Abstract Code review is a well-established and valued practice in the software engineering community contributing to both code quality and interpersonal benefits. However, there are challenges in both tools and processes that give rise to misalignments and frustrations. Recent research seeks to address this by automating code review entirely, but we believe that this risks losing the majority of the interpersonal benefits such as knowledge transfer and shared ownership. We believe that by better understanding the cognitive processes involved in code review, it would be possible to improve tool support, with or without AI, and make code review both more efficient, more enjoyable, while increasing or maintaining all of its benefits. In this paper, we conduct an ethnographic think-aloud study involving 10 participants and 34 code reviews. We build a cognitive model of code review bottom up through thematic, statistical, temporal, and sequential analysis of the transcribed material. Through the data, the similarities between the cognitive process in code review and decision-making processes, especially recognition-primed decision-making, become apparent. The result is the Code Review as Decision-Making ( CRDM ) model that shows how the developers move through two phases during the code review; first an orientation phase to establish context and rationale and then an analytical phase to understand, assess, and plan the rest of the review. Throughout the process several decisions must be taken, on writing comments, finding more information, voting, running the code locally, verifying continuous integration results, etc. Analysis software and process-coded data publicly available at: https://doi.org/10.5281/zenodo.15758266

Gaze-control and gaze-assistance in software development tools have so far been explored in the setting of code editing, but other developer activities like code review could also benefit from this kind of tool support. In this paper, we present GANDER, a platform for user studies on gaze-assisted code review. As a proof of concept, we extend the platform with an assistant that highlights name relationships in the code under review based on gaze behavior, and we perform a user study with 7 participants. While the participants experience the interaction as overwhelming and lacking explicit actions (seen in other similar user studies), the study demonstrates the platform’s capability for mobility, real-time gaze interaction, data logging, replay and analysis.