组合认知复杂度的程序谱软件错误定位方法

基于程序谱的软件错误定位(spectrum-based fault localization,SBFL)技术收集测试用例结果和语句覆盖信息,用以计算每条语句的可疑度值.认知复杂度是软件复杂性度量工具,其值高的代码较易出错.为提升错误定位性能,提出一种语句级认知复杂度和SBFL相组合的方法对语句排序.当多条语句可疑度值相等时,新方法优先检查认知复杂度高的语句.测试数据集有925个错误版本,包含Java、C和C++项目.实验结果证实,加入认知复杂度后,传统的SBFL技术能减少待排查语句.

Technique of spectrum-based fault localization using cognitive complexity

Spectrum-based fault localization(SBFL) technology collects test case results and statements coverage information to calculate the suspiciousness of each statement. Cognitive complexity is a software complexity metric, and the code with high value is prone to make mistakes. In order to improve the performance of fault localization, this paper proposed a method combining statement level cognitive complexity and SBFL to rank statements. When two or more statements were assigned the same suspiciousness, the method calculated cognitive complex values of every statement. The higher cognitive complexity value corresponding to statement, the more likely that it contained a bug. This test dataset has 925 wrong edition, includes Java projects, C projects and C++ projects. Experimental result demonstrates that the proposed method reduces fault localization cost that exists in SBFL approaches.