Configuring BDD Compilation Techniques for Feature Models

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Contributors

Abstract

The compilation of feature models into binary decision diagrams (BDDs) is a major challenge in the area of configurable systems analysis. Many large-scale feature models have been reported to exceed state-of-the-art compilation capabilities, e.g., for variants of the Linux kernel product line. However, experiments have been mainly conducted on standard settings of the BDD compilers themselves, not taking advanced configurations into account.
In this paper, we investigate the impact of various BDD compilation techniques for compiling feature models in conjunctive normal form. Specifically, we evaluate preprocessing techniques from satisfiability (SAT) solving, variable and clause ordering heuristics, non-incremental construction schemes, as well as parallelization of BDD construction. Our experiments on current feature models show that BDD compilation of feature models greatly benefits from these techniques, enabling to construct many previously not constructible large-scale feature models within seconds.

Details

Original languageEnglish
Title of host publicationSPLC '24: Proceedings of the 28th ACM International Systems and Software Product Line Conference
PublisherAssociation for Computing Machinery (ACM)
Pages209-216
Number of pages8
VolumeA
ISBN (electronic)9798400705939
Publication statusPublished - 2 Sept 2024
Peer-reviewedYes

External IDs

ORCID /0000-0002-0645-1078/work/165454298

Keywords