Exploiting Code Knowledge Graph for Bug Localization via Bi-directional Attention

Bug localization automatic localize relevant source files given a natural language description of bug within a software project. For a large project containing hundreds and thousands of source files, developers need cost lots of time to understand bug reports generated by quality assurance and localize these buggy source files. Traditional methods are heavily depending on the information retrieval technologies which rank the similarity between source files and bug reports in lexical level. Recently, deep learning based models are used to extract semantic information of code with significant improvements for bug localization. However, programming language is a highly structural and logical language, which contains various relations within and cross source files. Thus, we propose KGBugLocator to utilize knowledge graph embeddings to extract these interrelations of code, and a keywords supervised bi-directional attention mechanism regularize model with interactive information between source files and bug reports. With extensive experiments on four different projects, we prove our model can reach the new the-state-of-art(SOTA) for bug localization.