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Learning Human-Written Commit Messages to Document Code Changes

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Abstract

Commit messages are important complementary information used in understanding code changes. To address message scarcity, some work is proposed for automatically generating commit messages. However, most of these approaches focus on generating summary of the changed software entities at the superficial level, without considering the intent behind the code changes (e.g., the existing approaches cannot generate such message: “fixing null pointer exception”). Considering developers often describe the intent behind the code change when writing the messages, we propose ChangeDoc, an approach to reuse existing messages in version control systems for automatical commit message generation. Our approach includes syntax, semantic, pre-syntax, and pre-semantic similarities. For a given commit without messages, it is able to discover its most similar past commit from a large commit repository, and recommend its message as the message of the given commit. Our repository contains half a million commits that were collected from SourceForge. We evaluate our approach on the commits from 10 projects. The results show that 21.5% of the recommended messages by ChangeDoc can be directly used without modification, and 62.8% require minor modifications. In order to evaluate the quality of the commit messages recommended by ChangeDoc, we performed two empirical studies involving a total of 40 participants (10 professional developers and 30 students). The results indicate that the recommended messages are very good approximations of the ones written by developers and often include important intent information that is not included in the messages generated by other tools.

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Author information

Authors and Affiliations

  1. National Engineering Research Center of Digital Life, School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China

    Yuan Huang, Hao-Jie Zhou & Zi-Bin Zheng

  2. School of Information Engineering, Hebei GEO University, Shijiazhuang, 050031, China

    Nan Jia

  3. Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion, School of Communication and Design, Sun Yat-sen University, Guangzhou, 510006, China

    Xiang-Ping Chen

  4. School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, 510006, China

    Ming-Dong Tang

  5. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China

    Ming-Dong Tang

Authors
  1. Yuan Huang
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  2. Nan Jia
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  4. Xiang-Ping Chen
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  5. Zi-Bin Zheng
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  6. Ming-Dong Tang
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Corresponding author

Correspondence to Xiang-Ping Chen.

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Huang, Y., Jia, N., Zhou, HJ. et al. Learning Human-Written Commit Messages to Document Code Changes. J. Comput. Sci. Technol. 35, 1258–1277 (2020). https://doi.org/10.1007/s11390-020-0496-0

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  • DOI: https://doi.org/10.1007/s11390-020-0496-0

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