Modular synthesis of mobile device applications from domain-specific models
Bibliographic record
Abstract
Domain-specific modelling enables modelling using constructs familiar to experts of a specific domain. Domain-specific models (DSms) can be automatically transformed to var-ious lower-level artifacts such as configuration files, docu-mentation, executable programs and performance models. Although many researchers have tackled the formalization of various aspects of model-driven development such as model versioning, debugging and transformation, very little atten-tion has been focused on formalizing how artifacts are ac-tually synthesized from DSms. State-of-the-art approaches rely on ad hoc coded generators which essentially use mod-elling tool APIs to programmatically iterate through model entities and produce the final artifacts. In this work, we propose a more structured approach to artifact generation where layered model transformations are used to modularly isolate, compile and re-combine various aspects of DSms. We demonstrate our technique by detailing the synthesis of running Google Android applications from DSms, and discuss how it may be applied in addressing the character-istic non-functional requirements (e.g. timing constraints, resource utilization) of modern embedded systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".