International Conference on Computer and Knowledge Engineering

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DevRanker: An Effective Approach to Rank Developers for Bug Report Assignment

Authors :

Mohammad Reza Kardoost¹ Mohammad Reza Moosavi² Reza Akbari³

1- Department of Computer Science, Engineering, and IT, Shiraz University, Shiraz, Iran 2- Department of Computer Science, Engineering, and IT, Shiraz University, Shiraz, Iran 3- Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran

Keywords :

Bug report،Bug assignment،Bug triage،Deep learning،Ranking developers

Abstract :

Bug assignment, which routes software projects' bug reports to the appropriate fixers, is an important part of software development and maintenance. Manual bug assignment is a time-consuming process that delays debugging. So various machine learning and information retrieval approaches have been used for automating the bug assigning process. However, Most previous deep learning-based studies have focused on developers assigned to bug reports and have not specifically considered developers' collaboration and interaction to resolve bug reports. In this paper, we present a new automatic bug assignment approach based on Bidirectional Encoder Representations from Transformers (BERT) and Preference Neural Network (PNN). First, we preprocess the textual data in the bug reports. Second, we use BERT as a word embedding technique to get vector representation of bug reports. Third, we calculate the developers’ suitability score based on different developers’ activity features for each bug report. Finally, PNN is used to rank developers for each bug report. Experiments are performed on open-source projects, namely Eclipse UI, Birt, JDT and SWT, and top-k accuracy is measured as an evaluation metric. The experimental results show that our approach can effectively improve the performance of automatic bug assignment.