面向语句的MBFL变异体约减策略

MBFL with Statement-oriented Mutant Reduction Strategy

在软件调试过程中如何高效、精确地定位程序中的错误代码是软件开发人员普遍关注的问题。MBFL是一种基于变异分析的错误定位技术,它在获得较高错误定位精度的同时会生成大量变异体,并在变异体上执行测试用例集,开销庞大。为了减少MBFL的变异执行开销,提出面向语句的变异体约减策略,通过分析测试用例的执行信息,按一定比例对每条由失败测试用例覆盖的语句生成的变异体集合进行约减。实验结果表明,在7个程序包的112个错误版本上,应用面向语句的变异体约减策略的MBFL,在保持较高错误定位精度的同时,能够有效减少73.51%~79.98%的变异执行开销。

How to efficiently and accurately locate faults in programs during the process of software debugging is taken up as a matter of common concern for software developers.MBFL is a fault localization technique based on mutation analysis,which precisely identifies the root cause of fault but incurs a high execution cost,since it needs to execute the test suite on a large amount of mutants.For decreasing the execution cost of MBFL,this paper presented a statement-oriented mutant reduction strategy,which selects a certain proportion set of mutants generated by statements covered by failed tests,according to the previous execution information of test suite.Empirical studies were conducted on 112 faulty versions from 7 program packages.The results indicate that this strategy can reduce 73.51%~79.98% mutation execution cost under the case of keeping high fault location precision.