Combinatorial testing (CT) has been proven effective in revealing the failures caused by the interaction of factors that affect the behavior of a system. The theory of Minimal Failure-Causing Schema (MFS) has been proposed to isolate the cause of a failure after CT. Most algorithms that aim to identify MFS focus on handling a single fault in the System Under Test (SUT). However, we argue that multiple faults are more common in practice, under which masking effects may be triggered so that some failures cannot be observed. The traditional MFS theory lacks a mechanism to handle such effects; hence, they may incorrectly isolate the MFS. To address this problem, we propose a new MFS model that takes into account multiple faults. We first formally analyze the impact of the multiple faults on existing MFS identifying algorithms, especially in situations where masking effects are triggered by multiple faults. We then develop an approach that can assist traditional algorithms to better handle multiple faults. Empirical studies were conducted using several kinds of open-source software, which showed that multiple faults with masking effects do negatively affect traditional MFS identifying approaches and that our approach can help to alleviate these effects.

中文翻译：

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在存在多个故障的情况下识别导致故障的模式

组合测试 (CT) 已被证明可有效揭示由影响系统行为的因素相互作用引起的故障。已经提出了最小故障原因模式 (MFS) 理论来隔离 CT 后的故障原因。大多数旨在识别 MFS 的算法都专注于处理被测系统 (SUT) 中的单个故障。然而，我们认为在实践中多故障更为常见，在这种情况下可能会触发掩蔽效应，从而无法观察到某些故障。传统的 MFS 理论缺乏处理这种影响的机制；因此，他们可能会错误地隔离 MFS。为了解决这个问题，我们提出了一种新的 MFS 模型，该模型考虑了多个故障。我们首先正式分析了多个故障对现有 MFS 识别算法的影响，特别是在多个故障触发掩蔽效应的情况下。然后，我们开发了一种方法，可以帮助传统算法更好地处理多个故障。使用多种开源软件进行了实证研究，结果表明具有掩蔽效应的多个故障确实会对传统的 MFS 识别方法产生负面影响，而我们的方法可以帮助减轻这些影响。