A fine-grained fullness-guided chaining heuristic for symbolic reachability analysis

  • Authors:
  • Ming-Ying Chung;Gianfranco Ciardo;Andy Jinqing Yu

  • Affiliations:
  • Department of Computer Science and Engineering, University of California, Riverside;Department of Computer Science and Engineering, University of California, Riverside;Department of Computer Science and Engineering, University of California, Riverside

  • Venue:
  • ATVA'06 Proceedings of the 4th international conference on Automated Technology for Verification and Analysis
  • Year:
  • 2006

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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.