Open source software peer review practices
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
Peer review is seen as an important quality assurance mechanism in both industrial development and the open source software (OSS) community. The techniques for performing inspections have been well studied in industry; in OSS development, peer reviews are less well understood. We examine the two peer review techniques used by the successful, mature Apache server project: review-then-commit and commit-then-review. Using archival records of email discussion and version control repositories, we construct a series of metrics that produces measures similar to those used in traditional inspection experiments. Specifically, we measure the frequency of review, the level of participation in reviews, the size of the artifact under review, the calendar time to perform a review, and the number of reviews that find defects. We provide a comparison of the two Apache review techniques as well as a comparison of Apache review to inspection in an industrial project. We conclude that Apache reviews can be described as (1) early, frequent reviews (2) of small, independent, complete contributions (3) conducted asynchronously by a potentially large, but actually small, group of self-selected experts (4) leading to an efficient and effective peer review technique.
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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