Action-Driven Consistency for Modular Multi-Language Systems with Perspectives
Bibliographic record
Abstract
Model-driven engineering advocates the use of different modelling languages and multiple views to describe the characteristics of a complex system. This allows to express a specific system characteristic with the most appropriate modelling language. However, establishing the conceptual relationships between elements from different languages and then consistently maintaining the links between model elements are non-trivial tasks. In this paper, we propose Action-Driven Consistency (ADC) for maintaining the links between different model elements from different languages defined with the Perspectives for Multi-Language Systems (PML) framework. PML aims to promote modularity in language reuse, inter-language consistency, and combination of languages. A perspective groups different languages, each playing a role for a common modelling purpose. PML defines perspective actions based on existing language actions to maintain consistent models. In this work, we present generic templates from which perspective actions can be generated given relationships between language metaclasses. This allows the perspective designer to focus on these key relationships and frees her from the error-prone implementation of perspective actions. We illustrate our approach with a perspective that combines class diagram and use case diagram languages for the purpose of requirement elicitation and apply it to a bank application.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".