FDG:测试用例的故障诊断增益的精确测量

Gabin An, S. Yoo
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引用次数: 0

摘要

许多故障定位(FL)技术的性能直接依赖于所使用的测试套件的质量。因此,当添加到用于FL的测试套件时,能够精确地测量每个测试用例可以引入多少诊断能力是非常有用的。这样的测量不仅可以帮助我们确定用于FL的测试用例的优先级并选择测试用例,而且还可以有效地增加太弱而不能与FL技术一起使用的测试套件。针对单个测试用例,我们提出了一种新的故障诊断增益度量FDG。FDG的设计是基于我们对现有指标的分析,这些指标是为了更好地优化测试用例而设计的。与其他指标不同,FDG利用正在进行的测试用例结果来强调程序中需要更多信息的部分。我们使用缺陷4j对FDG的评估表明,它可以成功地帮助增加测试套件以获得更好的FL。当只给出几个失败的测试用例(平均2.3个测试用例)时,FDG可以通过优先考虑EvoSuite自动生成的测试用例来有效地增加给定的测试套件:在只需要10次人工判断EvoSuite生成的断言的正确性之后,增强功能可以将FL结果的acc@1和acc@10平均提高11.6倍和2.2倍。
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FDG: a precise measurement of fault diagnosability gain of test cases
The performance of many Fault Localisation (FL) techniques directly depends on the quality of the used test suites. Consequently, it is extremely useful to be able to precisely measure how much diagnostic power each test case can introduce when added to a test suite used for FL. Such a measure can help us not only to prioritise and select test cases to be used for FL, but also to effectively augment test suites that are too weak to be used with FL techniques. We propose FDG, a new measure of Fault Diagnosability Gain for individual test cases. The design of FDG is based on our analysis of existing metrics that are designed to prioritise test cases for better FL. Unlike other metrics, FDG exploits the ongoing FL results to emphasise the parts of the program for which more information is needed. Our evaluation of FDG with Defects4J shows that it can successfully help the augmentation of test suites for better FL. When given only a few failing test cases (2.3 test cases on average), FDG can effectively augment the given test suite by prioritising the test cases generated automatically by EvoSuite: the augmentation can improve the acc@1 and acc@10 of the FL results by 11.6x and 2.2x on average, after requiring only ten human judgements on the correctness of the assertions EvoSuite generates.
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