root F0 F1 F2 F3 A1 A2
Yes Yes Yes No Yes 3 0
Yes Yes No Yes Yes 2 2
Yes Yes Yes No No 2 0
Yes Yes No Yes No 0 8
Synthesis...
Notes
● Slides have been presented at SPLC'15
(Nashville, USA)
● Guillaume Bécan has made the vast
majority of the slides
...
Synthesis of Attributed Feature Models: Foundations 3
Product Lines/Highly configurable systems
config NR_CPUS
int "Maximu...
Synthesis of Attributed Feature Models: Foundations 4
● 2000+ options in about:config
● 3 types: boolean, integers, string...
Synthesis of Attributed Feature Models: Foundations 5
Product Lines
Product comparison matrices
Comparison of digital SLRs
Synthesis of Attributed Feature Models: Foundations 6
Modeling and Synthesis
● Boolean feature models (FMs) are nice but o...
Synthesis of Attributed Feature Models: Foundations 7
From Configuration Matrix to AFM
#2 synthesis procedure
#3 scalabili...
Synthesis of Attributed Feature Models: Foundations 8
Attributed Feature Model (AFM)
● AFM = attributed feature diagram + ...
Synthesis of Attributed Feature Models: Foundations 9
Formalizing the AFM Synthesis Problem
● Configuration matrix = inter...
Synthesis of Attributed Feature Models: Foundations 10
Formalizing the AFM Synthesis Problem
● Theoretical contributions (...
Synthesis of Attributed Feature Models: Foundations 11
Synthesis algorithm
● Input: Configuration Matrix + Domain Knowledg...
Synthesis of Attributed Feature Models: Foundations 12
Synthesis algorithm
● Extract feature, attributes and their domains...
Synthesis of Attributed Feature Models: Foundations 13
Synthesis algorithm
● Define hierarchy (feature tree + place of att...
Synthesis of Attributed Feature Models: Foundations 14
Synthesis algorithm
● Compute variability information (mandatory fe...
Synthesis of Attributed Feature Models: Foundations 15
Synthesis algorithm
● Compute readable constraints
Readable constra...
Synthesis of Attributed Feature Models: Foundations 16
User Effort
Domain knowledge =
● user input
● default heuristics
No...
Synthesis of Attributed Feature Models: Foundations 17
Scalability
Random dataset
● Generator of configuration matrices
– ...
Synthesis of Attributed Feature Models: Foundations 18
Scalability
Random dataset
● Execution time (no or-groups)
● Up to ...
Synthesis of Attributed Feature Models: Foundations 19
Scalability
Best Buy dataset
Execution time of 2.1s for the most ch...
Synthesis of Attributed Feature Models: Foundations 20
Conclusion
We introduce for the first time an algorithmic and
param...
Synthesis of Attributed Feature Models: Foundations 21
Future Work
● Empirical experiments provide evidence that the numbe...
Synthesis of Attributed Feature Models: Foundations 22
Future Work
● Take a set of constraints as input (rather
than a set...
Synthesis of Attributed Feature Models: Foundations 23
Questions?
root F0 F1 F2 F3 A1 A2
Yes Yes Yes No Yes 3 0
Yes Yes No...
Synthesis of Attributed Feature Models: Foundations 24
Product Lines
{
"name": "Luke Skywalker",
"height": "1.72 m",
"mass...
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Synthesis of Attributed Feature Models From Product Descriptions

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Many real-world product lines are only represented as non-hierarchical collections of distinct products, described by their configuration values. As the manual preparation of feature models is a tedious and labour-intensive activity, some techniques have been proposed to automatically generate boolean feature models from product descriptions. However , none of these techniques is capable of synthesizing feature attributes and relations among attributes, despite the huge relevance of attributes for documenting software product lines. In this paper, we introduce for the first time an algorithmic and parametrizable approach for computing a legal and appropriate hierarchy of features, including feature groups, typed feature attributes, domain values and relations among these attributes. We have performed an empirical evaluation by using both randomized configuration matrices and real-world examples. The initial results of our evaluation show that our approach can scale up to matrices containing 2,000 attributed features, and 200,000 distinct configurations in a couple of minutes.

