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Leveraging Control Flow Knowledge in SMT Solving of Program Verification
ACM Transactions on Software Engineering and Methodology ( IF 4.4 ) Pub Date : 2021-05-10 , DOI: 10.1145/3446211
Jianhui Chen 1 , Fei He 1
Affiliation  

Satisfiability modulo theories (SMT) solvers have been widely applied as the reasoning engine for diverse software analysis and verification technologies. The efficiency of the SMT solver has significant effects on the performance of these technologies. However, current SMT solvers are designed for the general purpose of constraint solving. Lots of useful knowledge of programs cannot be utilized during SMT solving. As a result, the SMT solver may spend much effort to explore redundant search space. In this article, we propose a novel approach to utilizing control-flow knowledge in SMT solving. With this technique, the search space can be considerably reduced, and the efficiency of SMT solving is observably improved. We conducted extensive experiments on credible benchmarks. The results show significant improvements of our approach.

中文翻译:

在 SMT 解决程序验证中利用控制流知识

可满足性模理论 (SMT)求解器已被广泛用作各种软件分析和验证技术的推理引擎。SMT 求解器的效率对这些技术的性能有显着影响。然而,当前的 SMT 求解器是为约束求解的一般目的而设计的。在 SMT 求解过程中无法利用许多有用的程序知识。因此,SMT 求解器可能会花费大量精力来探索冗余搜索空间。在本文中,我们提出了一种在 SMT 求解中利用控制流知识的新方法。使用这种技术,可以显着减少搜索空间,并显着提高 SMT 求解的效率。我们在可靠的基准上进行了广泛的实验。结果表明我们的方法有了显着的改进。
更新日期:2021-05-10
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