Duplicate Bug Report Detection Using Dual-Channel Convolutional Neural Networks

Abstract

Developers rely on bug reports to fix bugs. The bug reports are usually stored and managed in bug tracking systems. Due to the different expression habits, different reporters may use different expressions to describe the same bug in the bug tracking system. As a result, the bug tracking system often contains many duplicate bug reports. Automatically detecting these duplicate bug reports would save a large amount of effort for bug analysis. Prior studies have found that deep-learning technique is effective for duplicate bug report detection. Inspired by recent Natural Language Processing (NLP) research, in this paper, we propose a duplicate bug report detection approach based on Dual-Channel Convolutional Neural Networks (DC-CNN). We present a novel bug report pair representation, i.e., dual-channel matrix through concatenating two single-channel matrices representing bug reports. Such bug report pairs are fed to a CNN model to capture the correlated semantic relationships between bug reports. Then, our approach uses the association features to classify whether a pair of bug reports are duplicate or not. We evaluate our approach on three large datasets from three open-source projects, including Open Office, Eclipse, Net Beans and a larger combined dataset, and the accuracy of classification reaches 0.9429, 0.9685, 0.9534, 0.9552 respectively. Such performance outperforms the two state-of-the-art approaches which also use deep-learning techniques. The results indicate that our dual-channel matrix representation is effective for duplicate bug report detection.