PYREF: Refactoring Detection in Python Projects
Bibliographic record
Abstract
Refactoring, the process of improving the internal code structure of a software system without altering its external behavior, is widely applied during software development. Understanding how developers refactor source code can help gain better understanding of the software development process and the relationship between various versions of a system. Refactoring detection tools have been developed for many popular programming languages, such as Java (e.g., REFACTORINGMINER and REF-FINDER) but, quite surprisingly, this is not the case for Python, a widely used programming language.Inspired by REFACTORING MINER, we present PYREF, a tool that automatically detects method-level refactoring operations in Python projects. We evaluated PYREF against a manually built oracle and compared it with a PYTHON-ADAPTED REFACTOR-INGMINER, which converts Python program to Java and detects refactoring operations with REFACTORING MINER. Our results indicate that PYREF can achieve satisfactory precision and detect more refactorings than the current state-of-the-art.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".