Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
4-2024
Abstract
Modern code review is a critical quality assurance process that is widely adopted in both industry and open source software environments. This process can help newcomers learn from the feedback of experienced reviewers; however, it often brings a large workload and stress to reviewers. To alleviate this burden, the field of automated code reviews aims to automate the process, teaching large language models to provide reviews on submitted code, just as a human would. A recent approach pre-trained and fine-tuned the code intelligent language model on a large-scale code review corpus. However, such techniques did not fully utilise quality reviews amongst the training data. Indeed, reviewers with a higher level of experience or familiarity with the code will likely provide deeper insights than the others. In this study, we set out to investigate whether higher-quality reviews can be generated from automated code review models that are trained based on an experience-aware oversampling technique. Through our quantitative and qualitative evaluation, we find that experience-aware oversampling can increase the correctness, level of information, and meaningfulness of reviews generated by the current state-of-the-art model without introducing new data. The results suggest that a vast amount of high-quality reviews are underutilised with current training strategies. This work sheds light on resource-efficient ways to boost automated code review models.
Keywords
Code Review, Review Comments, Neural Machine Translation
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
MSR '24: Proceedings of the 21st International Conference on Mining Software Repositories, Lisbon, Portugal, April 15-16
First Page
278
Last Page
283
ISBN
9798400705878
Identifier
10.1145/3643991.3644910
Publisher
ACM
City or Country
New York
Citation
LIN, Hong Yi; THONGTANUNAM, Patanamon; TREUDE, Christoph; and CHAROENWET, Wachiraphan.
Improving automated code reviews: Learning from experience. (2024). MSR '24: Proceedings of the 21st International Conference on Mining Software Repositories, Lisbon, Portugal, April 15-16. 278-283.
Available at: https://ink.library.smu.edu.sg/sis_research/8882
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3643991.3644910