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Effectiveness of exploring historical commits for developer recommendation: an empirical study

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Abstract

Developer recommendation is an essential task for resolving incoming issues in the evolution of software. Many developer recommendation techniques have been developed in the literature; among these studies, most techniques usually combined historical commits as supplementary information with bug repositories and/or source-code repositories to recommend developers. However, the question of whether the messages in historical commits are always useful has not yet been answered. This article aims at solving this problem by conducting an empirical study on four open-source projects. The results show that: (1) the number of meaningful words of the commit description has an impact on the quality of the commit, and a larger number of meaningful words in the description means that it can generally better reflect developers’ expertise; (2) using commit description to recommend the relevant developers is better than that using relevant files that are recorded in historical commits; (3) developers tend to change the relevant files that they have changed many times before; (4) developers generally tend to change the files that they have changed recently.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61402396, 61611540347, and 61472344), the Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (CAAC-ITRB- 201704), the Open Funds of the State Key Laboratory for Novel Software Technology of Nanjing University (KFKT2016B21), the Jiangsu Qin Lan Project, the China Postdoctoral Science Foundation (2015M571489), and the Natural Science Foundation of Yangzhou City (YZ2017113). The authors would like to sincerely thank the anonymous reviewers who provided useful suggestions that helped to improve the article.

Author information

Authors and Affiliations

  1. School of Information Engineering, Yangzhou University, Yangzhou, 225127, China

    Xiaobing Sun, Hui Yang & Bin Li

  2. Information Technology Research Base of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin, 300300, China

    Xiaobing Sun

  3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Hareton Leung

  4. Coventry University, Coventry CVI 5FB, UK

    Hanchao Jerry Li

  5. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Xiaobing Sun

  6. Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Lingzhi Liao

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  1. Xiaobing Sun
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  4. Bin Li
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Corresponding author

Correspondence to Xiaobing Sun.

Additional information

Xiaobing Sun is an associate professor in the School of Information Engineering, Yangzhou University, China. He is a senior China Computer Federation (CCF) and ACM member. His current research interests include software analysis, testing, maintenance, and evolution.

Hui Yang is a master student in the School of Information Engineering, Yangzhou University, China. He is a CCF student member. His current research interests include recommendation systems for software maintenance, bug fixing, and developer recommendation.

Hareton Leung is the director of the Lab for Software Development and Management, The Hong Kong Polytechnic University, China. His current research interests include software testing, quality assurance, process and quality improvement.

Bin Li is a professor in the School of Information Engineering, Yangzhou University, China. He is a senior CCF member and ACM member. His current research interests include artificial intelligence, machine learning, and crowdsourcing computing.

Hanchao (Jerry) Li is a PhD student in Coventry University, UK. His current research interest is mathematical modeling and various modern computer technologies such as machine learning, information retrieval, and artificial intelligence.

Lingzhi Liao is a lecturer in Nanjing University of Information Science & Technology, China. Her current research interests include data mining, image processing, and pattern recognition.

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Sun, X., Yang, H., Leung, H. et al. Effectiveness of exploring historical commits for developer recommendation: an empirical study. Front. Comput. Sci. 12, 528–544 (2018). https://doi.org/10.1007/s11704-016-6023-3

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