具有可达性确定优化的静态分析工具

Yuexing Wang, Min Zhou, Yu Jiang, Xiaoyu Song, M. Gu, Jiaguang Sun
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引用次数: 5

摘要

为了减少静态分析的误报,许多工具收集路径约束并集成SMT求解器来过滤不可达的执行路径。然而,SMT求解器的累计调用和计算耗费大量时间和资源。本文介绍了TsmartLW,这是一个备选的静态分析工具,我们在其中实现了一个路径约束求解引擎,以加快可达性的确定。在引擎中,典型的约束模式类型首先是基于对大量代码库的经验研究来定义的。针对每种模式,设计并实现了约束求解算法。对于每个程序,引擎预测最合适的策略,然后应用该策略来解决路径约束。在一些知名的基准测试和实际应用程序上的实验结果表明,TsmartLW比一些最先进的静态分析工具要快。例如,它比CPAchecker快1.32倍,我们的引擎在求解路径约束方面比SMT求解器快369倍。演示视频可在https://www.youtube.com/watch?v=5c3ARhFclHA&t=2s上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A static analysis tool with optimizations for reachability determination
To reduce the false positives of static analysis, many tools collect path constraints and integrate SMT solvers to filter unreachable execution paths. However, the accumulated calling and computing of SMT solvers are time and resource consuming. This paper presents TsmartLW, an alternate static analysis tool in which we implement a path constraint solving engine to speed up reachability determination. Within the engine, typical types of constraint-patterns are firstly defined based on an empirical study of a large number of code repositories. For each pattern, a constraint solving algorithm is designed and implemented. For each program, the engine predicts the most suitable strategy and then applies the strategy to solve path constraints. The experimental results on some well-known benchmarks and real-world applications show that TsmartLW is faster than some state-of-the-art static analysis tools. For example, it is 1.32× faster than CPAchecker and our engine is 369× faster than SMT solvers in solving path constraints. The demo video is available at https://www.youtube.com/watch?v=5c3ARhFclHA&t=2s.
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