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arXiv 2602.03565cs.LO
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使用向量区间进行符号模型检查

Symbolic Model Checking using Intervals of Vectors

Computer Science Department Faculty of Science University of Geneva(日内瓦大学理学院计算机科学系)

Damien Morard, Didier Buchs

AI总结:

本文提出了一种基于向量区间的新方法,用于高效进行Petri网的CTL模型检查,通过同态操作和标准形式提升性能。

摘要:

模型检查是一种强大的软件验证技术。然而,这种方法显著受到著名的状态空间爆炸问题的困扰。为解决这一问题,本文介绍了一种新的符号方法用于编码Petri网标记。该方法基于向量上的广义区间,而非现有基于区间向量的方法,如区间决策图。我们对这些区间进行了形式化,证明了它们在对Petri网进行CTL模型检查时具有同态操作,并定义了一个标准形式,以提供良好的性能特征。我们的结构促进了在全局模型检查领域中CTL公式的符号评估,旨在识别所有满足公式的状态。在模型检查竞赛(MCC 2022)示例上的测试表明,我们的方法取得了有希望的结果。为此,我们实现了基于其他符号模型检查技术中提取出的饱和和聚类原则的高效计算。

英文摘要:

Model checking is a powerful technique for software verification. However, the approach notably suffers from the infamous state space explosion problem. To tackle this, in this paper, we introduce a novel symbolic method for encoding Petri net markings. It is based on the use of generalised intervals on vectors, as opposed to existing methods based on vectors of intervals such as Interval Decision Diagrams. We develop a formalisation of these intervals, show that they possess homomorphic operations for model checking CTL on Petri nets, and define a canonical form that provides good performance characteristics. Our structure facilitates the symbolic evaluation of CTL formulas in the realm of global model checking, which aims to identify every state that satisfies a formula. Tests on examples of the model checking contest (MCC 2022) show that our approach yields promising results. To achieve this, we implement efficient computations based on saturation and clustering principles derived from other symbolic model checking techniques.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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