Coverage-Based Fault Localization (CBFL) techniques are based on the conjecture that the program elements executed by more failed test cases and less passed test cases have more chance to be faulty. Coincidental Correct (CC) test case is one of the negative impacts on CBFL, for the reason that the CC test cases execute the faulty element but not propagate the faulty status to the final output. To alleviate the negative impact of CC test cases, in this paper, we propose a cluster-based technique to identify CC test cases from the passed test suite. To evaluate the effectiveness of our method, we conduct empirical studies on 102 versions from six programs. The experimental results show that, when using our method, it can accurately recognize CC test cases, where the precision and recall rate are both higher than 85%. A further study shows that, after removing identified CC test cases, the fault localization accuracy of SBFL can be improved apparently.

中文翻译：

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一种提高故障定位的通过测试用例聚类方法

基于覆盖的故障定位（CBFL）技术是基于这样一种推测，即由更多失败的测试用例和较少通过的测试用例执行的程序元素有更多的机会出错。巧合正确 (CC) 测试用例是对 CBFL 的负面影响之一，因为 CC 测试用例执行错误元素但不会将错误状态传播到最终输出。为了减轻 CC 测试用例的负面影响，在本文中，我们提出了一种基于集群的技术来从通过的测试套件中识别 CC 测试用例。为了评估我们方法的有效性，我们对来自六个程序的 102 个版本进行了实证研究。实验结果表明，使用我们的方法可以准确识别CC测试用例，准确率和召回率均高于85%。进一步的研究表明，