Model-Based Fault Injection and Diagnostic Validation for AUTOSAR Software Components in Safety-Critical Automotive Systems
Bibliographic record
Abstract
Ensuring the reliability of AUTOSAR software components in safety-critical automotive applications demands rigorous fault injection and diagnostic verification strategies. This paper introduces a systematic methodology for simulating and validating AUTOSAR component behavior within MATLAB/Simulink, utilizing the Diagnostic Event Manager (Dem) to assess fault handling mechanisms. The proposed approach employs Dem Status Override and Dem Status Inject blocks to simulate transient and persistent faults, enabling a comprehensive evaluation of diagnostic responses, fault recovery, and system resilience under abnormal operating conditions. By automating fault injections and validation, the methodology enhances test coverage, improves fault tolerance, and ensures compliance with AUTOSAR diagnostic specifications. Experimental results demonstrate the effectiveness of this framework in identifying vulnerabilities, strengthening component robustness, and streamlining verification processes for embedded automotive software. This work advances model-based fault validation techniques, contributing to the development of safer and more reliable AUTOSAR-compliant embedded systems.
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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 | 0.001 |
| 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 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".