Specification-Guided Path Shortcutting for Efficient Probabilistic Model Checking

2026-09-02

AI Summary

为解决概率模型检测中的状态空间爆炸问题，提出了一种基于规范指导的路径缩短方法，特别针对马尔可夫链和ω-正则属性，以提高验证效率。

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.

Problem

• probabilistic model checking
• state-space explosion
• scalability issue

Innovation

• specification-guided
• path shortcutting
• probabilistic model checking
• Markov chains
• state-space reduction