Help Me to Understand this Commit! -- A Vision for Contextualized Code Reviews
Michael Unterkalmsteiner, Deepika Badampudi, Ricardo Britto, Nauman bin Ali
TL;DR
The paper identifies a gap in modern code reviews: reviewers need context-rich, aggregated information beyond line-by-line diffs. Through a mapping study of 53 works, the authors categorize existing support into four areas—refactoring, change impact, code reorganization, and data integration—and propose Code Review Contextualizer (CoReCo), a vision for an environment that delivers on-demand, context-aware information aligned with global and local review contexts. CoReCo would build unit-specific profiles, track reviewer expertise and interactions, integrate diverse artifacts, and present synthesized data to support efficient decision making. The work outlines practical application scenarios and three research avenues—data infrastructure, data integration/visualization, and decision-making frameworks—to advance toward production-ready, context-rich code review tools with potential improvements in developer experience and code quality.
Abstract
Background: Modern Code Review (MCR) is a key component for delivering high-quality software and sharing knowledge among developers. Effective reviews require an in-depth understanding of the code and demand from the reviewers to contextualize the change from different perspectives. Aim: While there is a plethora of research on solutions that support developers to understand changed code, we have observed that many provide only narrow, specialized insights and very few aggregate information in a meaningful manner. Therefore, we aim to provide a vision of improving code understanding in MCR. Method: We classified 53 research papers suggesting proposals to improve MCR code understanding. We use this classification, the needs expressed by code reviewers from previous research, and the information we have not found in the literature for extrapolation. Results: We identified four major types of support systems and suggest an environment for contextualized code reviews. Furthermore, we illustrate with a set of scenarios how such an environment would improve the effectiveness of code reviews. Conclusions: Current research focuses mostly on providing narrow support for developers. We outline a vision for how MCR can be improved by using context and reducing the cognitive load on developers. We hope our vision can foster future advancements in development environments.