Toward more accurate developer recommendation via inference of development activities from interaction with bug repair process

Abstract

AbstractSoftware projects usually receive a large number of submitted bug reports every day. Manually triaging the bug reports is often time‐consuming and error‐prone; thus, it is necessary to automatically assign the bug reports to the suitable developers for bug repair, with the help of bug tracking systems. Aiming to reducing the time consumption and mismatch of bug report assignments, we present a developer recommendation model for bug repair based on weighted recurrent neural network, namely, DTPM, which contains two parts: One obtains multisource semantic information of bug reports and fuses them into high‐dimensional semantic feature vectors, and the other combines a penalty matrix into a single hidden layer neural network to obtain more reasonable developer recommendations. We conduct experiments on five datasets of open bug repositories (NetBeans, OpenOffice, GCC, Mozilla, and Eclipse), and the experimental results show that DTPM can achieve better performance than state‐of‐the‐art models LDA_KL, LDA_KL, LDA_SVM, DERTOM, DREX, and DeepTriage.