Empirically revisiting and enhancing automatic classification of bug and non-bug issues

Abstract

A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems (ITSs). Although the existing approaches have shown promising performance, the different design choices, including the different textual fields, feature representation methods and machine learning algorithms adopted by existing approaches, have not been comprehensively compared and analyzed. To fill this gap, we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification approaches. Our experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification, including: (1) Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification; (2) Word embedding with Long Short-Term Memory (LSTM) can better extract features from the textual fields in the issues, and hence, lead to better issue classification models; (3) There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues; (4) The performance of the issue classification model is not sensitive to the choices of ML algorithms. Based on our study outcomes, we further propose an advanced issue classification approach, DEEPLABEL, which can achieve better performance compared with the existing issue classification approaches.

Graphical abstract

Keywords

issue tracking / issue type prediction / empirical study

Zhong Li holds a BS in computer science and technology from Nanjing University of Posts and Telecommunications, China. He is currently a PhD student in Department of Computer Science and Technology, Nanjing University, China. His main research interests lie in intelligent software engineering

Minxue Pan received the BS and PhD degrees in computer science and technology from Nanjing University, China. He is an associate professor with the State Key Laboratory for Novel Software Technology and the Software Institute of Nanjing University, China. His research interests include software modeling and verification, software analysis and testing, cyber-physical systems, mobile computing, and intelligent software engineering

Yu Pei is an assistant professor with the Department of Computing, The Hong Kong Polytechnic University, China. His main research interests include automated program repair, software fault localization, and automated software testing

Tian Zhang received the PhD degree from Nanjing University, China. He is a professor with Nanjing University, China. His research interests include model driven aspects of software engineering, with the aim of facilitating the rapid and reliable development and maintenance of both large and small software systems

Linzhang Wang received the PhD degree from Nanjing University, China in 2005. He is currently a full professor with the State Key Laboratory of Novel Software Technology at Nanjing University, China. His research interests include software engineering, software testing, and software security

Xuandong Li received the BS, MS and PhD degrees from Nanjing University, China in 1985, 1991 and 1994, respectively. He is a full professor in Department of Computer Science and Technology, Nanjing University, China. His research interests include formal support for design and analysis of reactive, distributed, real-time, hybrid, and cyber-physical systems, and software testing and verification

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Acknowledgements

We thank the anonymous reviewers for their valuable feedback. This research was supported by the National Natural Science Foundation of China (Grant No. 61972193), and the Program B for Outstanding PhD Candidate of Nanjing University.

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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