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| 增强上下文的错误定位技术 |
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张卓1, 谭庆平1, 毛晓光1, 雷晏2,3, 常曦4, 薛建新1,4
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1.国防科技大学 计算机学院, 湖南 长沙 410072;2.重庆大学 大数据与软件学院, 重庆 400044;3.信息物理社会可信服务计算教育部重点实验室(重庆大学), 重庆 400044;4.上海第二工业大学 计算机与信息工程学院, 上海 200127
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| 摘要: |
| 错误定位就是寻找程序错误的位置.现有的错误定位方法大多利用测试用例的覆盖信息，以标识一组导致程序失效的可疑语句，却忽视了这些语句相互作用导致失效的上下文.因此，提出一种增强上下文的错误定位方法Context-FL，以构建上下文的方式来优化错误定位性能.Context-FL利用动态切片技术构建数据与控制相关性的错误传播上下文，显示了导致失效的语句之间传播依赖关系；然后，基于可疑值度量来区分上下文片段中不同语句的可疑度；最后，Context-FL以标记可疑值的上下文作为定位结果.实验结果表明，Context-FL优于8种典型错误定位方法. |
| 关键词: 错误定位 上下文 动态切片 SFL 可疑值 |
| DOI：10.13328/j.cnki.jos.005677 |
| 分类号: |
| 基金项目:国家自然科学基金（61602504，61672529，61379054，61502296） |
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| Effective Fault Localization Approach Based on Enhanced Contexts |
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ZHANG Zhuo1, TAN Qing-Ping1, MAO Xiao-Guang1, LEI Yan2,3, CHANG Xi4, XUE Jian-Xin1,4
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1.College of Computer, National University of Defense Technology, Changsha 410072, China;2.School of Big Data and Software Engineering, Chongqing University, Chongqing 400044, China;3.Key Laboratory of Dependable Service Computing in Cyber Physical Society(Chongqing University), Ministry of Education, Chongqing 400044, China;4.College of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 200127, China
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| Abstract: |
| Fault localization is a process to determine the root causes of abnormal behavior of a faulty program. Most existing fault localization approaches usually utilize coverage information of test cases to identify a set of isolated statements responsible for a failure, but do not show how these statements act on each other to cause the failure. Thus, this study proposes Context-FL:An approach enhancing contexts for these existing localization approaches by constructing contexts for fault localization optimization. Specifically, Context-FL uses dynamic slicing technology to construct a context showing how data/control dependence propagates to cause the faulty output. Then, it adopts suspiciousness evaluation to distinguish the elements of the context in terms of the suspiciousness being faulty. Finally, Context-FL outputs the context with suspiciousness as the localization result. The empirical results show that the proposed approach significantly outperforms 8 state-of-the-art fault localization techniques. |
| Key words: fault localization context dynamic slice SFL suspiciousness |
