1 Context

2 FCA and feature model

3 Algorithm

4 Case study

5 Conclusion

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Case study

Case study

Derived products from existing SPLExisting feature models for expert comparisonCorrectness (precision / recall) is evaluated by comparing productsgenerated by the computed FM and initial products

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Case study

Results

Group of Features CTCs Evaluation Metrics

# case study Num

berof

Produ

cts

Num

berof

Features

Base

Atomic

Set

ofFeatures

Inclusive-or

Exclusive-or

Requires

Excludes

Execution

times

(inms)

Precision

Recall

F-M

easure

1 ArgoUML-SPL 20 11 × × × 509 60% 100% 75%3 Graph product line 8 18 × × × × × 551 62% 100% 76%4 Berkeley DB 10 43 × × × × × × 661 50% 100% 66%2 Mobile media 8 18 × × × 441 68% 100% 80%3 Health complaint-SPL 10 16 × × × × 439 57% 100% 72%4 Video on demand 16 12 × × × × 572 66% 100% 80%5 Wiki engines 8 21 × × × × × × 555 54% 100% 70%7 Wikipedia 10 14 × × × × 552 72% 100% 84%5 Mobile phone 5 5 × × × 406 70% 100% 82%6 DC motor 10 15 × × 444 83% 100% 90%7 Cell phone-SPL 16 13 × × × × × × 486 51% 100% 68%

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Conclusion

1 Context

2 FCA and feature model

3 Algorithm

4 Case study

5 Conclusion

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Conclusion

Conclusion

A method which computes a simple FM model from productconfigurationsAdmits all initial product configurationsFocused on logical organizationWith cross-tree constraintsNo hypothetic sub-feature relationships

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Conclusion

Perspectives

Use information from source code and FCA for extracting sub-features(tree structure)Improve XOR computation (compute several)Compute covering sets of featuresDefine several alternative orders for computing the tree and evaluatethem on the case study

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Conclusion

Thank you!

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