The influence of non-technical factors on code review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When submitting a patch, the primary concerns of individual developers are “How can I maximize the chances of my patch being approved, and minimize the time it takes for this to happen?” In principle, code review is a transparent process that aims to assess qualities of the patch by their technical merits and in a timely manner; however, in practice the execution of this process can be affected by a variety of factors, some of which are external to the technical content of the patch itself. In this paper, we describe an empirical study of the code review process for WebKit, a large, open source project; we replicate the impact of previously studied factors - such as patch size, priority, and component and extend these studies by investigating organizational (the company) and personal dimensions (reviewer load and activity, patch writer experience) on code review response time and outcome. Our approach uses a reverse engineered model of the patch submission process and extracts key information from the issue tracking and code review systems. Our findings suggest that these nontechnical factors can significantly impact code review outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it