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.
Wed 17 NovDisplayed time zone: Hobart change
11:00 - 12:00 | Finding DefectsResearch Papers / NIER track / Journal-first Papers at Kangaroo Chair(s): Xiao Liu School of Information Technology, Deakin University | ||
11:00 20mTalk | Graph-based Incident Aggregation for Large-Scale Online Service Systems Research Papers Zhuangbin Chen Chinese University of Hong Kong, China, Yuxin Su The Chinese University of Hong Kong, Jinyang Liu , Hongyu Zhang University of Newcastle, Xuemin Wen Huawei Technologies, Xiao Ling Huawei Technologies, Yongqiang Yang Huawei Technologies, Michael Lyu The Chinese University of Hong Kong | ||
11:20 20mTalk | PyExplainer: Explaining the Predictions of Just-In-Time Defect Models Research Papers Chanathip Pornprasit Monash University, Kla Tantithamthavorn Monash University, Jirayus Jiarpakdee Monash University, Australia, Michael Fu Monash University, Patanamon Thongtanunam University of Melbourne | ||
11:40 10mTalk | Towards Systematic and Dynamic Task Allocation for Collaborative Parallel Fuzzing NIER track Thuan Pham The University of Melbourne, Manh-Dung Nguyen Montimage R&D, France, Quang-Trung Ta National University of Singapore, Toby Murray University of Melbourne, Benjamin I.P. Rubinstein University of Melbourne | ||
11:50 10mTalk | An Extensive Study on Smell-Aware Bug Localization Journal-first Papers Aoi Takahashi Tokyo Institute of Technology, Natthawute Sae-Lim Tokyo Institute of Technology, Shinpei Hayashi Tokyo Institute of Technology, Motoshi Saeki Nanzan University Link to publication DOI | ||