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AUGER: automatically generating review comments with pre-training models

Authors:

Chun ZuoAuthors Info & Claims

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

Pages 1009 - 1021

https://doi.org/10.1145/3540250.3549099

Published: 09 November 2022 Publication History

Abstract

Code review is one of the best practices as a powerful safeguard for software quality. In practice, senior or highly skilled reviewers inspect source code and provide constructive comments, consider- ing what authors may ignore, for example, some special cases. The collaborative validation between contributors results in code being highly qualified and less chance of bugs. However, since personal knowledge is limited and varies, the efficiency and effectiveness of code review practice are worthy of further improvement. In fact, it still takes a colossal and time-consuming effort to deliver useful review comments. This paper explores a synergy of multiple practical review comments to enhance code review and proposes AUGER (AUtomatically GEnerating Review comments): a review comments generator with pre-training models. We first collect empirical review data from 11 notable Java projects and construct a dataset of 10,882 code changes. By leveraging Text-to-Text Transfer Transformer (T5) models, the framework synthesizes valuable knowledge in the training stage and effectively outperforms baselines by 37.38% in ROUGE-L. 29% of our automatic review comments are considered useful according to prior studies. The inference generates just in 20 seconds and is also open to training further. Moreover, the performance also gets improved when thoroughly analyzed in case study.

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Index Terms

AUGER: automatically generating review comments with pre-training models

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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Published: 09 November 2022

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Strategy Priority Research Program of Chinese Academy of Sciences
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ESEC/FSE '22: 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

November 14 - 18, 2022

Singapore, Singapore

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Simões IVenson E(2024)Evaluating Source Code Quality with Large Language Models: a comparative studyProceedings of the XXIII Brazilian Symposium on Software Quality10.1145/3701625.3701650(103-113)Online publication date: 5-Nov-2024
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Vijayvergiya MSalawa MBudiselić IZheng DLamblin PIvanković MCarin JLewko MAndonov JPetrović GTarlow DManiatis PJust RAdams BZimmermann TOzkaya ILin DZhang J(2024)AI-Assisted Assessment of Coding Practices in Modern Code ReviewProceedings of the 1st ACM International Conference on AI-Powered Software10.1145/3664646.3665664(85-93)Online publication date: 10-Jul-2024
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