research-article

CommentFinder: a simpler, faster, more accurate code review comments recommendation

Authors:

Chakkrit Tantithamthavorn,

Patanamon Thongtanunam, and

Aldeida AletiAuthors Info & Claims

ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

November 2022

Pages 507 - 519

https://doi.org/10.1145/3540250.3549119

Published: 09 November 2022 Publication History

Abstract

Code review is an effective quality assurance practice, but can be labor-intensive since developers have to manually review the code and provide written feedback. Recently, a Deep Learning (DL)-based approach was introduced to automatically recommend code review comments based on changed methods. While the approach showed promising results, it requires expensive computational resource and time which limits its use in practice. To address this limitation, we propose CommentFinder – a retrieval-based approach to recommend code review comments. Through an empirical evaluation of 151,019 changed methods, we evaluate the effectiveness and efficiency of CommentFinder against the state-of-the-art approach. We find that when recommending the best-1 review comment candidate, our CommentFinder is 32% better than prior work in recommending the correct code review comment. In addition, CommentFinder is 49 times faster than the prior work. These findings highlight that our CommentFinder could help reviewers to reduce the manual efforts by recommending code review comments, while requiring less computational time.

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Index Terms

CommentFinder: a simpler, faster, more accurate code review comments recommendation

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

November 2022

1822 pages

ISBN:9781450394130

DOI:10.1145/3540250

General Chair:
Abhik Roychoudhury
National University of Singapore, Singapore
,
Program Chairs:
Cristian Cadar
Imperial College London, UK
,
Miryung Kim
University of California at Los Angeles, USA

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Olewicki DHabchi SAdams B(2024)An Empirical Study on Code Review Activity Prediction and Its Impact in PracticeProceedings of the ACM on Software Engineering10.1145/36608061:FSE(2238-2260)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660806
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