A generic framework for model-set selection for the unification of testing and learning MDE tasks
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
We propose a generic framework for model-set selection for learning or testing Model-Driven Engineering tasks. We target specifically tasks that apply to or manipulate models, such as model definition, model well-formedness checking, and model transformation. In our framework, we view the model-set selection as a multi-objective optimization problem. The framework can be tailored to the learning or testing of a specific task by firstly expressing the coverage criterion, which will be encoded as a first optimization objective. The coverage is expressed by tagging the subset of the input metamodel that is relevant to the considered task. Then, one or more minimality criteria are selected as additional optimization objectives. We illustrate the use of our framework with the testing of metamodels. This case study shows that the multi-objective approach gives better results than random and mono-objective selections.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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