WebFML 
Synthesizing 
Feature 
Models 
Everywhere 
Guillaume 
Bécan, 
Sana 
Ben 
Nasr, 
Mathieu 
Acher 
and 
Benoit 
Baudr...
There are numerous artefacts that 
exhibit features and their dependencies. 
Product 
Line 
Modeling 
variability 
2 
φ
Feature 
Models 
defacto 
standard 
for 
modeling 
variability 
3 
Feature 
Model 
= 
Feature 
Diagram 
+ 
Proposi>onal 
F...
4 
Feature 
Models 
defacto 
standard 
for 
modeling 
variability 
Valid 
configura@on: 
{Wiki, 
Storage, 
License, 
Hos@n...
For 
a 
given 
configura@on 
set, 
many 
(maximal) 
feature 
diagrams 
with 
different 
ontological 
seman@cs 
[She 
et 
a...
For 
a 
given 
configura@on 
set, 
many 
(maximal) 
feature 
diagrams 
with 
different 
ontological 
seman@cs 
[She 
et 
a...
Importance 
of 
ontological 
seman@cs 
(1) 
7
Importance 
of 
ontological 
seman@cs 
(2) 
8
Importance 
of 
ontological 
seman@cs 
(3) 
9 
Communica>on 
Comprehension 
Forward 
engineering 
(e.g., 
genera>on)
10 
Importance 
of 
ontological 
seman@cs 
(4) 
Two 
product 
configurators 
generated 
from 
two 
FMs 
with 
the 
same 
c...
Most 
of 
the 
exis@ng 
approaches 
neglect 
either 
configura@on 
or 
ontological 
seman@cs. 
We 
want 
both! 
11 
φ
Fundamental 
Problem 
Selec@ng 
a 
Spanning 
Tree 
of 
the 
Binary 
Implica@on 
Graph 
(BIG) 
12
13 
Fundamental 
Problem 
Selec@ng 
a 
Spanning 
Tree 
of 
the 
Binary 
Implica@on 
Graph 
(BIG) 
#0 
Op@mum 
branching 
(...
14 
Fundamental 
Problem 
Selec@ng 
a 
Spanning 
Tree 
of 
the 
Binary 
Implica@on 
Graph 
(BIG) 
#1 
Ranking 
lists 
best...
15 
Fundamental 
Problem 
Selec@ng 
a 
Spanning 
Tree 
of 
the 
Binary 
Implica@on 
Graph 
(BIG) 
#2 
Clusters 
~possible ...
16 
Fundamental 
Problem 
Selec@ng 
a 
Spanning 
Tree 
of 
the 
Binary 
Implica@on 
Graph 
(BIG) 
#3 
Cliques 
~bi-­‐impli...
17 
Fundamental 
Problem 
Selec@ng 
a 
Spanning 
Tree 
of 
the 
Binary 
Implica@on 
Graph 
(BIG) 
#4 
small 
BIG
18 
Fundamental 
Problem 
Selec@ng 
a 
Spanning 
Tree 
of 
the 
Binary 
Implica@on 
Graph 
(BIG) 
#4 
reduced 
BIG 
incomp...
Ontological 
Heuris@cs 
• For 
op>mum 
branching, 
compu>ng 
ranking 
lists 
and 
clusters 
– ~ 
« 
closedness 
» 
of 
fea...
WebFML 
20
• Dataset 
– 120+ 
feature 
models 
of 
SPLOT 
– 30+ 
product 
comparison 
matrices 
from 
Wikipedia 
(see 
Becan 
et 
al....
• Dataset 
– 120+ 
feature 
models 
of 
SPLOT 
– 30+ 
product 
comparison 
matrices 
from 
Wikipedia 
(see 
Becan 
et 
al....
• One-­‐step 
synthesis 
is 
far 
from 
the 
ground 
truth 
– despite 
state-­‐of-­‐the-­‐art 
techniques 
we 
have 
devel...
Support 
and 
Empirical 
Study 
(3) 
hgp://@nyurl.com/OntoFMExperiments 
24 
hgp://hal.inria.fr/hal-­‐00874867
WebFML 
25
WebFML 
26
WebFML 
27
WebFML 
28
WebFML 
29
WebFML 
30
h:p://>nyurl.com/WebFMLDemo 
31
• WebFML 
for 
synthesizing 
feature 
models: 
– from 
various 
kinds 
of 
artefacts 
– sound 
and 
meaningful 
– interac>...
Need 
a 
DEMO! 
FraSCAti 
SCAParser 
Java Compiler 
JDK6 JDT 
Optional 
Mandatory 
Alternative- 
Group 
Or-Group 
Assembly...
Prochain SlideShare
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WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)

