Flexible verification of user-defined semantic constraints in modelling tools
Bibliographic record
Abstract
Many modelling tools embed verification rules that are checked against user-defined models to ensure they satisfy the static semantic constraints of the modelling language. However, there are many other contexts where required constraints vary with the intended purpose of the model, and not just the modelling language used. In this paper, we propose a flexible and practical approach for users to define, select, store, group, exchange, enable, and verify custom semantic constraints on metamodels with the Object Constraint Language. We illustrate the benefits of this approach with extensions to an Eclipse-based modelling tool, called jUCMNav, and applications to various contexts such as style compliance, analysis, and transformations that involve chains of tools. We believe this approach to be easily adaptable to other Eclipse-based modelling tools, which could then enjoy similar benefits.
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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 | 0.045 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".