Efficient Slicing of Feature Models via Projected d-DNNF Compilation

Configurable systems often contain components from different fields or disciplines that are relevant for distinct stakeholders. For instance, tests or analyses targeting interactions of the software of a cyber-physical system may be only applicable for software components. However, managing such com...

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Veröffentlicht in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] S. 1332 - 1344
Hauptverfasser: Sundermann, Chico, Loth, Jacob, Thum, Thomas
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 27.10.2024
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ISSN:2643-1572
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Abstract Configurable systems often contain components from different fields or disciplines that are relevant for distinct stakeholders. For instance, tests or analyses targeting interactions of the software of a cyber-physical system may be only applicable for software components. However, managing such components in isolation is not trivial due, for instance, interdependencies between features. Feature models are a common formalism to specify such dependencies. Feature-model slicing corresponds to creating a subset of the feature model (e.g., with only components relevant to a particular stakeholder) that still preserves transitive dependencies from discarded features. However, slicing is computationally expensive and subsequent analyses often depend on complex computations, such as SAT or #SAT. With knowledge compilation, the original feature model can be translated to a beneficial format (e.g., d-DNNF or BDD) with an initial effort that accelerates subsequent analyses. Consequentially, acquiring a sliced target format depends on two expensive subsequent algorithms. In this work, we merge both steps by proposing projected d-DNNF compilation; a novel way to slice feature models that coincidently performs knowledge compilation to d-DNNF. Our empirical evaluation on real-world feature models shows that our tool pd4 often reduces runtimes substantially compared to existing techniques and scales to more input instances.
AbstractList Configurable systems often contain components from different fields or disciplines that are relevant for distinct stakeholders. For instance, tests or analyses targeting interactions of the software of a cyber-physical system may be only applicable for software components. However, managing such components in isolation is not trivial due, for instance, interdependencies between features. Feature models are a common formalism to specify such dependencies. Feature-model slicing corresponds to creating a subset of the feature model (e.g., with only components relevant to a particular stakeholder) that still preserves transitive dependencies from discarded features. However, slicing is computationally expensive and subsequent analyses often depend on complex computations, such as SAT or #SAT. With knowledge compilation, the original feature model can be translated to a beneficial format (e.g., d-DNNF or BDD) with an initial effort that accelerates subsequent analyses. Consequentially, acquiring a sliced target format depends on two expensive subsequent algorithms. In this work, we merge both steps by proposing projected d-DNNF compilation; a novel way to slice feature models that coincidently performs knowledge compilation to d-DNNF. Our empirical evaluation on real-world feature models shows that our tool pd4 often reduces runtimes substantially compared to existing techniques and scales to more input instances.
Author Thum, Thomas
Sundermann, Chico
Loth, Jacob
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Snippet Configurable systems often contain components from different fields or disciplines that are relevant for distinct stakeholders. For instance, tests or analyses...
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SubjectTerms Analytical models
Computational modeling
configurable systems
Cyber-physical systems
d-DNNF
feature models
knowledge compilation
product lines
projection
Proposals
Prototypes
Runtime
Scalability
slicing
Software
Stakeholders
Title Efficient Slicing of Feature Models via Projected d-DNNF Compilation
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