We have all of the clones, now what? Toward integrating clone analysis into software quality assessment
Bibliographic record
Abstract
Cloning might seems to be an unconventional way of designing and developing software, yet it is very widely practised in industrial development. The cloning research community has made substantial progress on modeling, detecting, and analyzing software clones. Although there is continuing discussion on the real role of clones on software quality, our community may agree on the need for advancing clone management techniques. Current clone management techniques concentrate on providing editing tools that allow developers to easily inspect clone instances, track their evolution, and check change consistency. In this position paper, we argue that better clone management can be achieved by responding to the fundamental needs of industry practitioners. And the possible research directions include a software problem-oriented taxonomy of clones, and a better structured clone detection report. We believe this line of research should inspire new techniques, and reach to a much wider range of professionals from both the research and industry community.
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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 | 0.026 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.031 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".