Combining BMC and Complementary Approximate Reachability to Accelerate Bug-Finding

Xiaoyu Zhang, Shengping Xiao, Jianwen Li, G. Pu, O. Strichman
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Abstract

Bounded Model Checking (BMC) is so far considered as the best engine for bug-finding in hardware model checking. Given a bound K, BMC can detect if there is a counterexample to a given temporal property within K steps from the initial state, thus performing a global-style search. Recently, a SAT-based model-checking technique called Complementary Approximate Reachability (CAR) was shown to be complementary to BMC, in the sense that frequently they can solve instances that the other technique cannot, within the same time limit. CAR detects a counterexample gradually with the guidance of an over-approximating state sequence, and performs a local-style search. In this paper, we consider three different ways to combine BMC and CAR. Our experiments show that they all outperform BMC and CAR on their own, and solve instances that cannot be solved by these two techniques. Our findings are based on a comprehensive experimental evaluation using the benchmarks of two hardware model checking competitions.
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结合BMC和互补近似可达性来加速bug查找
有界模型检查(BMC)被认为是目前硬件模型检查中最好的bug查找引擎。给定一个界限K, BMC可以在初始状态的K步内检测给定时间属性是否存在反例,从而执行全局式搜索。最近,一种称为互补近似可达性(CAR)的基于sat的模型检查技术被证明是对BMC的补充,因为它们通常可以在相同的时间限制内解决其他技术无法解决的实例。CAR算法在过逼近状态序列的引导下逐步检测反例,并进行局部搜索。在本文中,我们考虑了三种不同的方式来结合BMC和CAR。我们的实验表明,它们本身都优于BMC和CAR,并解决了这两种技术无法解决的实例。我们的发现是基于使用两个硬件模型检查竞赛基准的综合实验评估。
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