Reverse Engineering Feature Models from Software Configurations

1. Reverse Engineering Feature Models
from Software Configurations
R. AL-msie’deen, M. Huchard,
A.-D. Seriai, C. Urtado, S. Vauttier
LIRMM, CNRS et Université de Montpellier
LGI2P, Ecole des Mines d’Alès
France
Oct. 7-10, 2014
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2. 1 Context
2 FCA and feature model
3 Algorithm
4 Case study
5 Conclusion
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3. Context
1 Context
2 FCA and feature model
3 Algorithm
4 Case study
5 Conclusion
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4. Context
Product Line Engineering
Feature model
Assets Variable Architecture
Derived Products
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5. Context
Building a product
Feature selection
Selected Implemented
Assets Architecture
Product
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6. Context
Product Line Reverse Engineering
Similar Products
developed in undisciplined manner
Which Feature model?
Which Assets?
Which Architecture?
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7. Context
Software Product Line
Feature model
Assets
Variable architecture
Software systems
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8. Context
Software Product Line Reverse Engineering (REVPLINE)
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9. Context
Focus: building the feature model
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10. Context
Feature model
Classical FODA model: A tree
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11. Context
Existing approaches
Acher et al., semi-automatic approach
Lopez-Herrejon et al., genetic algorithm
She et al., heuristics based on textual description and given feature
dependencies
Ziadi et al., ad hoc algorithm, simpl. intents of Attribute Concepts
Loesch et al., FCA for understanding variability
Yang et al., FCA, pruning and merging similar concepts in the concept
lattice, uses concept specialization for subfeature relationships
Ryssel et al., FCA (AC-poset), computes FM and complex
implications, uses specialization between concepts for sub feature
relationships
! Find automatically a simple FM model, with cross-tree constraints, no
hypothetic subfeature relationships
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12. FCA and feature model
1 Context
2 FCA and feature model
3 Algorithm
4 Case study
5 Conclusion
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13. FCA and feature model
Using existing similar software products
Cell_Phone
Wireless
Infrared
Bluetooth
Accu_Cell
Strong
Medium
Weak
Display
Games
Multi_Player
Single_Player
Artificial_Opponent
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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14. FCA and feature model
What reveals FCA: Mandatory features
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15. FCA and feature model
Features always appearing together
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16. FCA and feature model
Implications, with different possible semantics
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17. FCA and feature model
Mutually exclusive, with different possible semantics
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18. FCA and feature model
Mutually exclusive, with different possible semantics
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19. FCA and feature model
From FCA information to the feature model
Used similar software systems are only examples of possible products
and may contain flaws
Only hypotheses can be found in the concept lattice or AOC-poset
For each extracted knowledge, one has to choose wether:
it will be in the tree or
it will be encoded into cross-tree constraints
Choose the form of the tree (which kind of nodes, how many levels)
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20. Algorithm
1 Context
2 FCA and feature model
3 Algorithm
4 Case study
5 Conclusion
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21. Algorithm
Extracting base features
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22. Algorithm
Extracting AND feature nodes
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23. Algorithm
Extracting XOR feature node
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24. Algorithm
Extracting OR feature node
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25. Algorithm
Extracting require cross-tree constraints
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26. Algorithm
Extracting exclude cross-tree constraints
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27. Algorithm
The resulting feature model
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28. Case study
1 Context
2 FCA and feature model
3 Algorithm
4 Case study
5 Conclusion
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29. Case study
Case study
Derived products from existing SPL
Existing feature models for expert comparison
Correctness (precision / recall) is evaluated by comparing products
generated by the computed FM and initial products
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30. Case study
Results
Group of Features CTCs Evaluation Metrics
# case study
Number of Products
Number of Features
Base
Atomic Set of Features
Inclusive-or
Exclusive-or
Requires
Excludes
Execution times (in ms)
Precision
Recall
F-Measure
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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31. Conclusion
1 Context
2 FCA and feature model
3 Algorithm
4 Case study
5 Conclusion
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32. Conclusion
Conclusion
A method which computes a simple FM model from product
configurations
Admits all initial product configurations
Focused on logical organization
With cross-tree constraints
No hypothetic sub-feature relationships
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33. Conclusion
Perspectives
Use information from source code and FCA for extracting sub-features
(tree structure)
Improve XOR computation (compute several)
Compute covering sets of features
Define several alternative orders for computing the tree and evaluate
them on the case study
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34. Conclusion
Thank you!
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