Bridging the Gap Between System Architecture and Software Design using Model Transformation
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
A well-known challenge when adopting Model-Based Systems Engineering (MBSE) for building reliable software-intensive systems is the incompatibility between the tools and underlying formalisms used for different engineering tasks. Incompatibility can lead to redundant work, less reliable software development processes, and can hinder traceability between development artifacts. Stellantis, our industrial partner, encountered difficulties bridging the gap between SysML system architecture models and AUTOSAR software architecture models. To address this, we propose a model-to-model transformation that refines a SysML system architecture model into an AUTOSAR software model. The approach is developed to enhance the reliability of the process of producing software designs while utilizing tools that have been proven to apply to the industrial development of reliable systems. Our approach has been evaluated by Stellantis’ system and software architects. We demonstrate the effectiveness of the approach through an example and share our experiences and lessons learned.
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 | 0.001 | 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 | 0.000 | 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