FDG：测试用例故障可诊断性增益的精确度量
FDG: A Precise Measurement of Fault Diagnosability Gain of Test Cases
摘要：
许多故障定位（FL）技术的性能直接取决于所使用的测试套件的质量。因此，它是非常有用的，能够精确地测量有多少诊断能力，每个测试用例可以引入时，添加到用于FL的测试套件。这样的措施可以帮助我们不仅优先考虑和选择测试用例用于FL，但也有效地增强测试套件，太弱，不能与FL技术。我们提出了FDG，一个新的措施的故障诊断增益为个人的测试用例。FDG的设计是基于我们对现有指标的分析，这些指标旨在优先考虑测试用例，以获得更好的FL。与其他指标不同，FDG利用正在进行的FL结果来强调需要更多信息的程序部分。我们对FDG与Defects4J的评估表明，它可以成功地帮助增强测试套件，以获得更好的FL。（平均2.3个测试用例），FDG可以通过对EvoSuite自动生成的测试用例进行优先级排序来有效地增强给定的测试套件：增强可以将FL结果的acc@1和acc@10平均提高11.6倍和2.2倍，在EvoSuite生成的断言的正确性上仅需要十次人工判断之后。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
