DEVS for AUTOSAR-based system deployment modeling and simulation
Bibliographic record
Abstract
AUTOSAR (AUTomotive Open System ARchitecture) is an open and standardized automotive software architecture, developed by automobile manufacturers, suppliers, and tool developers. Its design is a direct consequence of the increasingly important role played by software in vehicles. As design choices during the software deployment phase have a large impact on the behavior of the system, designers need to explore various trade-offs. Examples of such design choices are the mapping of software components to processors, the priorities of tasks and messages, and buffer allocation. In this paper, we evaluate the appropriateness of DEVS, the Discrete-Event System specification, for modeling and subsequent performance evaluation of AUTOSAR-based systems. Moreover, a DEVS simulation model is constructed for AUTOSAR-based electronic control units connected by a communication bus. To aid developers in evaluating a deployment solution, the simulation model is extended with co-simulation with a plant and environment model, evaluation at different levels of detail, and fault injection. Finally, we examine how the simulation model supports the relationship between the supplier and the original equipment manufacturer in the automotive industry. We demonstrate and validate our work by means of a power window case study.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".