TSLocator: A Transformer-Based Approach to Bug Localization

作     者：HU Cheng XIAO Yuliang HU Cheng;XIAO Yuliang

作者机构：HongYi Honor CollegeWuhan UniversityWuhan 430072HubeiChina School of Computer ScienceWuhan UniversityWuhan 430072HubeiChina 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2021年第26卷第2期

页      面：200-206页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：bug localization Transformer information retrieval SVM(support vector machine) natural language processing(NLP) 

摘      要：For projects with thousands of files, finding the locations of bugs is time-consuming and labor-intensive. Bug reports as a potential resource to help locate bugs in source codes have been used to design automatic tools to solve this problem. Existing information retrieval(IR)-based bug localization methods rely heavily on the similarity score between bug report and historical reports. As deep learning methods show great advantages in calculating text semantic similarity, we adapt the transformer network with IR-based bug localization methods to design a novel approach, TSLocator, to bug localization. In TSLocator, we propose five new features between bug reports and source codes. We use SVMRank to model the relation between all the six features and the actual buggy file. Given a new bug report, TSLocator automatically calculates the features and linearly weights the features to produce a suspicious score for all candidate files. TSLocator recommends a list of suspicious buggy files ranked by the score. The experimental results show that TSLocator outperforms existing methods in accuracy and performance of bug localization.