MaRV: A Manually Validated Refactoring Dataset
Bibliographic record
Abstract
Despite the existence of traditional refactoring tools that offer semi-automated assistance, machine learning-based models have shown significant potential to generate refactored code. A comprehensive, manually validated refactoring dataset could help the software engineering community to train such models for effective refactorings. However, the community lacks a manually validated refactoring dataset. This paper introduces the MaRV dataset containing 693 manually evaluated code pairs extracted out of 126 GitHub Java repositories, representing four types of refactoring. In addition, the metadata describing the supposedly refactored elements was collected. Each code pair was manually evaluated by two reviewers out of 40 participants. MaRV dataset is constantly evolving with a web-based tool available for evaluating refactoring representations. The potential application of this dataset is to improve the accuracy and reliability of state-of-the-art models in refactoring tasks (e.g., refactoring candidate identification and refactoring code generation) by providing high-quality data.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".