gCad: A Near-Miss Clone Genealogy Extractor to Support Clone Evolution Analysis
Bibliographic record
Abstract
Understanding the evolution of code clones is important for both developers and researchers to understand the maintenance implications of clones and to design robust clone management systems. Generally, a study of clone evolution starts with extracting clone genealogies across multiple versions of a program and classifying them according to their change patterns. Although these tasks are straightforward for exact clones, extracting the history of near-miss clones and classifying their change patterns automatically is challenging due to the potential diverse variety of clone fragments even in the same clone class. In this tool demonstration paper we describe the design and implementation of a near-miss clone genealogy extractor, gCad, that can extract and classify both exact and near-miss clone genealogies. Developers and researchers can compute a wide range of popular metrics regarding clone evolution by simply post processing the gCad results. gCad scales well to large subject systems, works for different granularities of clones, and adapts easily to popular clone detection tools.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".