Proceedings of the 7th International Workshop on Software Clones
Bibliographic record
Abstract
Software clones are identical or similar pieces of code or design. Clones are known to be closely related to various issues on software engineering, such as software quality, complexity, architecture, refactoring, evolution, licensing, plagiarism, and so on. Various characteristics of software systems can be uncovered through clone analysis, and system restructuring can be performed by merging clones. Moreover, a clear understanding of real use cases in clone management is a fundamental prerequisite for categorizing, evaluating and directing future research in this area. For this reason, this IWSC will emphasize clone management in practice, that is, use cases and experiences with clones and clone management in the software life-cycle. The purpose of this workshop is to continue to solidify and give shape to this research/application area and community. More specifically, the goals are to bring together academic and industrial researchers and practitioners from around the world to evaluate the current state of research and applications, discuss common problems, discover new opportunities for collaboration, exchange ideas, and envision new areas of research and applications.We are very pleased that we are succeeding in this attempt. This workshop is the seventh issue, and for the fourth time, we are colocating with ICSE. This co-location helps us to reach new researchers and practitioners in related fields. I am particularly glad that we again managed to attract practitioners, given the special focus theme of this IWSC: clone management in practice, that is, use cases and experiences with clones and clone management in the software lifecycle.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.022 |
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".