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)
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".