Automatic Refactoring Candidate Identification Leveraging Effective Code Representation
Bibliographic record
Abstract
The use of machine learning to automate the detection of refactoring candidates is a rapidly evolving research area. The majority of work in this direction uses source code metrics and commit messages to predict refactoring candidates and do not exploit the rich semantics of source code. This paper proposes a new approach for extract method refactoring candidates identification. First, we propose a novel mechanism to identify negative samples for the refactoring candidate identification task. We then employ a self-supervised autoencoder to acquire a compact representation of source code generated by a pre-trained large language model. Subsequently, we train a binary classifier to predict extract method refactoring candidates. Experiments show that our new approach outperforms the state of the art by 30% in terms of F1 score. The proposed work has implications for researchers and practitioners. Software developers may use the proposed automated approach to predict refactoring candidates better. This study will facilitate the development of improved refactoring candidate identification methods that the researchers in the field could use and extend.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".