BugPecker: Locating Faulty Methods with Deep Learning on Revision Graphs
Given a bug report of a project, the task of locating the faults of the bug report is called fault localization. To help programmers in the fault localization process, many approaches have been proposed, and have achieved promising results to locate faulty files. However, it is still challenging to locate faulty methods, because many methods are short and do not have sufficient details to determine whether they are faulty. In this paper, we present BugPecker, a novel approach to locate faulty methods based on its deep learning on revision graphs. Its key idea includes (1) building revision graphs and capturing the details of past fixes as much as possible, and (2) discovering relations inside our revision graphs to expand the details for methods and calculating various features to assist our ranking. We have implemented BugPecker, and evaluated it on three open source projects. The early results show that BugPecker achieves a mean average precision (MAP) of 0.263 and mean reciprocal rank (MRR) of 0.291, which improve the prior approaches significantly. For example, BugPecker improves the MAP values of all three projects by five times, compared with two recent approaches such as DNNLoc-m and BLIA 1.5.
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Junming CaoSchool of Software, Shanghai Jiao Tong University, Shouliang YangSchool of Software, Shanghai Jiao Tong University, Wenhui JiangSchool of Software, Shanghai Jiao Tong University, Hushuang ZengSchool of Software, Shanghai Jiao Tong University, Beijun ShenSchool of Software, Shanghai Jiao Tong University, Hao ZhongShanghai Jiao Tong University