GitRev: An LLM-Based Gamification Framework for Modern Code Review Activities
Bibliographic record
Abstract
Modern code review (MCR) is recognized as an effective software quality assurance practice that is broadly adopted by open-source and commercial software projects. MCR is most effective when developers follow best practices, as it improves code quality, enhances knowledge transfer, increases team awareness and shares code ownership. However, prior work highlights that poor code review practices are common and often manifest in the form of low review participation and engagement, shallow review, and toxic communications. To address these issues, we introduce GitRev, a novel approach that applies gamification mechanisms to boost developer motivation and engagement. GitRev is built on top of a Large Language Model (LLM), used as a points-based reward system that leverages the code change context, and code review activities. We implement GitRev as a GitHub app with a web browser extension that consists of a client-side web browser extension that gamifies the GitHub user interface, and a server-side composed of a Node.js server for authentication and data management. To evaluate GitRev, we conduct a controlled experiment with 86 graduate and undergraduate students. Results indicate the promising potential of our approach for improving the code review process and developers' engagement. GitRev is publicly available at https://anonymous.40pen.science/r/GitRev-OB74
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".