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Feature Models (FMs) are the de-facto standard for documenting, model checking, and reasoning about the configurations of a software system. This paper introduces WebFML a comprehensive environment for synthesizing FMs from various kinds of artefacts (e.g. propositional formula, dependency graph, FMs or product comparison matrices). A key feature of WebFML is an interactive support (through ranking lists, clusters, and logical heuristics) for choosing a sound and meaningful hierarchy. WebFML opens avenues for numerous practical applications (e.g., merging multiple product lines, slicing a configuration process, reverse engineering configurable systems).

tinyurl.com/WebFMLDemo

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WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)

  1. 1. WebFML Synthesizing Feature Models Everywhere Guillaume Bécan, Sana Ben Nasr, Mathieu Acher and Benoit Baudry 1 h:p://>nyurl.com/WebFMLDemo
  2. 2. There are numerous artefacts that exhibit features and their dependencies. Product Line Modeling variability 2 φ
  3. 3. Feature Models defacto standard for modeling variability 3 Feature Model = Feature Diagram + Proposi>onal Formula φ . Configura@on Seman@cs Set of valid configura@ons (set of selected features) of an FM. Ontological Seman@cs Hierarchy and feature groups of an FM. Define the seman@cs of the features’ rela@onships. Features parent feature 2 child features root
  4. 4. 4 Feature Models defacto standard for modeling variability Valid configura@on: {Wiki, Storage, License, Hos@ng, P.SQL, P.License, H.Service , Ontological Seman@cs Hierarchy and feature groups of an FM. Define the seman@cs of the features’ rela@onships. Domain} Hierarchy + Variability = set of valid configura@ons
  5. 5. For a given configura@on set, many (maximal) feature diagrams with different ontological seman@cs [She et al. ICSE’11, Andersen et al. SPLC’12, Acher et al. VaMoS’13] 5
  6. 6. For a given configura@on set, many (maximal) feature diagrams with different ontological seman@cs [She et al. ICSE’11, Andersen et al. SPLC’12, Acher et al. VaMoS’13] 6
  7. 7. Importance of ontological seman@cs (1) 7
  8. 8. Importance of ontological seman@cs (2) 8
  9. 9. Importance of ontological seman@cs (3) 9 Communica>on Comprehension Forward engineering (e.g., genera>on)
  10. 10. 10 Importance of ontological seman@cs (4) Two product configurators generated from two FMs with the same configura@on seman@cs but different ontological seman@cs. Good FM Good configura@on interface Bad FM Bad configura@on interface
  11. 11. Most of the exis@ng approaches neglect either configura@on or ontological seman@cs. We want both! 11 φ
  12. 12. Fundamental Problem Selec@ng a Spanning Tree of the Binary Implica@on Graph (BIG) 12
  13. 13. 13 Fundamental Problem Selec@ng a Spanning Tree of the Binary Implica@on Graph (BIG) #0 Op@mum branching (Tarjan) weigh@ng edges
  14. 14. 14 Fundamental Problem Selec@ng a Spanning Tree of the Binary Implica@on Graph (BIG) #1 Ranking lists best parent candidates for each feature
  15. 15. 15 Fundamental Problem Selec@ng a Spanning Tree of the Binary Implica@on Graph (BIG) #2 Clusters ~possible siblings
