Conflict‐aware optimal scheduling of prioritised code clone refactoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Duplicated or similar source code, also known as code clones, are possible malicious ‘code smells’ that may need to be removed through refactoring to enhance maintainability. Among many potential refactoring opportunities, the choice and order of a set of refactoring activities may have distinguishable effect on the design/code quality measured in terms of software metrics. Moreover, there may be dependencies and conflicts among those refactorings of different priorities. Addressing all the conflicts, priorities and dependencies, a manual formulation of an optimal refactoring schedule is very expensive, if not impossible. Therefore an automated refactoring scheduler is necessary to ‘maximise benefit and minimise refactoring effort’. However, the estimation of the efforts required to perform code clone refactoring is a challenging task. This study makes two contributions. First, the authors propose an effort model for the estimation of code clone refactoring efforts. Second, the authors propose a constraint programming (CP) approach for conflict‐aware optimal scheduling of code clone refactoring. A qualitative evaluation of the effort model from the developers’ perspective suggests that the model is complete and useful for code clone refactoring effort estimation. The authors also quantitatively compared their refactoring scheduler with other well‐known scheduling techniques such as the genetic algorithm, greedy approaches and linear programming. The authors’ empirical study suggests that the proposed CP‐based approach outperforms other approaches they considered.
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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 | 0.001 |
| 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 it