Assessing the Refactorability of Software Clones
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
The presence of duplicated code in software systems is significant and several studies have shown that clones can be potentially harmful with respect to the maintainability and evolution of the source code. Despite the significance of the problem, there is still limited support for eliminating software clones through refactoring, because the unification and merging of duplicated code is a very challenging problem, especially when software clones have gone through several modifications after their initial introduction. In this work, we propose an approach for automatically assessing whether a pair of clones can be safely refactored without changing the behavior of the program. In particular, our approach examines if the differences present between the clones can be safely parameterized without causing any side-effects. The evaluation results have shown that the clones assessed as refactorable by our approach can be indeed refactored without causing any compile errors or test failures. Additionally, the computational cost of the proposed approach is negligible (less than a second) in the vast majority of the examined cases. Finally, we perform a large-scale empirical study on over a million clone pairs detected by four different clone detection tools in nine open-source projects to investigate how refactorability is affected by different clone properties and tool configuration options. Among the highlights of our conclusions, we found that (a) clones in production code tend to be more refactorable than clones in test code, (b) clones with a close relative location (i.e., same method, type, or file) tend to be more refactorable than clones in distant locations (i.e., same hierarchy, or unrelated types), (c) Type-1 clones tend to be more refactorable than the other clone types, and (d) clones with a small size tend to be more refactorable than clones with a larger size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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