Modeling the Diagnostic Efficiency of Regression Test Suites
Modeling the Diagnostic Efficiency of Regression Test Suites
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DOI：
10.1109/icstw.2011.22
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发表时间：
2011-03
期刊：
影响因子：
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通讯作者：
Alberto González-Sanchez;H. Groß;A. V. Gemund
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文献类型：
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作者：
Alberto González-Sanchez;H. Groß;A. V. Gemund
诊断性能是根据检测到故障后必须花费的手动努力来衡量的，这并不是诊断的唯一重要质量。效率，即测试数量和最终诊断的收敛速度也是诊断的非常重要的质量。在本文中，我们提出了一个分析模型和一个模拟模型，以预测使用信息增益算法优先考虑测试套件的诊断效率。我们表明，除了系统本身的大小外，还需要最佳的覆盖密度和均匀的覆盖率分布来实现有效的诊断。我们的模型使我们能够确定将IG与当前的测试套件一起使用将提供良好的诊断效率，并使我们能够定义生成或改进测试套件的标准。
Diagnostic performance, measured in terms of the manual effort developers have to spend after faults are detected, is not the only important quality of a diagnosis. Efficiency, i.e., the number of tests and the rate of convergence to the final diagnosis is a very important quality of a diagnosis as well. In this paper we present an analytical model and a simulation model to predict the diagnostic efficiency of test suites when prioritized with the information gain algorithm. We show that, besides the size of the system itself, an optimal coverage density and uniform coverage distribution are needed to achieve an efficient diagnosis. Our models allow us to decide whether using IG with our current test suite will provide a good diagnostic efficiency, and enable us to define criteria for the generation or improvement of test suites.