Abstract. This paper aims at making partial order reduction independent of the modeling language. Our starting point is the stubborn set algorithm of Valmari (see also Godefroid's thesis), which relies on necessary enabling sets. We generalize it to a guard-based algorithm, which can be implemented on top of an abstract model checking interface. We extend the generalized algorithm by introducing necessary disabling sets and adding a heuristics to improve state space reduction. The effect of the changes to the algorithm are measured using an implementation in the LTSmin model checking toolset. We compare our results to the Spin model checker, both on the benchmarks from the BEEM database, as well as on a number of Promela models. In many cases, the reduction obtained by our algorithm surpasses the ideal upper bound on the reduction obtained by the ample set method, as established empirically by Geldenhuys, Hansen and Valmari.
Abstract. This paper aims at making partial-order reduction independent of the modeling language. Our starting point is the stubborn set algorithm of Valmari (see also Godefroid's thesis), which relies on necessary enabling sets. We generalise it to a guard-based algorithm, which can be implemented on top of an abstract model checking interface.We extend the generalised algorithm by introducing necessary disabling sets and adding a heuristics to improve state space reduction. The effect of the changes to the algorithm are measured using an implementation in the LTSmin model checking toolset. We experiment with partial-order reduction on a number of Promela models, some with LTL properties, and on benchmarks from the BEEM database in the DVE language.We compare our results to the Spin model checker. While the reductions take longer, they are consistently better than Spin's ample set and even often surpass the ideal upper bound for the ample set, as established empirically by Geldenhuys, Hansen and Valmari on BEEM models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.