Configuring BDD Compilation Techniques for Feature Models

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

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

OriginalspracheEnglisch
TitelSPLC 2024 - 28th ACM International Systems and Software Product Line Conference, Proceedings
Redakteure/-innenMaxime Cordy, Daniel Struber, Daniel Struber, Monica Pinto, Iris Groher, Deepak Dhungana, Jacob Kruger, Juliana Alves Pereira, Mathieu Acher, Thomas Thum, Thomas Thum, Maurice H. ter Beek, Jessie Galasso-Carbonnel, Paolo Arcaini, Mohammad Reza Mousavi, Xhevahire Ternava, Jose A. Galindo, Tao Yue, Lidia Fuentes, Jose Miguel Horcas
Herausgeber (Verlag)Association for Computing Machinery (ACM)
Seiten209-216
Seitenumfang8
BandA
ISBN (elektronisch)9798400705939
PublikationsstatusVeröffentlicht - 2 Sept. 2024
Peer-Review-StatusJa

Externe IDs

ORCID /0000-0002-0645-1078/work/165454298
Scopus 85203792615

Schlagworte

Schlagwörter

  • Binary Decision Diagrams, Knowledge Compilation, Feature Models, Configurable Systems