Bug Localization with Features Crossing and Structured Semantic Information Matching

Abstract

Bug localization techniques aim to locate the relevant buggy source files according to the bug described by the given bug report, so as to improve the localization efficiency of developers and reduce the cost of software maintenance. The traditional bug localization techniques based on Information Retrieval (IR) usually use the classical text retrieval model and combines the specific domain knowledge features in software engineering to locate the bugs. However, there exists the vocabulary mismatch problem between the bug report and the source file, which may affect the performance of bug localization. Therefore, the relevant deep learning model was introduced later to compute the similarity between the bug report and the source file from the perspective of semantic features. Bug localization approaches based on IR and deep learning have their own advantages and disadvantages, so this paper proposes a model named LocFront which combines IR and deep learning. On the one hand, the Features Crossing module in LocFront carries out the deep crossing operation on the extracted software-specific features to fully acquire the linear and nonlinear relationships. On the other hand, the Structured Semantic Information Matching module in LocFront performs semantic matching on the structured information between the bug report and the source file. Then the Fusion module in LocFront fuses the results of the two above modules to obtain the final localization score. The experimental results on five benchmark datasets show that LocFront outperforms the state-of-the-art bug localization approaches.