Slicing Feature Models  Mathieu Acher1, Philippe Collet1 , Philippe Lahire1 and Robert France21 University                ...
Feature Models                       defacto standard for modeling variability                           more than 1000 ci...
Feature Modelssemantics: control legal combination of features (aka configurations)                                        ...
Feature Modelssupport: automated reasoning (e.g., configurators) Benavides et al. 2010         languages and tools e.g., Fe...
Feature Models                 large, complex and multiple     Feature Model of Linux: more than 5000 features Berger et a...
Feature Models              large, complex and multiple                We need support for Separation of Concerns(1) abili...
Slicing Feature Models               We need support for Separation of Concerns                              (II) ability ...
Slicing Feature Modelsinput: slicing criterion (arbitrary set of features, relevant for a feature model user)output: a new...
ASE11 short paper     Semantics                                                                                           ...
See you!                                                         Slicing Feature Models                                   ...
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ASE'11 (short paper)

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Feature models (FMs) are a popular formalism for describing the commonality and variability of software product lines (SPLs) in terms of features. As SPL development increasingly involves numerous large FMs, scalable modular techniques are required to manage their complexity. In this paper, we present a novel slicing technique that produces a projection of an FM, including constraints. The slicing allows SPL practitioners to find semantically meaningful decompositions of FMs and has been integrated into the FAMILIAR language.

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ASE'11 (short paper)

