Automatic Assignment Method for Software Bug Based on Multivariate Features of Developers

Abstract

Software bug repair is a problem that cannot be ignored in the process of software life.How to efficiently assign software bugs automatically is a very important research direction.Now,the existing research methods mainly focus on the bugreport’s text content or the low-level information of the developers’ tossing network,while ignoring the high-level topology information in the tossing network.Therefore,this paper proposes a software bug automatic assignment model MFD-GCN based on the developers’ multivariate features.Model fully considers the high-level topological features in the developers’ tossing network,and uses the powerful network feature extraction capabilities of graph convolution network to fully mine the multivariate features that represent developers’ deep cooperation relationship and fixing preferences,and train the classifier together with the bug text features.The proposed method is evaluated on two large open-source software projects,i.e.,Eclipse and Mozilla.Expe-rimental results show that compared with the mainstream bug-assignment methods proposed in recent years,the MFD-GCN mo-del has achieved state-of-art results in recommending the top K developers.The accuracy rate of top-1 recommendation on the Eclipse and Mozilla project reaches 69.8% and 59.7%,respectively.

Keywords

• automatic assignment|bug report|developers’ tossing network|graph convolution network|multivariate feature