Bibliographic record
Abstract
Code review is an integral part of modern software development, where fellow developers critique the content, premise, and structure of code changes. Organizations like DellEMC have made considerable investment in code reviews, yet tracking the characteristics of feedback that code reviews provide (a primary product of the code reviewing process) is still a difficult process. To understand community and personal feedback trends, we perform a longitudinal study of 39,249 reviews that contain 248,695 review comments from a proprietary project that is developed by DellEMC. To investigate generalizability, we replicate our study on the OpenStackN ova project. Through an analysis guided by topic models, we observe that more context-specific, technical feedback is introduced as the studied projects and communities age and as the reviewers within those communities accrue experience. This suggests that communities are reaping a larger return on investment in code review as they grow accustomed to the practice and as reviewers hone their skills. The code review trends uncovered by our models present opportunities for enterprises to monitor reviewing tendencies and improve knowledge transfer and reviewer skills.
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.
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 | 0.079 | 0.487 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".