Developer Activity Motivated Bug Triaging: Via Convolutional Neural Network

Abstract

As bugs become prevalent in software development, bug triaging has become one of the most important activities in software maintenance. To decrease the time cost in manual work, text classification techniques have been applied in automatic bug triaging. In this paper, we present a new automatic bug triaging approach which is based on convolution neural network (CNN) and developer activities. Firstly, we implement the word vector representation of the text features in bug report by using Word2vec. Then, we combine CNN with batch normalization, pooling and full connection approach to learn from the word vector representation of bug report with known fixers. In addition, we also study the recent activities of the developers which can effectively distinguish similar bug reports and get a more suitable developer recommendation list. We empirically investigate the accuracy of automatic bug triaging on three large open source projects, namely Eclipse, Mozilla and NetBeans. The experimental results show that our approach can effectively improve the performance of automatic bug triaging.