Bug Localization with Combination of Deep Learning and Information Retrieval
An Ngoc Lam, Anh Tuan Nguyen, Hoan Anh Nguyen, Tien N. Nguyen · 2017
The automated task of locating the potential buggy files in a software project given a bug report is called bug localization. Bug localization helps developers focus on crucial files. However, the existing automated bug localization approaches face a key challenge, called lexical mismatch. Specifically, the terms used in bug reports to describe a bug are different from the terms and code tokens used in source files. To address that, we present a novel approach that uses deep neural network (DNN) in combination with rVSM, an information retrieval (IR) technique. rVSM collects the feature on the textual similarity between bug reports and source files. DNN is used to learn to relate the terms in bug reports to potentially different code tokens and terms in source files. Our empirical evaluation on real-world bug reports in the open-source projects shows that DNN and IR complement well to each other to achieve higher bug localization accuracy than individual models. Importantly, our new model, DNNLOC, with a combination of the features built from DNN, rVSM, and project's bug-fixing history, achieves higher accuracy than the state-of-the-art IR and machine learning techniques. In half of the cases, it iscorrect with just a single suggested file. In 66% of the time, acorrect buggy file is in the list of three suggested files. With 5 suggested files, it is correct in almost 70% of the cases.