Deep learning based on word vector for improving bug triage performance
Guangliang Liu, Xiangyu Wang, Xiao Zhang · IET conference proceedings. · 2022
Bug triage is the process of quickly assigning bug reports to the most suitable developer so that the defect can be fixed quickly. Usually, the manual triage method not only consumes a lot of time for developers, but also has low ac-curacy. Therefore, how to automatically and efficiently allocate defects has become a hot research problem in the field of software maintenance. At present, the automatic bug triage method has become the mainstream technology, but there are also problems such as feature extraction and insufficient text representation ability. In view of the above problems, the task of automatic bug triage is regarded as a text classification problem. The method for automatic bug triage of multi-scale convolutional neural networks based on word vectors MCNN- BT can more effectively capture the important text features of bug reports, thereby providing more accurate defect repairer recommendation services. Empirical research is carried out in five software projects, Platform, Pde, Jdt, Cdt, Birt. The experimental results show that the prediction accuracy of MCNN-BT method is higher than that of the Naive Bayes method and the Latent Dirichlet Allocation(LDA) method.