Foundations of multi-paradigm modeling and simulation: computer automated multi-paradigm modelling: meta-modelling and graph transformation
Bibliographic record
Abstract
We present Computer Automated Multi-Paradigm Modelling (CAMPaM) (Mosterman and Vangheluwe 2002) for Model-Driven Development based on Meta-Modelling and Graph Transformation. The syntax of a class of models of interest is graphically meta-modelled in an appropriate formalism such as Entity-Relationship Diagrams. From this description of abstract syntax, augmented with concrete (visual) syntax information, an interactive, visual modelling environment is automatically generated. As the abstract syntax of models, irrespective of the formalism they are described in, is graph-like, graph rewriting can be used to perform model transformation. Graph Grammar models thus allow for model transformation specification. The Graph Grammar formalism can be meta-modelled in its own right and hence a visual environment for manipulating transformation models can also be automatically generated. Graph rewriting provides a rigourous basis for specifying and analyzing model transformations such as simplification, simulation, and code generation. In this article, we introduce AToM3, A Tool for Multi-formalism and Meta-Modelling. We present the meta-modelling and graph transformation concepts through a simple reactive system example: a Timed Automata model of a traffic light. Meta-modelling Timed Automata, generating the visual modelling environment, and modelling transformations as graph grammers, as well as executing them, are all performed in the AToM3 environment. The model transformations include simulation, transformation into Timed Transition Petri Nets, and code generation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".