面向语句的MBFL变异体约减策略

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。

MBFL with Statement-oriented Mutant Reduction Strategy

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.