Improving Faulty Interaction Localization Using Logistic Regression
Improving Faulty Interaction Localization Using Logistic Regression
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DOI：
10.1109/qrs.2017.24
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发表时间：
2017-07
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通讯作者：
Kinari Nishiura;Eun-Hye Choi;O. Mizuno
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文献类型：
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作者：
Kinari Nishiura;Eun-Hye Choi;O. Mizuno
组合测试是检测被测系统参数交互作用引起的故障的一种常用技术。故障交互定位（FIL）是从组合测试用例及其测试结果中定位触发故障的参数-值组合的问题。FIL对于调试很重要，但对于大型测试套件和SUT来说是昂贵的，因为错误交互的候选数量随着参数的数量和交互的大小呈指数级增加。为了解决这个问题，本文提出了一种方法，采用逻辑回归。基于loGistic回归分析的回归系数（称为FROG）的FIL从其相应的回归系数计算要包括在故障交互中的每个参数-值组合的可疑性。我们通过将FROG应用于真实的应用SUT模型（例如TCAS，GCC和Apache）的组合t路测试用例（2 ≤ t ≤ 4）来评估所提出的方法。我们的实验结果表明，FROG可以有效地定位注入的错误交互，同时有效地减少了潜在的错误交互的候选人进行检查的数量。
Combinatorial testing is a widely used technique to detect failures caused by interactions of system under test (SUT) parameters. Faulty interaction localization (FIL) is a problem to locate parameter-value combinations that trigger failures from combinatorial test cases and their testing results. FIL is important for debugging, but is expensive for large test suites and SUTs since the number of candidates of faulty interactions increases exponentially with the number of parameters and the size of interactions. To address this problem, this paper proposes a method employing logistic regression. The proposed FIL based on Regression coefficients Of loGistic regression analysis (called FROG) computes the suspiciousness of each parameter-value combination to be included in a faulty interaction from its corresponding regression coefficient. We evaluate the proposed method by applying FROG to combinatorial t-way test cases (2 ≤ t ≤ 4) for real application SUT models, e.g. TCAS, GCC, and Apache. Our experiment results show that FROG can effectively locate faulty interactions injected while efficiently reducing the number of candidates of potential faulty interactions to be checked.