Modular synthesis of mobile device applications from domain-specific models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it