Bibliographic record
Abstract
Model Clone Detection is a growing area within the field of software model maintenance. New model clone detection techniques and tools for different types of models are being created, however, there is no clear way of objectively and quantitatively evaluating and comparing them. In this paper, we provide a synopsis of our work in devising and validating an evaluation framework that uses Mutation Analysis to provide such a facility. In order to demonstrate the framework's feasibility and also walk through its steps, we implement a framework implementation for evaluating Simulink model clone detectors. This includes a taxonomy of Simulink mutations, Simulink clone report transformations, and more. We outline how the framework calculates precision and recall, and do so on multiple Simulink model clone detectors. In addition, we also discuss areas of future work, including semantic clone mutations, and developing framework implementations for other model types, like UML. Lastly, we address some lessons we learned during the Ph.D. Process, such as partitioning the work into logical, self-contained, milestones, and being open and willing to engage in other research. We hope that our framework will help cultivate further research gains in Model Clone Detection.
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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 | 0.020 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".