  1. 1. Slicing Feature Models Mathieu Acher1, Philippe Collet1 , Philippe Lahire1 and Robert France21 University 2 Colorado State University, USA of Nice Sophia Antipolis, CNRS, France {acher,collet,lahire}@i3s.unice.fr Computer Science Department france@cs.colostate.edu Slicing Feature Models Semantics, Algorithm, Support, and Applications Mathieu Acher1, Philippe Collet1 , Philippe Lahire1 and Robert France2 1 University 2 Colorado State University, USA of Nice Sophia Antipolis, CNRS, France {acher,collet,lahire}@i3s.unice.fr Computer Science Department france@cs.colostate.edu ASE11 short paper Semantics Algorithm Hierarchy Set of Support for Semantics-aware configurations Constraints Technique Root Support Or Mandatory Slicing Xor Optional Technique Future Work Motivation Reasoning about two kinds of variability Reconciling Updating and Paper Feature Models Extracting Views Large and Multiple, Inter- Support Complex FMs related FMs Algorithm Propositional Demonstration Long Short Logics Support for Constraints Corrective Capabilities Semantics-aware Automation Language Syntactical Technique Environment Root Support Technique Case Study BDD SAT Standalone Eclipse Editors Semantics Video Surveillance Processing Chains Medical Imaging Reverse Engineering Graphical Textual Workflows Software Architecture Editor Editor Hierarchy Set of configurations (Algorithm <-> Semantics) ^ (Algorithm <-> CorrectiveCapabilities) ^ (Algorithm <-> RootSupport) ^ (CorrectiveCapabilities -> SupportForConstraints) ^ (CorrectiveCapabilities -> SemanticsAware) ^ (SetOfConfigurations <-> SemanticsAware) ^ (SemanticsAware -> Automation) ^ (Language -> TextualEditor) ^ (TextualEditor -> Eclipse) ^ Language ASE11 demonstration Applications Support Technique Case Study Language Reasoning Automation Environment about two kinds Reconciling Updating of variability Video Feature and Surveillance Models Extracting Processing Views Textual Chains Medical Standalone Eclipse Reverse Engineering BDD SAT Editor Imaging Software Architecture Workflows
  2. 2. Feature Models defacto standard for modeling variability more than 1000 citations of Kang et al. 1990 per year Slicing Motivation Algorithm Propositional Large and Multiple, Inter- LogicsComplex FMs related FMs Support for Constraints Corrective Capabilities Semantics-aware Syntactical Technique Root Support Technique(CorrectiveCapabilities -> SupportForConstraints) ^(CorrectiveCapabilities -> SemanticsAware)^ (SetOfConfigurations <-> SemanticsAware) Semantics Or Mandatory Xor Optional Hierarchy Set of configurations
  3. 3. Feature Modelssemantics: control legal combination of features (aka configurations) Batory et al. 2005, Czarnecki et al. 2007, Schobbens et al. 2007 Slicing Motivation Algorithm Propositional Large and Multiple, Inter- Logics Complex FMs related FMs Support for Constraints Corrective Capabilities Semantics-aware Syntactical Technique Root Support Technique (CorrectiveCapabilities -> SupportForConstraints) ^ (CorrectiveCapabilities -> SemanticsAware) ^ (SetOfConfigurations <-> SemanticsAware) Semantics Or Mandatory Xor Optional Hierarchy Set of configurations
  4. 4. Feature Modelssupport: automated reasoning (e.g., configurators) Benavides et al. 2010 languages and tools e.g., FeatureIDE, SPLOT, TVL and FAMILIAR Slicing Motivation Algorithm Propositional Large and Multiple, Inter- Logics Complex FMs related FMs Support for Constraints Corrective Capabilities Semantics-aware Syntactical Technique Root Support Technique (CorrectiveCapabilities -> SupportForConstraints) ^ (CorrectiveCapabilities -> SemanticsAware) ^ (SetOfConfigurations <-> SemanticsAware) Semantics Or Mandatory Xor Optional Hierarchy Set of configurations
  5. 5. Feature Models large, complex and multiple Feature Model of Linux: more than 5000 features Berger et al. ASE’10, She et al. ICSE’10Feature models are governed by many complex constraints Hubaux et al. 2010, Benavides et al. 2010 Feature models are multiple (e.g., systems-of-systems, suppliers) Acher et al. 2011 (PhD thesis) Slicing Motivation Algorithm Propositional Large and Multiple, Inter- Logics Complex FMs related FMs Support for Constraints Corrective Capabilities Semantics-aware Syntactical Technique Root Support Technique (CorrectiveCapabilities -> SupportForConstraints) ^ (CorrectiveCapabilities -> SemanticsAware) ^ (SetOfConfigurations <-> SemanticsAware) Semantics Or Mandatory Xor Optional Hierarchy Set of configurations
  6. 6. Feature Models large, complex and multiple We need support for Separation of Concerns(1) ability to compose feature models (inserting, merging, aggregating) Acher et al. 2009 (II) ability to decompose feature models Slicing Motivation Algorithm Propositional Large and Multiple, Inter- Logics Complex FMs related FMs Support for Constraints Corrective Capabilities Semantics-aware Syntactical Technique Root Support Technique (CorrectiveCapabilities -> SupportForConstraints) ^ (CorrectiveCapabilities -> SemanticsAware) ^ (SetOfConfigurations <-> SemanticsAware) Semantics Or Mandatory Xor Optional Hierarchy Set of configurations
  7. 7. Slicing Feature Models We need support for Separation of Concerns (II) ability to decompose feature models Slicing Motivation Algorithm Propositional Large and Multiple, Inter- LogicsComplex FMs related FMs Support for Constraints Corrective Capabilities Semantics-aware Syntactical Technique Root Support Technique(CorrectiveCapabilities -> SupportForConstraints) ^(CorrectiveCapabilities -> SemanticsAware)^ (SetOfConfigurations <-> SemanticsAware) Semantics Or Mandatory Xor Optional Hierarchy Set of configurations
  8. 8. Slicing Feature Modelsinput: slicing criterion (arbitrary set of features, relevant for a feature model user)output: a new feature model (a slice), representing a projected set of configurations Slicing Motivation Algorithm Propositional Large and Multiple, Inter- Logics Complex FMs related FMs Support for Constraints Corrective Capabilities Semantics-aware Syntactical Technique Root Support Technique (CorrectiveCapabilities -> SupportForConstraints) ^ (CorrectiveCapabilities -> SemanticsAware) ^ (SetOfConfigurations <-> SemanticsAware) Semantics Or Mandatory Xor Optional Hierarchy Set of configurations
  9. 9. ASE11 short paper Semantics AlgorithmHierarchy Set of Support for Semantics-aware configurations Constraints Technique Root Support Slicing Algorithm Motivation Propositional Logics Support for Constraints Corrective Capabilities Semantics-aware Large and Multiple, Inter- Syntactical Technique Complex FMs related FMs Root Support Technique (Algorithm <-> Semantics) ^ (Algorithm <-> CorrectiveCapabilities) ^ (Algorithm <-> RootSupport) ^ (CorrectiveCapabilities -> SupportForConstraints) ^ (CorrectiveCapabilities -> SemanticsAware) Semantics ^ (SetOfConfigurations <-> SemanticsAware) Or Mandatory Hierarchy Set of configurations Xor Optional
  10. 10. See you! Slicing Feature Models Semantics, Algorithm, Support, and Applications Mathieu Acher1, Philippe Collet1 , Philippe Lahire1 and Robert France2 1 University 2 Colorado of Nice Sophia Antipolis, CNRS, France State University, USA {acher,collet,lahire}@i3s.unice.fr Computer Science Department france@cs.colostate.edu ASE11 short paper Semantics Algorithm Hierarchy Set of Support for Semantics-aware configurations Constraints Technique Root Support Or Mandatory Slicing Xor Optional Technique Future Work Motivation Reasoning about two kinds of variability Reconciling Updating and Paper Feature Models Extracting Views Large and Multiple, Inter- Support Complex FMs related FMs Algorithm Propositional Demonstration Long Short Logics Support for Constraints Corrective Capabilities Semantics-aware Automation Language Syntactical Technique Environment Root Support Technique Case Study BDD SAT Standalone Eclipse Editors SemanticsVideo SurveillanceProcessing Chains Medical Imaging Reverse Engineering Graphical Textual Workflows Software Architecture Editor Editor Hierarchy Set of configurations (Algorithm <-> Semantics) ^ (Algorithm <-> CorrectiveCapabilities) ^ (Algorithm <-> RootSupport) ^ (CorrectiveCapabilities -> SupportForConstraints) ^ (CorrectiveCapabilities -> SemanticsAware) ^ (SetOfConfigurations <-> SemanticsAware) ^ (SemanticsAware -> Automation) ^ (Language -> TextualEditor) ^ (TextualEditor -> Eclipse) ^ Language ASE11 demonstration Applications Support Technique Case Study Language Reasoning Automation Environment about two kinds Reconciling Updating of variability Video Feature and Surveillance Models Extracting Processing Views Textual Chains Medical Standalone Eclipse Reverse Engineering BDD SAT Editor Imaging Software Architecture Workflows
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