Towards an effective approach for composition of model transformations
Bibliographic record
Abstract
Model Driven Engineering (MDE) adoption in the industry suffers from many technical and non-technical problems. One of the significant technical problems lies in the difficulty of building complex transformations from the composition of small and reusable transformations. Another problem resides in developing transformations from scratch in case they are missing. In this paper, we present an approach to how to handle these issues. The approach allows composing reusable transformations to build more complex ones by providing a catalog of prebuilt transformations targeting common architectures, frameworks, and design patterns. To give guidance and simplify the task of developing new transformations, we describe a platform description model of an entire system or a part of it in two views: a UML profile and a set of transformations. We also present three transformation types, each of which handles different abstraction design concerns. Generic transformations are small and reusable to build complex transformations, system-independent transformations are reusable and implement high-level design decisions, and system-specific transformations are not reusable and implement all design decisions needed for a given system. The approach is implemented as a plugin for a UML modeling tool and validated by developing a system that simulates the behavior of a gas station through model transformations built from the composition of reusable transformations.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".