Detecting Duplicate Bug Reports with Convolutional Neural Networks

Abstract

Bug tracking systems are widely used to track bugs from users during the lifecycle of software systems for reliability maintenance. When software systems have a large user base, which is common in practice, different users may encounter a same bug and then generate many duplicate bug reports. In a large project, each bug report is usually assigned to a different developer or team to parallelize the bug debugging and fixing activities. The presence of duplicate bug reports thus leads to many unnecessary efforts of developers spending on debugging a same issue. To speed up the bug fixing process and save the cost of developers, there is a high demand for automated detection of duplicate bug reports. In this paper, we explore the use of powerful deep learning techniques, including word embedding and Convolution Neural Networks, to calculate the similarity between a pair of bug reports and thus identify possible duplicates. In contrast to previous work that consider only common words between bug descriptions for lexical similarity computation, our approach is able to better capture semantic similarity between words. We further improve traditional CNN models by combining some domain-specific features extracted from bug reports. Evaluation results on the bug reports from four popular open-source projects show that DBR-CNN has made a significant improvement on duplicate detection accuracy over traditional approaches.