short-paper

Open access

Where should I comment my code?: a dataset and model for predicting locations that need comments

Authors:

Charles SuttonAuthors Info & Claims

ICSE-NIER '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: New Ideas and Emerging Results

Pages 21 - 24

https://doi.org/10.1145/3377816.3381736

Published: 18 September 2020 Publication History

PDF eReader

Abstract

Programmers should write code comments, but not on every line of code. We have created a machine learning model that suggests locations where a programmer should write a code comment. We trained it on existing commented code to learn locations that are chosen by developers. Once trained, the model can predict locations in new code. Our models achieved precision of 74% and recall of 13% in identifying comment-worthy locations. This first success opens the door to future work, both in the new where-to-comment problem and in guiding comment generation. Our code and data is available at http://groups.inf.ed.ac.uk/cup/comment-locator/.

References

[1]

A. Blasi, A. Goffi, K. Kuznetsov, A. Gorla, M. D. Ernst, M. Pezzè, and S. D. Castellanos. 2018. Translating code comments to procedure specifications. In ISSTA. 242---253.

Google Scholar

[2]

R. P. Buse and W. R. Weimer. 2010. Automatically documenting program changes. In ASE. 33---42.

Google Scholar

[3]

M. D. Ernst. 2017. Natural language is a programming language: Applying natural language processing to software development. In SNAPL. 4:1---4:14.

Google Scholar

[4]

M. Fowler. 2000. Refactoring: Improving the Design of Existing Code. Addison-Wesley.

Digital Library

Google Scholar

[5]

A. Goffi, A. Gorla, M. D. Ernst, and M. Pezze. 2016. Automatic generation of oracles for exceptional behaviors. In ISSTA. 213---224.

Google Scholar

[6]

X. Hu, G. Li, X. Xia, D. Lo, and Z. Jin. 2018. Deep code comment generation. In ICPC. 200---210.

Google Scholar

[7]

A. Louis, S. K. Dash, E. T. Barr, and C. Sutton. 2018. Deep Learning to Detect Redundant Method Comments. http://arxiv.org/abs/1806.04616.

Google Scholar

[8]

S. McConnell. 2004. Code complete: A practical handbook of software construction (2nd ed.). Microsoft Press, Redmond, WA, USA.

Google Scholar

[9]

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS. 3111---3119.

Google Scholar

[10]

M. Motwani and Y. Brun. 2019. Automatically Generating Precise Oracles from Structured Natural Language Specifications. In ICSE. Montreal, Canada, 188---199.

Google Scholar

[11]

D. Movshovitz-Attias and W. W. Cohen. 2013. Natural language models for predicting programming comments. In ACL. 35---40.

Google Scholar

[12]

L. Tan, D. Yuan, G. Krishna, and Y. Zhou. 2007. /*iComment: Bugs or Bad Comments?*/. In SOSP. 145---158.

Google Scholar

[13]

L. Tan, Y. Zhou, and Y. Padioleau. 2011. aComment: Mining annotations from comments and code to detect interrupt related concurrency bugs. In ICSE. 11---20.

Google Scholar

[14]

S. H. Tan, D. Marinov, L. Tan, and G. T. Leavens. 2012. @tComment: Testing Javadoc Comments to Detect Comment-Code Inconsistencies. In ICST. Montreal, Canada, 260---269.

Google Scholar

Cited By

View all

Jabrayilzade EYurtoğlu ATüzün E(2024)Taxonomy of inline code comment smellsEmpirical Software Engineering10.1007/s10664-023-10425-529:3Online publication date: 3-Apr-2024
https://doi.org/10.1007/s10664-023-10425-5
Huang YChen YChen XZhou X(2024)Are your comments outdated? Toward automatically detecting code‐comment consistencyJournal of Software: Evolution and Process10.1002/smr.2718Online publication date: 27-Aug-2024
https://doi.org/10.1002/smr.2718
Yao KOliva GHassan AAsaduzzaman MMalton AWalenstein A(2023)Finding associations between natural and computer languagesJournal of Systems and Software10.1016/j.jss.2023.111651201:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111651

Index Terms

Where should I comment my code?: a dataset and model for predicting locations that need comments
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. 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. Maintaining software

Recommendations

A Survey on Research of Code Comment
ICMSS 2019: Proceedings of the 2019 3rd International Conference on Management Engineering, Software Engineering and Service Sciences

Code comments are one of the effective means for assisting programmers to understand the source code. High-quality code comments play an important role in areas such as software maintenance and software reuse. Good code comments can help programmers ...
A study of comment abstraction, coupling, and placement
SEA '07: Proceedings of the 11th IASTED International Conference on Software Engineering and Applications

Some program comprehension techniques involve analyzing comments and identifiers in software. Other techniques analyze the code itself. Biggerstaff et al. noted a difference in abstraction level between comments and code. Comments are domain-related, ...
Empirical Analysis of Fault-Proneness in Methods by Focusing on their Comment Lines
APSEC '14: Proceedings of the 2014 21st Asia-Pacific Software Engineering Conference - Volume 02

This paper focuses on comments described in Java programs, and conducts an empirical analysis about relationships between comments and fault-proneness in the programs. The types of comments analyzed in this paper are comments described inside a method ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ICSE-NIER '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: New Ideas and Emerging Results

June 2020

128 pages

ISBN:9781450371261

DOI:10.1145/3377816

General Chairs:
Gregg Rothermel
North Carolina State University
,
Doo-Hwan Bae
KAIST, South Korea

This work is licensed under a Creative Commons Attribution International 4.0 License.

In-Cooperation

KIISE: Korean Institute of Information Scientists and Engineers
IEEE CS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 September 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

Engineering and Physical Sciences Research Council

Conference

ICSE '20

Sponsor:

SIGSOFT

ICSE '20: 42nd International Conference on Software Engineering

June 27 - July 19, 2020

Seoul, South Korea

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
673
Total Downloads

Downloads (Last 12 months)132
Downloads (Last 6 weeks)13

Reflects downloads up to 02 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Jabrayilzade EYurtoğlu ATüzün E(2024)Taxonomy of inline code comment smellsEmpirical Software Engineering10.1007/s10664-023-10425-529:3Online publication date: 3-Apr-2024
https://doi.org/10.1007/s10664-023-10425-5
Huang YChen YChen XZhou X(2024)Are your comments outdated? Toward automatically detecting code‐comment consistencyJournal of Software: Evolution and Process10.1002/smr.2718Online publication date: 27-Aug-2024
https://doi.org/10.1002/smr.2718
Yao KOliva GHassan AAsaduzzaman MMalton AWalenstein A(2023)Finding associations between natural and computer languagesJournal of Systems and Software10.1016/j.jss.2023.111651201:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111651
Chen QXia XHu HLo DLi S(2021)Why My Code Summarization Model Does Not WorkACM Transactions on Software Engineering and Methodology10.1145/343428030:2(1-29)Online publication date: 10-Feb-2021
https://dl.acm.org/doi/10.1145/3434280

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

A Survey on Research of Code Comment

A study of comment abstraction, coupling, and placement

Empirical Analysis of Fault-Proneness in Methods by Focusing on their Comment Lines