Leveraging Reviewer Experience in Code Review Comment Generation
Bibliographic record
Abstract
Leveraging Reviewer Experience in Code Review Comment Generation (Replication Package) This is the replication package for journal paper "Leveraging Reviewer Experience in Code Review Comment Generation". Authors Hong Yi Lin (University of Melbourne) Patanamon Thongtanunam (University of Melbourne) Christoph Treude (Singapore Management University) Michael Godfrey (University of Waterloo) Chunhua Liu (University of Melbourne) Wachiraphan Charoenwet (University of Melbourne) List of Contents Manual_Evaluation Accuracy annotations Informativeness annotations Comment category annotations Manual annotation guidelines 100 random samples ELF_AVG Model checkpoints for ELF_AVG strategy (Repository, Subsystem, Package) ELF_ACO Model checkpoints for ELF_ACO strategy (Repository, Subsystem, Package) ELF_MAX Model checkpoints for ELF_MAX strategy (Repository, Subsystem, Package) ELF_RSO Model checkpoints for ELF_RSO strategy (Repository, Subsystem, Package) Oversampling Model checkpoints for Experience-Aware Oversampling (Repository) CodeReviewer Model checkpoints for the original CodeReviewer (Fine-tuned on our dataset) Predictions All model generated predictions for test set Repository_History Pull request and commit histories for all repositories in training, validation and test set Code_Review_Dataset_Tagged Cleaned training, validation and test set including tagged ownership ratios Top10_B4_Delta Top 10 generations for each ELF model vs CodeReviewer in terms of BLEU-4 ELF_Code Fine-tuning and testing scripts for ELF (Adapted from CodeReviewer)
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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 | 0.180 | 0.637 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.036 |
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".