Controlling Meta-Model Extensibility in Model-Driven Engineering
Bibliographic record
Abstract
Model-driven engineering (MDE) considers the systematic use of models in software development. A model must be specified through a well-defined modeling language with precise syntax and semantics. In MDE, this syntax is defined by a meta-model. While meta-models tend to be fixed, there are several scenarios that require the customization of existing meta-models. For example, standards of the object management group (OMG) like the knowledge discovery meta-model (KDM) or the diagram definition (DD) are based on the extension of base meta-models according to certain rules. However, these rules are not “operational”but are described in natural language and therefore not supported by tools. Although modeling is an activity regulated by meta-models, currently there are no commonly accepted mechanisms to regulate how meta-models can be extended. Hence, in order to solve this problem, we propose a mechanism that allows specifying customization and extension rules for meta-models, as well as a tool that makes it possible to customize the meta-models according to such rules. The tool is based on the Eclipse modeling framework, has been implemented as an Eclipse plugin, and has been validated to guide the extension of OMG standard meta-models, such as KDM and DD.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".