research-article

Deep code comment generation

Authors:

Zhi JinAuthors Info & Claims

ICPC '18: Proceedings of the 26th Conference on Program Comprehension

Pages 200 - 210

https://doi.org/10.1145/3196321.3196334

Published: 28 May 2018 Publication History

Abstract

During software maintenance, code comments help developers comprehend programs and reduce additional time spent on reading and navigating source code. Unfortunately, these comments are often mismatched, missing or outdated in the software projects. Developers have to infer the functionality from the source code. This paper proposes a new approach named DeepCom to automatically generate code comments for Java methods. The generated comments aim to help developers understand the functionality of Java methods. DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features. We use a deep neural network that analyzes structural information of Java methods for better comments generation. We conduct experiments on a large-scale Java corpus built from 9,714 open source projects from GitHub. We evaluate the experimental results on a machine translation metric. Experimental results demonstrate that our method DeepCom outperforms the state-of-the-art by a substantial margin.

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Cited By

Liang YFan GYu HLi MHuang Z(2025)Automatic Code Summarization Using Abbreviation Expansion and Subword SegmentationExpert Systems10.1111/exsy.1383542:2Online publication date: 8-Jan-2025
https://doi.org/10.1111/exsy.13835
Chen XChen JLian ZHuang YZhou XWu YZheng Z(2025)An alternative to code comment generation? Generating comment from bytecodeInformation and Software Technology10.1016/j.infsof.2024.107623179(107623)Online publication date: Mar-2025
https://doi.org/10.1016/j.infsof.2024.107623
Vu TBui TDo TNguyen TVo HNguyen S(2025)Automated description generation for software patchesInformation and Software Technology10.1016/j.infsof.2024.107543177(107543)Online publication date: Jan-2025
https://doi.org/10.1016/j.infsof.2024.107543
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Index Terms

Deep code comment generation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Documentation

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Published In

cover image ACM Conferences

ICPC '18: Proceedings of the 26th Conference on Program Comprehension

May 2018

423 pages

ISBN:9781450357142

DOI:10.1145/3196321

General Chair:
Foutse Khomh
École Polytechnique de Montréal, Canada
,
Program Chairs:
Chanchal K. Roy
University of Saskatchewan, Canada
,
Janet Siegmund
University of Passau, Germany

Copyright © 2018 ACM.

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SIGSOFT: ACM Special Interest Group on Software Engineering
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 May 2018

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National Natural Science Foundation of China
National Basic Research Program of China (the 973 Program)

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ICSE '18

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SIGSOFT
IEEE-CS

ICSE '18: 40th International Conference on Software Engineering

May 28 - 29, 2018

Gothenburg, Sweden

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2025 IEEE/ACM 46th International Conference on Software Engineering

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Cited By

Liang YFan GYu HLi MHuang Z(2025)Automatic Code Summarization Using Abbreviation Expansion and Subword SegmentationExpert Systems10.1111/exsy.1383542:2Online publication date: 8-Jan-2025
https://doi.org/10.1111/exsy.13835
Chen XChen JLian ZHuang YZhou XWu YZheng Z(2025)An alternative to code comment generation? Generating comment from bytecodeInformation and Software Technology10.1016/j.infsof.2024.107623179(107623)Online publication date: Mar-2025
https://doi.org/10.1016/j.infsof.2024.107623
Vu TBui TDo TNguyen TVo HNguyen S(2025)Automated description generation for software patchesInformation and Software Technology10.1016/j.infsof.2024.107543177(107543)Online publication date: Jan-2025
https://doi.org/10.1016/j.infsof.2024.107543
Zhang KLi JLi ZJin ZLi G(2025)Transformer-based code model with compressed hierarchy representationEmpirical Software Engineering10.1007/s10664-025-10612-630:2Online publication date: 23-Jan-2025
https://doi.org/10.1007/s10664-025-10612-6
Ni AAllamanis MCohan ADeng YShi KSutton CYin PSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)NEXTProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693610(37929-37956)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693610
Fu HTan JZhang PLi FSun JSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)PinNetProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692636(14157-14174)Online publication date: 21-Jul-2024
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Shi YYin YYu MChu L(2024)CogCol: Code Graph-Based Contrastive Learning Model for Code SummarizationElectronics10.3390/electronics1310181613:10(1816)Online publication date: 8-May-2024
https://doi.org/10.3390/electronics13101816
Wang YZhao QXu DLiu XLarson K(2024)Purpose enhanced reasoning through iterative promptingProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/720(6513-6521)Online publication date: 3-Aug-2024
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Delmo ADespi JArpon P(2024)A 5 Year Bibliometric Review of Programming Language Research Dynamics in Southeast Asia (2018-2023)SSRN Electronic Journal10.2139/ssrn.4706647Online publication date: 2024
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Hou XZhao YLiu YYang ZWang KLi LLuo XLo DGrundy JWang H(2024)Large Language Models for Software Engineering: A Systematic Literature ReviewACM Transactions on Software Engineering and Methodology10.1145/369598833:8(1-79)Online publication date: 20-Sep-2024
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