An automatic framework for extracting and classifying near-miss clone genealogies
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
Extracting code clone genealogies across multiple versions of a program and classifying them according to their change patterns underlies the study of code clone evolution. While there are a few studies in the area, the approaches do not handle near-miss clones well and the associated tools are often computationally expensive. To address these limitations, we present a framework for automatically extracting both exact and near-miss clone genealogies across multiple versions of a program and for identifying their change patterns using a few key similarity factors. We have developed a prototype clone genealogy extractor, applied it to three open source projects including the Linux Kernel, and evaluated its accuracy in terms of precision and recall. Our experience shows that the prototype is scalable, adaptable to different clone detection tools, and can automatically identify evolution patterns of both exact and near-miss clones by constructing their genealogies.
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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 | 0.000 |
| 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