Support, Not Automation: Towards AI-supported Code Review For Code Quality and Beyond
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| Title: | Support, Not Automation: Towards AI-supported Code Review For Code Quality and Beyond |
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| Authors: | Lo Heander, Emma Söderberg, Christofer Rydenfält |
| Source: | Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering. :591-595 |
| Publisher Information: | ACM, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Code review, Multi-agent systems, Code review tools, Software Engineering, Human-in-the-loop, Large language models, Agentic systems |
| Description: | Code review is a well-established and valuable software development practice associated with code quality, interpersonal, and team benefits. However, it is also time-consuming, with developers spending 10–20% of their working time doing code reviews. With recent advances in AI capabilities, there are more and more initiatives aimed at fully automating code reviews to save time and streamline software developer workflows.However, while automated tools might succeed in maintaining the code quality, we risk losing interpersonal and team benefits such as knowledge transfer, shared code ownership, and team awareness. Instead of automating code review and losing these important benefits, we envision a code review platform where AI is used to support code review to increase benefits for both code quality and the development team.We propose an AI agent-based architecture that collects and combines information to support the user throughout the code review and adapt the workflow to their needs. We analyze this design in relation to the benefits of code review and outline a research agenda aimed at realizing the proposed design. |
| Document Type: | Article Conference object |
| File Description: | application/pdf |
| DOI: | 10.1145/3696630.3728505 |
| Access URL: | https://lup.lub.lu.se/record/c071364a-5617-4954-9b1c-84b11feb9d85 |
| Accession Number: | edsair.doi.dedup.....b1419e371a060cbda91946f94020b1f8 |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Code review is a well-established and valuable software development practice associated with code quality, interpersonal, and team benefits. However, it is also time-consuming, with developers spending 10–20% of their working time doing code reviews. With recent advances in AI capabilities, there are more and more initiatives aimed at fully automating code reviews to save time and streamline software developer workflows.However, while automated tools might succeed in maintaining the code quality, we risk losing interpersonal and team benefits such as knowledge transfer, shared code ownership, and team awareness. Instead of automating code review and losing these important benefits, we envision a code review platform where AI is used to support code review to increase benefits for both code quality and the development team.We propose an AI agent-based architecture that collects and combines information to support the user throughout the code review and adapt the workflow to their needs. We analyze this design in relation to the benefits of code review and outline a research agenda aimed at realizing the proposed design. |
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| DOI: | 10.1145/3696630.3728505 |