Replication package for code review as decision-making
Gullstrand Heander, Lo LU
(2025)
- 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 out without AI, and make code review both more efficient, more enjoyable, while increasing or maintaining all of its benefits. In this... (More) - 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 out 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.
In this replication package, the process-coded data is available along with the Python software for doing temporal, statistical, and sequential analysis of codes and themes. Please check README.md for instructions on how to run the software. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/38612014-1d1c-44ca-aba4-5f7ffb9bfec8
- author
- Gullstrand Heander, Lo
LU
- organization
- publishing date
- 2025-06-27
- type
- Non-textual form
- publication status
- published
- subject
- DOI
- 10.5281/zenodo.15758266
- project
- How can code reviews be made fit-for-purpose?
- DAPPER: Seamless, Tailored Code Review
- language
- English
- LU publication?
- yes
- id
- 38612014-1d1c-44ca-aba4-5f7ffb9bfec8
- date added to LUP
- 2025-08-21 11:21:00
- date last changed
- 2025-08-21 16:12:40
@misc{38612014-1d1c-44ca-aba4-5f7ffb9bfec8,
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.<br/><br/>We believe that by better understanding the cognitive processes involved in code review, it would be possible to improve tool support, with out 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.<br/><br/>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.<br/><br/>In this replication package, the process-coded data is available along with the Python software for doing temporal, statistical, and sequential analysis of codes and themes. Please check README.md for instructions on how to run the software.}},
author = {{Gullstrand Heander, Lo}},
language = {{eng}},
month = {{06}},
title = {{Replication package for code review as decision-making}},
url = {{http://dx.doi.org/10.5281/zenodo.15758266}},
doi = {{10.5281/zenodo.15758266}},
year = {{2025}},
}