Paper has been presented at SPLC'15 (Nashville, USA)

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Synthesis of Attributed Feature Models From Product Descriptions

  1. 1. root F0 F1 F2 F3 A1 A2 Yes Yes Yes No Yes 3 0 Yes Yes No Yes Yes 2 2 Yes Yes Yes No No 2 0 Yes Yes No Yes No 0 8 Synthesis root F1 F3 F2F0 A1 A2 A2 < 8 => A1 >= 2 A1 > 0 => A2 <= 2 Guillaume Bécan, Razieh Behjati, Arnaud Gotlieb, Mathieu Acher Synthesis of Attributed Feature Models From Product Descriptions
  2. 2. Notes ● Slides have been presented at SPLC'15 (Nashville, USA) ● Guillaume Bécan has made the vast majority of the slides ● Some slides were previously presented at FOSD'15 meeting ● Paper here: https://hal.inria.fr/hal-01178454
  3. 3. Synthesis of Attributed Feature Models: Foundations 3 Product Lines/Highly configurable systems config NR_CPUS int "Maximum number of CPUs" if SMP && !MAXSMP range 2 8 if SMP && X86_32 && !X86_BIGSMP range 2 512 if SMP && !MAXSMP && !CPUMASK_OFFSTACK range 2 8192 if SMP && !MAXSMP && CPUMASK_OFFSTACK && X86_64 default "1" if !SMP default "8192" if MAXSMP default "32" if SMP && X86_BIGSMP default "8" if SMP ---help--- This allows you to specify the maximum number of CPUs which this kernel will support. If CPUMASK_OFFSTACK is enabled, the maximum supported value is 4096, otherwise the maximum value is 512. The minimum value which makes sense is 2. This is purely to save memory - each supported CPU adds approximately eight kilobytes to the kernel image. Linux kernel
  4. 4. Synthesis of Attributed Feature Models: Foundations 4 ● 2000+ options in about:config ● 3 types: boolean, integers, string Firefox Product Lines/Highly configurable systems
  5. 5. Synthesis of Attributed Feature Models: Foundations 5 Product Lines Product comparison matrices Comparison of digital SLRs
  6. 6. Synthesis of Attributed Feature Models: Foundations 6 Modeling and Synthesis ● Boolean feature models (FMs) are nice but options are not only boolean. Languages/operations have been defined on top of attributed FMs; so where are attributed feature models? ● Numerous works address the synthesis of Boolean feature models, eg [Czarnecki et al. SPLC'07, Andersen et al. SPLC'12, Davril et al. FSE'13, Becan et al. ESE'15] but none of them consider attributes ● We introduce for the first time an algorithmic and parameterizable approach for synthesizing attributed FMs from product descriptions
  7. 7. Synthesis of Attributed Feature Models: Foundations 7 From Configuration Matrix to AFM #2 synthesis procedure #3 scalability evaluation #1 semantics
  8. 8. Synthesis of Attributed Feature Models: Foundations 8 Attributed Feature Model (AFM) ● AFM = attributed feature diagram + an arbitrary constraint ● Attributed feature diagram = – Hierarchy of features – Attributes: placed in features, they take a value in a domain – Domain = set of values, null value and a partial order – A set of human readable constraints
  9. 9. Synthesis of Attributed Feature Models: Foundations 9 Formalizing the AFM Synthesis Problem ● Configuration matrix = intermediate representation of a set of configurations ● Given a configuration matrix, synthesize an AFM which is – Maximal = the feature diagram contains as much as possible information – Sound and Complete = represent exactly the set of configurations
