RT Conference Proceedings
T1 Elimination of constraints for parallel analysis of feature models.
A1 Horcas-Aguilera, José Miguel
A1 Ballesteros-Gómez, Joaquín
A1 Pinto-Alarcón, Mónica
A1 Fuentes-Fernández, Lidia
K1 Ingeniería del software
K1 Programación en paralelo (Informática)
AB Cross-tree constraints give feature models maximal expressive power since any interdependency between features can be captured through arbitrary propositional logic formulas. However, the existence of these constraints increases the complexity of reasoning about feature models, both for using SAT solvers or compiling the model to a binary decision diagram for efficient analyses.Although some works have tried to refactor constraints to eliminate them, they deal only with simple constraints (i.e., requires and excludes) or require the introduction of an additional set of features, increasing the complexity of the resulting feature model.This paper presents an approach that eliminates all the cross-tree constraints present in regular boolean feature models, including arbitrary constraints, in propositional logic formulas.Our approach for removing constraints consists of splitting the semantics of feature models into orthogonal disjoint feature subtrees, which are then analyzed in parallel to alleviate the exponential blow-up in memory of the resulting feature tree.
YR 2023
FD 2023
LK https://hdl.handle.net/10630/27433
UL https://hdl.handle.net/10630/27433
LA spa
NO Work supported by the projects IRIS PID2021-122812OB-I00 (cofinanced by FEDER funds), LEIA UMA18-FEDERJA-157, and DAEMON H2020-101017109; and by Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026