A Technique for Just-in-Time Clone Detection in Large Scale Systems
Bibliographic record
Abstract
Existing clone tracking tools have limited support for sharing clone information between developers in a large scale system. Developers are not notified when new clones are introduced by other developers or when existing clones are modified. We propose a client-server architecture that centrally detects and maintains clone information for an entire software system stored in a version control system. Clients retrieve a list of clones relevant to the code they are working on from the server. Whenever an update is committed to the version control system, the server detects and incrementally updates clone information. We propose techniques to improve the speed of the incremental clone detection. In order to reduce the number of comparisons required for clone detection, we select representative clones from the existing clone list. We build a string-based technique to compare the newly committed code with the representative clones and to update the clone list. In a case study, we show that our approach significantly reduces the clone detection time, while supporting clone detection across the entire software system.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".