{"title":"gMutant: A gCov based Mutation Testing Analyser","authors":"Monika Rani Golla, Sangharatna Godboley","doi":"10.1145/3578527.3578546","DOIUrl":null,"url":null,"abstract":"gMutant is a novel mutation analyser to measure the quality of test cases. It is based on gCov. The existing gCov outputs code coverage (line and branch). Here, we scale it up in order to produce an effective metric i.e., Mutation Score (%) which is useful to report the quality of test cases. The key idea is to generate one Mutant Meta Program that has all the mutants instrumented in it. Hence, for each test input, only the generated Mutant Meta Program gets executed, instead of executing all the mutants separately. This mitigates the computational cost challenge of Mutation Testing in producing Mutation score. The gMutant that has been proposed is a generic tool which can be plugged in with any tester. It needs test cases as well as a program to generate a score (%) and a descriptive report. In our experimentation, we used Bounded Model Checker and Fuzzer to generate test cases. The run time of the gMutant is very advantageous in contrast to the traditional method of computing mutation score. It gives scores, in 4.03 (s) for CBMC and 4.01 (s) for AFL, which proves its efficiency.","PeriodicalId":326318,"journal":{"name":"Proceedings of the 16th Innovations in Software Engineering Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th Innovations in Software Engineering Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3578527.3578546","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
gMutant is a novel mutation analyser to measure the quality of test cases. It is based on gCov. The existing gCov outputs code coverage (line and branch). Here, we scale it up in order to produce an effective metric i.e., Mutation Score (%) which is useful to report the quality of test cases. The key idea is to generate one Mutant Meta Program that has all the mutants instrumented in it. Hence, for each test input, only the generated Mutant Meta Program gets executed, instead of executing all the mutants separately. This mitigates the computational cost challenge of Mutation Testing in producing Mutation score. The gMutant that has been proposed is a generic tool which can be plugged in with any tester. It needs test cases as well as a program to generate a score (%) and a descriptive report. In our experimentation, we used Bounded Model Checker and Fuzzer to generate test cases. The run time of the gMutant is very advantageous in contrast to the traditional method of computing mutation score. It gives scores, in 4.03 (s) for CBMC and 4.01 (s) for AFL, which proves its efficiency.
gMutant是一种新的突变分析器,用于测量测试用例的质量。它基于gCov。现有的gCov输出代码覆盖率(行和分支)。在这里,我们将其扩大,以便产生一个有效的度量,例如,突变分数(%),它对报告测试用例的质量很有用。关键思想是生成一个包含所有突变体的突变体元程序。因此,对于每个测试输入,只执行生成的突变元程序,而不是单独执行所有的突变。这减轻了突变测试在产生突变分数时的计算成本挑战。已经提出的gMutant是一个可以插入任何测试器的通用工具。它需要测试用例以及生成分数(%)和描述性报告的程序。在我们的实验中,我们使用Bounded Model Checker和Fuzzer来生成测试用例。与传统的计算突变分数的方法相比,gMutant的运行时间非常有利。计算结果表明,CBMC和AFL分别为4.03分和4.01分,证明了算法的有效性。