摘要： 基于覆盖的错误定位(CBFL)方法通过获取成功和失败测试用例的覆盖信息和执行结果对程序中的错误进行定位,但该方法未考虑偶然性成功测试用例的影响,降低了错误定位的准确率。为此,提出一种新的软件错误定位方法,通过分析程序变异减少偶然性成功测试用例的影响,改进怀疑度计算公式,并加入对变异影响的计算。实验结果表明,与传统CBFL方法相比,该方法能够有效提高错误定位的准确率。

关键词: 错误定位, 程序变异, 成功测试用例, 代码覆盖, 自动化测试

Abstract: Coverage-Based Fault Localization(CBFL) method can locate the fault by analyzing the information and results of success and failure test cases.However,CBFL ignores the impact of accidental successful test cases,and the existence of accidental successful test cases will reduce the accuracy of the fault location.Aiming at this problem,this paper presents a new fault localization method.It reduces the influence of accidental success test cases based on program mutation anatysis,improves the doubt degree calculation formula and adds calculation of the influence of variation.Experimental results show that this method can significantly improve the accuracy of fault localization compared with the traditional CBFL method.

Key words: fault localization, program mutation, successful test case, code coverage, automated test

中图分类号: 

• TP311