A Multi-Paradigm Approach for Modelling Service Interactions in Model-Driven Engineering Processes
Bibliographic record
Abstract
To tackle the growing complexity of engineered systems, Model-Driven Engineering (MDE) proposes to promote models to first-class citizens in the development process. Within MDE, Multi-Paradigm Modelling (MPM) advocates modelling every relevant aspect of a system explicitly, using the most appropriate formalism(s), at the most appropriate level(s) of abstraction, while explicitly modelling the underlying process. Often, activities of the process require interaction with (domain-specific) engineering and modelling tools. These interactions are, however, typically captured in scripts and program code, which is ill-suited for describing the timed, reactive, and concurrent behaviour of these protocols. Additionally, formal analysis of the overall process is limited due to the incorporation of black-box activities. In this paper, we propose an approach for the explicit modelling of service interaction protocols in the activities of MDE processes. We also explicitly model the execution semantics of our process model, to promote reuse and allow for future analysability. For both purposes, we propose to use SCCD, a Statecharts variant, resulting in a unified and concise formalism.
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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 | 0.004 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 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".