  16. 16. 16 Fundamental Problem Selec@ng a Spanning Tree of the Binary Implica@on Graph (BIG) #3 Cliques ~bi-­‐implica@ons
  17. 17. 17 Fundamental Problem Selec@ng a Spanning Tree of the Binary Implica@on Graph (BIG) #4 small BIG
  18. 18. 18 Fundamental Problem Selec@ng a Spanning Tree of the Binary Implica@on Graph (BIG) #4 reduced BIG incomplete but drama@cally reduce the problem
  19. 19. Ontological Heuris@cs • For op>mum branching, compu>ng ranking lists and clusters – ~ « closedness » of features based on their names • Syntac>cal heuris>cs – Smith-­‐Waterman (SW) and Levenshtein (L) • Wordnet – PathLength (PL) and Wu&Palmer (WP) • Wikipedia Miner offers an API to browse Wikipedia's ar>cles and compute their relatedness – Wik>onary (Wikt) 19 40 GB! Milne, D.N., Wigen, I.H.: An open-­‐source toolkit for mining wikipedia. Ar@f. Intell. 194, 222{239 (2013)
  20. 20. WebFML 20
  21. 21. • Dataset – 120+ feature models of SPLOT – 30+ product comparison matrices from Wikipedia (see Becan et al. ASE’14 and ASE’13) – Ground truths are known • Effec>veness of techniques (reduced BIG + ontological heuris>cs) – One shot synthesis – Quality of the ranking lists (top 2), clusters – Comparison with randomized and exis>ng techniques 21 Support and Empirical Study (1) Goal: evidence and empirical insights of what heuris@cs are effec@ve and what support is needed in WebFML
  22. 22. • Dataset – 120+ feature models of SPLOT – 30+ product comparison matrices from Wikipedia (see Becan et al. ASE’14 and ASE’13) – Ground truths are known • Effec>veness of techniques (reduced BIG + ontological heuris>cs) – One shot synthesis – Quality of the ranking lists (top 2), clusters – Comparison with randomized and exis>ng techniques 22 Support and Empirical Study (2) Default heuris@cs/support has been determined through an empirical study
  23. 23. • One-­‐step synthesis is far from the ground truth – despite state-­‐of-­‐the-­‐art techniques we have developed – interac>ve support is thus crucial • State-­‐of-­‐the-­‐art heuris>cs for ranking lists and clusters • Empirical insights on « cliques » and BIG reduc>on – e.g., support for unfolding of cliques 23 Support and Empirical Study (3) Default heuris@cs/support has been determined through an empirical study
  24. 24. Support and Empirical Study (3) hgp://@nyurl.com/OntoFMExperiments 24 hgp://hal.inria.fr/hal-­‐00874867
  25. 25. WebFML 25
  26. 26. WebFML 26
  27. 27. WebFML 27
  28. 28. WebFML 28
  29. 29. WebFML 29
  30. 30. WebFML 30
  31. 31. h:p://>nyurl.com/WebFMLDemo 31
  32. 32. • WebFML for synthesizing feature models: – from various kinds of artefacts – sound and meaningful – interac>ve support – full automa>on is neither feasible nor desirable • Tooling support is based on empirical studies • WebFML opens avenues for – reverse engineering variability-­‐intensive data/systems – refactoring/merging/slicing feature models 32 Conclusion
  33. 33. Need a DEMO! FraSCAti SCAParser Java Compiler JDK6 JDT Optional Mandatory Alternative- Group Or-Group Assembly Factory Binding http rest MMFrascati Component Factory Metamodel MMTuscany constraints rest requires MMFrascati http requires MMTuscany FM1 Feature Model Ontologies Knowledge User Heuris@cs WebFML: Synthesizing Feature Models 33 φ h:p://>nyurl.com/WebFMLDemo

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