Inferring Metamodel Relaxations Based on Structural Patterns to Support Model Families
Bibliographic record
Abstract
A model family is a set of related models in a given language that results from the evolution of models over time and/or variations over the space (product) dimension. To enable a more efficient analysis of family members, all at once, we have already proposed union models to capture the union of all elements in all family members, in a compact and exact manner. However, despite having each model in a model family conforming to the same metamodel, there is still no guarantee that their union model will conform to the original metamodel of the family members. This paper aims to support the representation of union models (as valid instances of a metamodel) by inferring, from the structure of the original metamodel, a relaxed metamodel to which a union model conforms. In particular, instead of relaxing all metamodel constraints, the paper contributes a heuristic method that relaxes particular constraints (related only to multiplicities of attributes and association ends) by inferring where such relaxations are needed in the metamodel. To infer relaxation points, structural patterns are first identified in metamodels, then an evidence-based or an anticipation-based approach is applied to get the actual inference. The purpose behind inferring particular metamodel relaxation points is to be able to adapt the existing tools and analysis techniques once and minimally for all potential model families of a given modeling language.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".