Abstract Spectrum-based fault localization (SBFL) is a promising approach to reduce the cost of program debugging and there has been a large body of research on introducing effective SBFL techniques. However, performance of these techniques can be adversely affected by the existence of coincidental correct (CC) test cases in the test suites. Such test cases execute the faulty statement but do not cause failures. Given that coincidental correctness is prevalent, it is necessary to precisely identify CC test cases and eliminate their effects from test suites. To do so, in this paper, we propose several important factors to identify CC test cases and model the CC identification process as a decision making system by constructing a fuzzy expert system and proposing a novel fuzzy CC identification method, namely FCCI. FCCI estimates the CC likelihood of passed test cases using the designed fuzzy rules, which effectively correlate the proposed CC identification factors. We evaluated FCCI by conducting extensive experiments on 17 popular and open source subject programs ranging from small- to large-scale containing both artificial and real faults. The experimental results indicate that FCCI successfully improves the accuracy of the CC identification as well as the accuracy of the representative SBFL techniques.

中文翻译：

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FCCI：一种用于识别巧合正确测试用例的模糊专家系统

摘要 基于频谱的故障定位 (SBFL) 是降低程序调试成本的一种很有前途的方法，并且已经有大量关于引入有效 SBFL 技术的研究。然而，这些技术的性能可能会受到测试套件中巧合正确 (CC) 测试用例的影响。这样的测试用例执行错误的语句但不会导致失败。鉴于巧合正确性普遍存在，有必要精确识别 CC 测试用例并从测试套件中消除它们的影响。为此，在本文中，我们提出了几个重要因素来识别 CC 测试用例，并将 CC 识别过程建模为决策系统，通过构建模糊专家系统并提出一种新的模糊 CC 识别方法，即 FCCI。FCCI 使用设计的模糊规则估计通过测试用例的 CC 可能性，这有效地关联了提议的 CC 识别因素。我们通过对 17 个流行的开源主题程序进行广泛的实验来评估 FCCI，这些程序从包含人为和真实故障的小规模到大规模不等。实验结果表明，FCCI 成功地提高了 CC 识别的准确性以及代表性 SBFL 技术的准确性。