Property-directed k-induction

Dejan Jovanovic, B. Dutertre
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引用次数: 43

摘要

IC3和k-归纳通常用于无限状态系统的自动分析。我们提出了IC3的一个重新表述,将可达性检查与归纳推理分开。这使得算法更加模块化,并允许我们集成IC3和k归纳。我们称这种新方法为属性导向k归纳法(PD-KIND)。我们证明了k-感应比正则感应更强大,并且在插值方法的模假设下,PD-KIND比k-感应更强大。并且,将k-归纳作为IC3的不变量生成后端,新方法可以生成更简洁的不变量。我们已经在SALLY模型检查器中实现了该方法。我们提出了实证结果来支持其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Property-directed k-induction
IC3 and k-induction are commonly used in automated analysis of infinite-state systems. We present a reformulation of IC3 that separates reachability checking from induction reasoning. This makes the algorithm more modular, and allows us to integrate IC3 and k-induction. We call this new method property-directed k-induction (PD-KIND). We show that k-induction is more powerful than regular induction, and that, modulo assumptions on the interpolation method, PD-KIND is more powerful than k-induction. Moreover, with k-induction as the invariant generation back-end of IC3, the new method can produce more concise invariants. We have implemented the method in the SALLY model checker. We present empirical results to support its effectiveness.
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