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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Bibliographic Details
Published in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] pp. 1332 - 1344
Main Authors: Sundermann, Chico, Loth, Jacob, Thum, Thomas
Format: Conference Proceeding
Language:English
Published: ACM 27.10.2024
Subjects:
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
ISSN:2643-1572
Online Access:Get full text
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Summary: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.
ISSN:2643-1572
DOI:10.1145/3691620.3695594