For projects with thousands of files,finding the loca-tions 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 cal-culating text semantic similarity,we adapt the transformer network with IR-based bug localization methods to design a novel ap-proach,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 auto-matically 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 exist-ing methods in accuracy and performance of bug localization.