A Tool-Supported Methodology for Validation and Refinement of Early-Stage Domain Models
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
Model-driven engineering (MDE) promotes automated model transformations along the entire development process. Guaranteeing the quality of early models is essential for a successful application of MDE techniques and related tool-supported model refinements. Do these models properly reflect the requirements elicited from the owners of the problem domain? Ultimately, this question needs to be asked to the domain experts. The problem is that a gap exists between the respective backgrounds of modeling experts and domain experts. MDE developers cannot show a model to the domain experts and simply ask them whether it is correct with respect to the requirements they had in mind. To facilitate their interaction and make such validation more systematic, we propose a methodology and a tool that derive a set of customizable questionnaires expressed in natural language from each model to be validated. Unexpected answers by domain experts help to identify those portions of the models requiring deeper attention. We illustrate the methodology and the current status of the developed tool MOTHIA, which can handle UML Use Case, Class, and Activity diagrams. We assess MOTHIA effectiveness in reducing the gap between domain and modeling experts, and in detecting modeling faults on the European Project CHOReOS.
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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 | 0.001 | 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