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.
