随机模型检验中反例生成的遗传算法

Proceedings of the 2018 VII International Conference on Network, Communication and Computing Pub Date : 2018-12-14 DOI:10.1145/3301326.3301348

Tingting Zheng, Yang Liu

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引用次数: 3

摘要

反例是使系统工程师相信形式验证的价值的最有效的特征。证明了随机模型检验中最小反例的生成是np完全的。本文应用遗传算法生成随机模型检验的一个反例。我们使用诊断子图来表示反例，并采用间接编码方法来生成更有效的路径。我们基于随机模型检查器PRISM实现了我们的方法，并将其应用于一些案例，以说明它的适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Genetic Algorithm for Generating Counterexample in Stochastic Model Checking

Counterexamples are the most effective feature to convince system engineers about the value of formal verification. Generating the smallest counterexample in stochastic model checking has been proved to be NP-complete. In this paper, we apply the genetic algorithm to generate a counterexample for stochastic model checking. We use the diagnostic subgraph to represent a counterexample and employs indirect coding method to generate the more effective path. We implemented our method based on the stochastic model checker PRISM and applied it to some cases, in order to illustrate its applicability.

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Proceedings of the 2018 VII International Conference on Network, Communication and Computing

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