Broadcast vs. Unicast Review Technology: Does It Matter?
Bibliographic record
Abstract
Code review is the process of having other team members examine changes to a software system in order to evaluate their technical content and quality. Over the years, multiple tools have been proposed to help software developers conduct and manage code reviews. Some software organizations have been migrating from broadcast review technology to a more advanced unicast review approach such as Jira, but it is unclear if these unicast review technology leads to better code reviews. This paper empirically studies review data of five Apache projects that switched from broadcast based code review to unicast based, to understand the impact of review technology on review effectiveness and quality. Results suggest that broadcast based review is twice faster than review done with unicast based review technology. However, unicast's review quality seems to be better than that of the broadcast based. Our findings suggest that the medium (i.e., broadcast or unicast) technology used for code reviews can relate to the effectiveness and quality of reviews activities.
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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 | 0.122 | 0.545 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".