Software Fault Localization Based on Combining Information Retrieval and Mutation Analysis*
Lei Yue, Jingwen Li, Liwei Zheng, Li Li, Zhanqi Cui · 2023
Information Retrieval-based Bug Localization (IRBL) and Mutation-based Fault Localization (MBFL) are two widely used static and dynamic fault localization techniques, respectively. IRBL takes less time and utilizes more static information of software, while MBFL achieves high accuracy and the results are not easily affected by coincidental correctness test cases. However, the granularity of IRBL is coarse and MBFL consumes a lot of time to generate and execute mutants. In this paper, we propose IRMBFL (Information Retrieval and Mutation Analysis Based Software Fault Localization), a software fault localization technique that combines information retrieval and mutation analysis. First, the suspiciousness of source code files is measured by calculating the text similarity between the bug report and the source code to extract the files which may contain bugs. Then, the extracted files are mutated and tested. Finally, the bug statements are located by analyzing the changes in the execution results of the test cases. The experiments are conducted on the Defects4J dataset and$E_{inspect}{@} n$and EXAM are used as evaluation metrics to evaluate the performance of IRMBFL. The experimental results show that IRMBFL locates 14 and 3 more bug statements than BugLocator and Metallaxis for$E_{inspect}{@}n$when$n=1$. IRMBFL outperforms BugLocator on all projects and outperforms Met-allaxis on 2 out of 6 projects in terms of EXAM. In addition, the average bug localization time overhead of IRMBFL is reduced from 73.87% to 99.78% than Metallaxis.