Extracting code clones for refactoring using combinations of clone metrics
Bibliographic record
Abstract
Code clone detection tools may report a large number of code clones, while software developers are interested in only a subset of code clones that are relevant to software development tasks such as refactoring. Our research group has supported many software developers with the code clone detection tool CCFinder and its GUI front-end Gemini. Gemini shows clone sets (i.e., a set of code clones identical or similar to each other) with several clone metrics including their length and the number of code clones; however, it is not clear how to use those metrics to extract interesting code clones for developers. In this paper, we propose a method combining clone metrics to extract code clones for refactoring activity. We have conducted an empirical study on a web application developed by a Japanese software company. The result indicates that combinations of simple clone metric is more effective to extract refactoring candidates in detected code clones than individual clone metric.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".