WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)

WebFML

Synthesizing Feature Models Everywhere

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

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h:p://>nyurl.com/WebFMLDemo

There are numerous artefacts that exhibit features and their dependencies.

Product Line

Modeling variability

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φ

Feature Models defacto standard for modeling variability

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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

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Ontological Seman@cs Hierarchy and feature groups of an FM. Define the seman@cs of the features’ rela@onships.

Valid configura@on: {Wiki, Storage, License, Hos@ng, P.SQL, P.License, H.Service , Domain}

Hierarchy + Variability =

set of valid configura@ons

Feature Models defacto standard for modeling variability

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]

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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]

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Importance of ontological seman@cs (1)

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Communica>on Comprehension Forward engineering (e.g., genera>on)

10 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


Most of the exis@ng approaches neglect either configura@on or ontological seman@cs.

We want both!

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φ

Fundamental Problem Selec@ng a Spanning Tree of the Binary Implica@on Graph (BIG)

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#0 Op@mum branching (Tarjan) weigh@ng edges


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#1 Ranking lists best parent candidates for each feature


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#2 Clusters ~possible siblings


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#3 Cliques ~bi-‐implica@ons


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#4 small BIG


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#4 reduced BIG incomplete but drama@cally reduce the problem


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 Milne, D.N., Wigen, I.H.: An open-‐source toolkit for mining wikipedia. Ar@f. Intell. 194, 222{239 (2013)

40 GB!

WebFML

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•  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

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Support and Empirical Study (1) Goal: evidence and empirical insights of what heuris@cs are effec@ve and what support is needed in WebFML

•  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

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Support and Empirical Study (2) Default heuris@cs/support has been determined through an empirical study

•  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

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Support and Empirical Study (3) Default heuris@cs/support has been determined through an empirical study

hgp://@nyurl.com/OntoFMExperiments

24 hgp://hal.inria.fr/hal-‐00874867

Support and Empirical Study (3)

WebFML

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WebFML

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WebFML

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WebFML

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WebFML

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WebFML

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•  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

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Conclusion

FraSCAti

SCAParser

Java Compiler

JDK6 JDT

Optional

Mandatory

Alternative-Group

Or-Group

Assembly Factory

resthttp

Binding

MMFrascati

Component Factory

Metamodel

MMTuscany

constraints

rest requires MMFrascatihttp requires MMTuscany

FM1

Feature Model

Ontologies Knowledge

User

Heuris@cs

WebFML: Synthesizing Feature Models

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Need a DEMO!

φ


WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)

Engineering

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