  10. 10. Synthesis of Attributed Feature Models: Foundations 10 Formalizing the AFM Synthesis Problem ● Theoretical contributions (more details in the paper) – Over-approximation of the attributed feature diagram – Several AFMs are possible for one configuration matrix root F0 F1 F2 F3 A1 A2 Yes Yes Yes No Yes 3 0 Yes Yes No Yes Yes 2 2 Yes Yes Yes No No 2 0 Yes Yes No Yes No 0 8 root F1 F3 F2F0 A1 A2 root F1 F3F2F0 A1 A2 excludes Synthesis
  11. 11. Synthesis of Attributed Feature Models: Foundations 11 Synthesis algorithm ● Input: Configuration Matrix + Domain Knowledge ( ) ● Output: AFM which is maximal, sound and complete = user input + default heuristics Tool name : FOReverSE
  12. 12. Synthesis of Attributed Feature Models: Foundations 12 Synthesis algorithm ● Extract feature, attributes and their domains Features ● Root ● F0 ● F1 ● F2 ● F3 Attributes ● A1 : {0,2,3}, 0 ● A2 : {0,2,8}, 0 root F0 F1 F2 F3 A1 A2 Yes Yes Yes No Yes 3 0 Yes Yes No Yes Yes 2 2 Yes Yes Yes No No 2 0 Yes Yes No Yes No 0 8 ● Compute binary implications root F0 F1 F2 F3 A1 A2 Yes Yes Yes No Yes 3 0 Yes Yes No Yes Yes 2 2 Yes Yes Yes No No 2 0 Yes Yes No Yes No 0 8 ... F2 => ¬ F1 ¬ F2 => F1 F2 => A1 ∈ {0,2} ¬ F2 => A1 ∈ {2,3} F2 => A2 ∈ {2,8} ¬ F2 => A2 ∈ {0} … A1 = 2 => A2 ∈ {0,2}
  13. 13. Synthesis of Attributed Feature Models: Foundations 13 Synthesis algorithm ● Define hierarchy (feature tree + place of attributes) Binary implications ... F2 => ¬ F1 ¬ F2 => F1 F2 => A1 ∈ {0,2} ¬ F2 => A1 ∈ {2,3} F2 => A2 ∈ {2,8} ¬ F2 => A2 ∈ {0} ... root F1 F3 F2F0 Binary implication graph (all possible hierarchies) root F1 F3 F2F0 Hierarchy Possible places for attributes ¬ f => ( a = null value of a ) A1 : root, F0 A2 : root, F0, F2 A1 A2
  14. 14. Synthesis of Attributed Feature Models: Foundations 14 Synthesis algorithm ● Compute variability information (mandatory features, features groups) – Only based on features – Reuse existing algorithms (She et al., Inform Software Tech, 2014) Xor: {F1, F2} root F1 F3 F2 F0 Mutex graph Solver Binary implications Possible feature groups (Mutex, Or, Xor) root F1 F3 F2F0 A1 A2 + + Binary implication graph Hierarchy+ Mandatory features
  15. 15. Synthesis of Attributed Feature Models: Foundations 15 Synthesis algorithm ● Compute readable constraints Readable constraints A1 > 0 => A2 <= 2 A2 < 8 => A1 >= 2 Interesting values for attributes A1 → 0 A2 → 8 Binary implications ... A1 = 0 => A2 ∈ {8} A1 = 2 => A2 ∈ {0,2} A1 = 3 => A2 ∈ {0} A2 = 0 => A1 ∈ {2,3} A2 = 2 => A1 ∈ {2} A2 = 8 => A1 ∈ {0} … Merge constraints Grammar of readable constraints
  16. 16. Synthesis of Attributed Feature Models: Foundations 16 User Effort Domain knowledge = ● user input ● default heuristics No user effort Arbitrary choices Fully automated algorithm More user effort => better AFM ● Feature or attribute? ● Interpretation of the cells (“Yes” = true) ● Hierarchy (F3 below F0) ● Overlapping feature groups ● Bounds in constraints (“0” is an interesting value for A1)
  17. 17. Synthesis of Attributed Feature Models: Foundations 17 Scalability Random dataset ● Generator of configuration matrices – Number of variables (features + attributes) – Number of configurations – Maximum domain size (number of distinct values in a column) ● Execution time of or-group computation ● 1000 configurations ● max domain size of 10 Timeout always reached with more than 60 variables Or groups do not scale ! = default heuristics only
