The performance of coverage-based fault localization greatly depends on the quality of test cases being executed. These test cases execute some lines of the given program and determine whether the underlying tests are passed or failed. In particular, some test cases may be well-behaved (i.e., passed) while executing faulty statements. These test cases, also known as coincidentally correct test cases, may negatively influence the performance of the spectra-based fault localization and thus be less helpful as a tool for the purpose of automated debugging. In other words, the involvement of these coincidentally correct test cases may introduce noises to the fault localization computation and thus cause in divergence of effectively localizing the location of possible bugs in the given code. In this paper, we propose a hybrid approach of ensemble learning combined with a supervised learning algorithm namely, Random Forests (RF) for the purpose of correctly identifying test cases that are mislabeled to be the passing test cases. A cost-effective analysis of flipping the test status or trimming (i.e., eliminating from the computation) the coincidental correct test cases is also reported.

中文翻译：

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通过随机森林检测巧合正确的测试用例

基于覆盖的故障定位的性能在很大程度上取决于正在执行的测试用例的质量。这些测试用例执行给定程序的某些行并确定底层测试是通过还是失败。特别是，某些测试用例在执行错误语句时可能表现良好（即通过）。这些测试用例，也称为巧合正确的测试用例，可能会对基于频谱的故障定位的性能产生负面影响，因此作为自动调试工具的帮助不大。换句话说，这些巧合正确的测试用例的参与可能会给故障定位计算带来噪音，从而导致在给定代码中有效定位可能错误的位置的分歧。在本文中，我们提出了一种集成学习与监督学习算法相结合的混合方法，即随机森林 (RF)，目的是正确识别被错误标记为通过的测试用例的测试用例。还报告了翻转测试状态或修剪（即从计算中消除）巧合的正确测试用例的成本效益分析。