Fault localization is one of the most important and difficult tasks in the software debugging process. Therefore, several methods have been proposed to automate and improve this process. Mutation-based fault localization is one of the states of the art techniques that try to locate faults by executing different mutants of the faulty program. In addition to favorable results, it is along with a massive increase in mutation execution cost. In this paper, we propose a new mutation-based fault localization approach called SMBFL, that aim to reduce the execution cost by reducing the number of statements to be mutated. As fewer mutants execute with SMBFL, the whole process will become faster and the cost will decrease. SMBFL only examines the statements in the dynamic slice of the program under test. The statements that present in the dynamic slice have a direct effect on the execution of the program with the specified test case. In the SMBFL method, the suspiciousness score of program statements is measured based on the entropy of their mutants. The proposed formula, MuEn, determines the suspiciousness score based on the result of executing mutants of each statement of the program. SMBFL is evaluated during a series of tests. The results show a relative increase in the accuracy of fault localization, by an average of 14.2%, and a decrease in the execution time of the fault localization process, by an average of 24.3%. Finally, the MuEn formula applies the least execution overhead to the fault localization process.

中文翻译：

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SMBFL：基于切片的基于突变的故障定位的成本降低

故障定位是软件调试过程中最重要、最困难的任务之一。因此，已经提出了几种方法来自动化和改进这个过程。基于突变的故障定位是尝试通过执行错误程序的不同突变体来定位故障的最先进技术之一。除了有利的结果外，还伴随着变异执行成本的大幅增加。在本文中，我们提出了一种新的基于变异的故障定位方法，称为 SMBFL，旨在通过减少要变异的语句数量来降低执行成本。随着 SMBFL 执行的突变体越少，整个过程将变得更快，成本将降低。SMBFL 只检查被测程序动态切片中的语句。动态切片中出现的语句对具有指定测试用例的程序的执行有直接影响。在 SMBFL 方法中，程序语句的可疑性分数是基于其突变体的熵来测量的。建议的公式 MuEn 根据执行程序每个语句的突变体的结果来确定可疑性分数。SMBFL 在一系列测试中进行评估。结果表明，故障定位的准确率相对提高了 14.2%，故障定位过程的执行时间平均减少了 24.3%。最后，MuEn 公式将最少的执行开销应用于故障定位过程。程序语句的可疑性分数是根据其突变体的熵来衡量的。建议的公式 MuEn 根据执行程序每个语句的突变体的结果来确定可疑性分数。SMBFL 在一系列测试中进行评估。结果表明，故障定位的准确率相对提高了 14.2%，故障定位过程的执行时间平均减少了 24.3%。最后，MuEn 公式将最少的执行开销应用于故障定位过程。程序语句的可疑性分数是根据其突变体的熵来衡量的。建议的公式 MuEn 根据执行程序每个语句的突变体的结果来确定可疑性分数。SMBFL 在一系列测试中进行评估。结果表明，故障定位的准确率相对提高了 14.2%，故障定位过程的执行时间平均减少了 24.3%。最后，MuEn 公式将最少的执行开销应用于故障定位过程。结果表明，故障定位的准确率相对提高了 14.2%，故障定位过程的执行时间平均减少了 24.3%。最后，MuEn 公式将最少的执行开销应用于故障定位过程。结果表明，故障定位的准确率相对提高了 14.2%，故障定位过程的执行时间平均减少了 24.3%。最后，MuEn 公式将最少的执行开销应用于故障定位过程。