Understanding broadcast based peer review on open source software projects
Bibliographic record
Abstract
Software peer review has proven to be a successful technique in open source software (OSS) development. In contrast to industry, where reviews are typically assigned to specific individuals, changes are broadcast to hundreds of potentially interested stakeholders. Despite concerns that reviews may be ignored, or that discussions will deadlock because too many uninformed stakeholders are involved, we find that this approach works well in practice. In this paper, we describe an empirical study to investigate the mechanisms and behaviours that developers use to find code changes they are competent to review. We also explore how stakeholders interact with one another during the review process. We manually examine hundreds of reviews across five high profile OSS projects. Our findings provide insights into the simple, community-wide techniques that developers use to effectively manage large quantities of reviews. The themes that emerge from our study are enriched and validated by interviewing long-serving core developers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.071 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".