Year: 2026
Type: preprint
Abstract: Given the safety-critical nature of many embedded systems, their safety assurance is essential. Because such systems are typically stochastic, probabilistic model checking is a particularly important technique. However, there is a well-known scalability issue due to state-space explosion, especially when verifying complex properties. To mitigate this issue, we propose specification-guided path shortcutting for probabilistic systems, focusing on Markov chains (MCs) and $ω$-regular properties. The key idea is that, when the verified property is fixed, certain sequences of transitions in an MC can be replaced with a single transition without changing the satisfaction probability, and thus, we can reduce the state space of the MC. We implement the proposed path shortcutting and evaluate its contribution to the performance of probabilistic model checking, using Storm as the baseline model checker. The results suggest that our approach often outperforms the baseline, particularly on benchmark instances with complex specifications.
Source: arXiv (Cornell University)
Institutions: National Institute of Informatics, Kyoto University
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: AIを含むソフトウェアのための形式検証手法, G8008078829
OpenAlex ID: W7207601465
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