A retrieval-augmented LLM approach validates and recovers traceability links between stakeholder and system requirements for automotive diagnostic trouble codes, reporting 98.87% validation accuracy and 85.50% recovery correctness on industrial data.
International Organization for Standardization, Geneva, Switzer- land (2015-03)
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TVR: Automotive System Requirement Traceability Validation and Recovery Through Retrieval-Augmented Generation
A retrieval-augmented LLM approach validates and recovers traceability links between stakeholder and system requirements for automotive diagnostic trouble codes, reporting 98.87% validation accuracy and 85.50% recovery correctness on industrial data.