Abstract
Chaining can reduce the number of iterations required for symbolic state-space generation and model-checking, especially in Petri nets and similar asynchronous systems, but requires considerable insight and is limited to a static ordering of the events in the high-level model. We introduce a two-step approach that is instead fine-grained and dynamically applied to the decision diagrams nodes. The first step, based on a precedence relation, is guaranteed to improve convergence, while the second one, based on a notion of node fullness, is heuristic. We apply our approach to traditional breadth-first and saturation state-space generation, and show that it is effective in both cases.
Work supported in part by the National Science Foundation under grants CNS-0501747 and CNS-0501748.
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Chung, MY., Ciardo, G., Yu, A.J. (2006). A Fine-Grained Fullness-Guided Chaining Heuristic for Symbolic Reachability Analysis. In: Graf, S., Zhang, W. (eds) Automated Technology for Verification and Analysis. ATVA 2006. Lecture Notes in Computer Science, vol 4218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11901914_7
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