Bibliographic record
Abstract
Code duplication, also known as software clones, is a persistent problem in software systems that is usually associated with error-proneness and poor software maintainability. Despite the fact that clone detection is a mature research field, clone refactoring has not been equally investigated. Clone refactoring requires the unification and merging of duplicated code, which is a challenging problem because of the changes that take place on the initial clones after their introduction. \n \nIn recent years, more research works attempted to address the challenges around clone refactoring by applying different techniques; however, they suffer from poor accuracy or performance issues, especially for large clone groups containing more than two clone instances. We contribute to this field by proposing an automated approach that a) finds refactorable subgroups (consisting of three clones or more) within the original group of clones, b) finds the statements that to be merged and extracted in a fast yet accurate way, and c) assesses the refactorability of clone subgroups. \n \nWe evaluated our approach in comparison to the state-of-the-art, and the results show that we have a high accuracy in matching the clone statements, while maintaining high performance. In a case study, where we carefully examined all clone groups in project JFreeChart 1.0.10, we found that around 49% of the 98 clone subgroups are actually refactorable. Finally, we conducted a large-scale study on over 44k clone groups (13.6k groups containing 3 clones or more) detected by four clone detection tools in nine open source projects to assess the refactorability for clone groups. The outcome of this study revealed the presence of 2,833 refactorable clone subgroups that contain in total 13,398 clone instances.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".