2021/04/22 by Aoi Takahashi, Natthawute Sae-Lim, Shinpei Hayashi +1 · 11 citations
Computer Science · #Benchmark (surveying) #Class (philosophy) #Code (set theory) #Pattern recognition (psychology) #Software #Software Engineering Research #Software Engineering Techniques and Practices #Software Testing and Debugging Techniques #Software bug #cs.SE
paper · pdf · open access · doi:10.1016/j.jss.2021.110986
published in Journal of Systems and Software 178, 110986 (Elsevier BV) · 19 pages, JSS
arxiv created 2021/04/22 · openalex publication_date 2021/04/24 · openalex created_date 2021/04/26 · arxiv updated 2021/05/07 · openalex updated_date 2026/08/05
Bug localization is an important aspect of software maintenance because it can locate modules that should be changed to fix a specific bug. Our previous study showed that the accuracy of the information retrieval (IR)-based bug localization technique improved when used in combination with code smell information. Although this technique showed promise, the study showed limited usefulness because of the small number of: (1) projects in the dataset, (2) types of smell information, and (3) baseline bug localization techniques used for assessment. This paper presents an extension of our previous experiments on Bench4BL, the largest bug localization benchmark dataset available for bug localization. In addition, we generalized the smell-aware bug localization technique to allow different configurations of smell information, which were combined with various bug localization techniques. Our results confirmed that our technique can improve the performance of IR-based bug localization techniques for the class level even when large datasets are processed. Furthermore, because of the optimized configuration of the smell information, our technique can enhance the performance of most state-of-the-art bug localization techniques.