Workflow-Level Design Principles for Trustworthy GenAI in Automotive System Engineering
This work addresses trustworthiness issues for automotive engineers using GenAI in safety-critical applications, though it appears incremental in applying existing techniques to a specific domain.
The paper tackles the challenge of integrating large language models into safety-critical automotive system engineering by proposing workflow-level design principles that improve completeness and correctness in requirement delta identification and ensure traceable regression testing.
The adoption of large language models in safety-critical system engineering is constrained by trustworthiness, traceability, and alignment with established verification practices. We propose workflow-level design principles for trustworthy GenAI integration and demonstrate them in an end-to-end automotive pipeline, from requirement delta identification to SysML v2 architecture update and re-testing. First, we show that monolithic ("big-bang") prompting misses critical changes in large specifications, while section-wise decomposition with diversity sampling and lightweight NLP sanity checks improves completeness and correctness. Then, we propagate requirement deltas into SysML v2 models and validate updates via compilation and static analysis. Additionally, we ensure traceable regression testing by generating test cases through explicit mappings from specification variables to architectural ports and states, providing practical safeguards for GenAI used in safety-critical automotive engineering.