DesignAware code review enhanced by learning from past Linux discussions
Benchmarking and Enhancing Design-Aware Code Review in the Linux Kernel
Software Engineering
Summary
Code reviews often need people to think about design choices, not just code errors. The authors studied how past review discussions in the Linux kernel can help with this harder task. They created DesignLore, a system that learns design decisions from historical reviews to guide new ones better. Their tests show that design-aware reviewing is still tough for AI tools, but their approach improved performance compared to others. This work helps advance tools that support understanding and checking software design in reviews.
What this means in practice
- •For open source maintainers: Use historical review data to inform and improve design discussions during code evaluation in large projects like the Linux kernel.
- •For software engineering tool developers: Enhance automated code review tools with design knowledge extraction methods to better detect design trade-offs in submitted code.
Authors
Zhenhao Zhou, Kai Yu, Ying Wang, Zhiqiang Yuan, Junhao Zeng, Yijian Wu, Chong Wang, Xin Peng
Abstract
Code review often requires reasoning beyond local correctness. In mature software systems, reviewers must recognize design trade-offs introduced by a proposed change, compare alternative solutions, and reason about their compatibility with project-specific constraints. We refer to this problem as design-aware code review. However, existing automated review approaches primarily focus on defects and correctness, while the design knowledge accumulated in historical review discussions remains difficult to reuse. To address these challenges, we present DesignLore, a knowledge-augmented approach that extracts design decisions from historical review discussions, organizes them into an adaptive design taxonomy, and reuses relevant historical knowledge to guide code review. We further construct DesignReviewBench, a benchmark derived from real patch-review histories for systematically evaluating design-aware code review. Our evaluation shows that design-aware review remains challenging for current coding agents, with the strongest baseline achieving an F1 of only 0.213 on design trade-off detection. In comparison, DesignLore achieves an F1 of 0.259, a 21.6% relative improvement, and consistently delivers gains across different agent systems and backbone. These results highlight the importance of design-aware review and demonstrate the potential of recovering and reusing historical design knowledge to support it.