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 |
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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. |
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| 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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| 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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