A Retrieval-Augmented Generation System for Automated Functional Safety Analysis of AUTOSAR Basic Software Module Dependencies

This study presents an advanced Retrieval-Augmented Generation (RAG) system designed to assist functional safety engineers in performing safety analysis of AUTOSAR Classic Platform Basic Software (BSW) module dependencies. The system extracts structured dependency information from 128 AUTOSAR Software Specification (SWS) documents in ARXML format and generates draft Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and Dependent Failure Analysis (DFA) tables compliant with AIAG VDA, IEC 60812, IEC 61025, and ISO 26262 standards for human expert review and approval. Key innovations include: 1) LLM-driven table definition extraction that designs optimal analysis output formats based on merged AUTOSAR safety context, ISO 26262 lifecycle considerations, and standard methodologies; 2) content-based inter-module dependency validation that prevents hallucination of non-existent module interactions; 3) ASIL-aware analysis that prioritizes lower-integrity components corrupting higher-integrity components per ISO 26262 freedom from interference; 4) a modular architecture with dual interfaces (CLI tool and LangGraph-based conversational chatbot) where the chatbot reuses core RAG functions, enabling single-source maintenance. The architecture combines semantic chunking with metadata-based filtering for precise module retrieval, episodic and working memory for multi-turn sessions, and automated Excel report generation with source traceability. A comparative evaluation against an LLM-only baseline and a standard semantic-search RAG baseline demonstrates that metadata filtering with content validation eliminates hallucinated dependencies. On a curated stress-test dataset of 15 safety-critical modules representing the most complex BSW interdependencies (watchdog supervision, diagnostics, memory management, communication stacks), the system achieves perfect micro-averaged precision/recall across 95 documented dependencies. Preliminary expert validation by three functional safety engineers confirmed the practical utility of the generated analyses as draft starting points for formal safety assessments.

@article{Hammad2026,
  title     = {A Retrieval-Augmented Generation System for Automated Functional Safety Analysis of AUTOSAR Basic Software Module Dependencies},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Mohand Hammad and Ahmed Moro and Mohamed Taher},
  doi       = {10.14569/IJACSA.2026.0170396},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170396}
}