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Enlightened by the big success of pre-training in natural languageprocessing, pre-trained models for programming languages have been widely usedto promote code intelligence in recent years. In particular, BERT has been usedfor bug localization tasks and impressive results have been obtained. However,these BERT-based bug localization techniques suffer from two issues. First, thepre-trained BERT model on source code does not adequately capture the deepsemantics of program code. Second, the overall bug localization models neglectthe necessity of large-scale negative samples in contrastive learning forrepresentations of changesets and ignore the lexical similarity between bugreports and changesets during similarity estimation. We address these twoissues by 1) proposing a novel directed, multiple-label code graphrepresentation named Semantic Flow Graph (SFG), which compactly and adequatelycaptures code semantics, 2) designing and training SemanticCodeBERT based onSFG, and 3) designing a novel Hierarchical Momentum Contrastive BugLocalization technique (HMCBL). Evaluation results show that our methodachieves state-of-the-art performance in bug localization.

Pre-training Code Representation with Semantic Flow Graph for Effective Bug Localization