A Constraint Programming Approach to Conflict-Aware Optimal Scheduling of Prioritized Code Clone Refactoring
Bibliographic record
Abstract
Duplicated code, also known as code clones, are one of the malicious ‘code smells' that often need to be removed through refactoring for enhancing maintainability. Among all the potential refactoring opportunities, the choice and order of a set of refactoring activities may have distinguishable effect on the design/code quality. Moreover, there may be dependencies and conflicts among those refactorings. The organization may also impose priorities on certain refactoring activities. Addressing all these conflicts, priorities, and dependencies, manual formulation of an optimal refactoring schedule is very expensive, if not impossible. Therefore, an automated refactoring scheduler is necessary, which will maximize benefit and minimize refactoring effort. In this paper, we present a refactoring effort model, and propose a constraint programming approach for conflict-aware optimal scheduling of code clone refactoring.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 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".