Toward the Automatic Classification of Self-Affirmed Refactoring
Bibliographic record
Abstract
The concept of Self-Affirmed Refactoring (SAR) was introduced to explore how developers document their refactoring activities in commit messages, i.e., developers explicit documentation of refactoring operations intentionally introduced during a code change. In our previous study, we have manually identified refactoring patterns and defined three main common quality improvement categories including internal quality attributes, external quality attributes, and code smells, by only considering refactoring-related commits. However, this approach heavily depends on the manual inspection of commit messages. In this paper, we propose a two-step approach to first identify whether a commit describes developer-related refactoring events, then to classify it according to the refactoring common quality improvement categories. Specifically, we combine the N-Gram TF-IDF feature selection with binary and multiclass classifiers to build a new model to automate the classification of refactorings based on their quality improvement categories. We challenge our model using a total of 2,867 commit messages extracted from well engineered open-source Java projects. Our findings show that (1) our model is able to accurately classify SAR commits, outperforming the pattern-based and random classifier approaches, and allowing the discovery of 40 more relevent SAR patterns, and (2) our model reaches an F-measure of up to 90% even with a relatively small training dataset
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".