Automatic ranking of clones for refactoring through mining association rules
Bibliographic record
Abstract
In this paper, we present an in-depth empirical study on identifying clone fragments that can be important refactoring candidates. We mine association rules among clones in order to detect clone fragments that belong to the same clone class and have a tendency of changing together during software evolution. The idea is that if two or more clone fragments from the same class often change together (i.e., are likely to co-change) preserving their similarity, they might be important candidates for refactoring. Merging such clones into one (if possible) can potentially decrease future clone maintenance effort. We define a particular clone change pattern, the Similarity Preserving Change Pattern (SPCP), and consider the cloned fragments that changed according to this pattern (i.e., the SPCP clones) as important candidates for refactoring. For the purpose of our study, we implement a prototype tool called MARC that identifies SPCP clones and mines association rules among these. The rules as well as the SPCP clones are ranked for refactoring on the basis of their change-proneness. We applied MARC on thirteen subject systems and retrieved the refactoring candidates for three types of clones (Type 1, Type 2, and Type 3) separately. Our experimental results show that SPCP clones can be considered important candidates for refactoring. Clones that do not follow SPCP either evolve independently or are rarely changed. By considering SPCP clones for refactoring we not only can minimize refactoring effort considerably but also can reduce the possibility of delayed synchronizations among clones and thus, can minimize inconsistencies in software systems.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 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".