  18. 18. Synthesis of Attributed Feature Models: Foundations 18 Scalability Random dataset ● Execution time (no or-groups) ● Up to 2,000 variables ● 1,000 configurations ● Max domain size of 10 ● 100 variables ● Up to 200,000 configurations ● Max domain size of 10 ● 10 variables ● 10,000 configurations ● Up to 6000 distinct values = default heuristics only On all experiments: Average: 2.6 min Max: 62 min
  19. 19. Synthesis of Attributed Feature Models: Foundations 19 Scalability Best Buy dataset Execution time of 2.1s for the most challenging matrix: ● 77 variables ● 185 configurations ● Maximum domain size of 185 Execution time is similar to the random dataset ● 242 matrices ● < 25% of empty cells ● Interpretation of empty cells = default heuristics only
  20. 20. Synthesis of Attributed Feature Models: Foundations 20 Conclusion We introduce for the first time an algorithmic and parameterizable approach for synthesizing attributed FMs from product descriptions Three key contributions: ● Semantics of attributed feature models/configuration matrix (over- approximation, equivalence) ● We designed and implemented a tool-supported synthesis algorithm ● We empirically evaluated the scalability of the synthesis algorithm on random and real-world matrices.
  21. 21. Synthesis of Attributed Feature Models: Foundations 21 Future Work ● Empirical experiments provide evidence that the number of constraints can be huge – Random dataset: 237 constraints in average, 8906 max – Best Buy dataset: 6821 constraints in average, 28300 max ● How to address the problem? – Minimization, prioritization, user-specified – Combination thereof ● Opencompare.org – More empirical studies on realistic matrices (beyond Best Buy)
  22. 22. Synthesis of Attributed Feature Models: Foundations 22 Future Work ● Take a set of constraints as input (rather than a set of configurations) – arbitrary propositional formula – require the use of SMT/CP solvers ● Develop other operations on attributed feature models: aggregate, merge, slice... – Instance of synthesis problem
  23. 23. Synthesis of Attributed Feature Models: Foundations 23 Questions? root F0 F1 F2 F3 A1 A2 Yes Yes Yes No Yes 3 0 Yes Yes No Yes Yes 2 2 Yes Yes Yes No No 2 0 Yes Yes No Yes No 0 8 Synthesis root F1 F3 F2F0 A1 A2 A2 < 8 => A1 >= 2 A1 > 0 => A2 <= 2
  24. 24. Synthesis of Attributed Feature Models: Foundations 24 Product Lines { "name": "Luke Skywalker", "height": "1.72 m", "mass": "77 Kg", "hair_color": "Blond", "skin_color": "Caucasian", "eye_color": "Blue", "birth_year": "19 BBY", "gender": "Male", "homeworld": "http://swapi.co/api/planets/1/", "films": [ "http://swapi.co/api/films/1/", "http://swapi.co/api/films/2/", "http://swapi.co/api/films/3/" ], ... } The Star Wars API (http://swapi.co/) { "name": "Darth Vader", "height": "202", "mass": "136", "hair_color": "none", "skin_color": "white", "eye_color": "yellow", "birth_year": "41.9BBY", "gender": "male", "homeworld": "http://swapi.co/api/planets/1/", "films": [ "http://swapi.co/api/films/6/", "http://swapi.co/api/films/3/", "http://swapi.co/api/films/2/", "http://swapi.co/api/films/1/" ], ... }

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