Fault localization techniques aim to localize faulty statements using the information gathered from both passed and failed test cases. We present a mutation-based fault localization technique called MuSim. MuSim identifies the faulty statement based on its computed proximity to different mutants. We study the performance of MuSim by using four different similarity metrics. To satisfactorily measure the effectiveness of our proposed approach, we present a new evaluation metric called Mut_Score. Based on this metric, on an average, MuSim is 33.21% more effective than existing fault localization techniques such as DStar, Tarantula, Crosstab, Ochiai.

中文翻译：

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MuSim：使用测试用例接近的基于突变的故障定位

故障定位技术旨在使用从通过和失败的测试用例中收集的信息来定位错误语句。我们提出了一种基于突变的故障定位技术，称为 MuSim。MuSim 根据计算出的与不同突变体的接近度来识别错误语句。我们通过使用四种不同的相似性指标来研究 MuSim 的性能。为了令人满意地衡量我们提出的方法的有效性，我们提出了一个新的评估指标，称为 Mut_Score。基于这个指标，平均而言，MuSim 比现有的故障定位技术（如 DStar、Tarantula、Crosstab、Ochiai）效率高 33.21%。