GRT:程序分析引导的随机测试

Lei Ma, Cyrille Artho, Cheng Zhang, Hiroyuki Sato, Johannes Gmeiner, R. Ramler
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引用次数: 58

摘要

我们提出引导随机测试(GRT),它使用静态和动态分析来包括自动测试生成的各个阶段中的程序类型、数据和依赖关系的信息。静态分析从被测系统中提取知识。通过状态模糊和连续覆盖率分析,进一步提高了测试覆盖率。我们在32个实际项目中评估了GRT,发现GRT在代码覆盖率(13%)和突变分数(9%)方面优于主要的同类技术。在研究的四个包含224个真实故障的缺陷4j基准测试中,GRT也显示出比同类技术更好的故障检测能力,发现147个故障(66%)。此外,在对十个流行的现实世界项目的最新版本进行深入评估后,GRT成功地检测出了超过20个由开发人员确认的未知缺陷。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GRT: Program-Analysis-Guided Random Testing (T)
We propose Guided Random Testing (GRT), which uses static and dynamic analysis to include information on program types, data, and dependencies in various stages of automated test generation. Static analysis extracts knowledge from the system under test. Test coverage is further improved through state fuzzing and continuous coverage analysis. We evaluated GRT on 32 real-world projects and found that GRT outperforms major peer techniques in terms of code coverage (by 13 %) and mutation score (by 9 %). On the four studied benchmarks of Defects4J, which contain 224 real faults, GRT also shows better fault detection capability than peer techniques, finding 147 faults (66 %). Furthermore, in an in-depth evaluation on the latest versions of ten popular real-world projects, GRT successfully detects over 20 unknown defects that were confirmed by developers.
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