Bibliographic record
Abstract
Extension is a common term used in model-driven engineering. However, it is expressed in different ways by different modeling languages. A class diagram modeler uses extension to mean inheriting from a class. An aspect modeler extends a base with an aspect. Despite the different ways the term is being expressed, it generally refers to adding/changing structure/behaviour of a model in some way. We observe that model extensions vary in several ways. For example, in some cases, such as in use case diagram extensions, the extended model (base model) is information complete, meaning that it requires no further information in order for it to be useful. In other languages, the base model is not useful on its own and must be completed with an extension. Extensions also vary in terms of granularity, e.g., inheritance between two classes is an extension at a low level of granularity (between two elements, i.e., classes, of the same class diagram) compared to extension between two models. This paper presents a feature model for extensions in modeling languages. We discuss how extensions vary in terms of granularity, the completeness of the base model, whether or not the extension model requires to specify matching information, and the changes the composed model does to the base. Using this feature model, we explore extensions in several popular modeling languages and report our findings.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".