Addressing the shortcomings of commercial-of-the-shelf model-to-model transformations with open-source tools; from SysML to AUTOSAR
Bibliographic record
Abstract
Model-Based Systems Engineering (MBSE) is a widely adopted approach to managing the complexity of modern cyber physical systems, including automotive systems. In the domain of automotive engineering, it is common for engineers to use a variety of languages, at various levels of abstraction, to provide diverse and concrete perspectives on a system. However, a significant incompatibility challenge arises due to weak or nonexistent integration among these languages. In some cases, these challenges can be addressed by using commercial off the shelf (COTS) model-to-model (M2M) transformation tools. However, in certain cases these tools have semantic and technical limitations that hinder the development process, produce sub-optimal results, and generate trace information in a proprietary format. In this paper, we present how the same transformation can be implemented using an open-source tool. First, we discuss the technical limitations and present how the open-source tool provides better development support. Then, we present the results of running both implementations for a set of test models and show that the open-source implementation provides more detailed output models and produces more fine-grained traceability data. By using the open-source implementation, we reduce the development effort, produce output that is better suited for purpose and generate trace information that can be easily consumed in other tools.
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.
How this classification was reachedexpand
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 | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".