Generating test cases for automotive requirement testingusing ragKrepek, Matic (Avtor) Klančnik, Simon (Mentor) Dvoršek, Nejc (Komentor) automotive requirements validationtest case generationlarge Language ModelsRetrieval-Augmented GenerationThe automotive industry is increasingly confronted with challenges in managing complex requirements and test cases arising from the integration of advanced electronic systems, software functionalities, and compliance with international standards. Conventional manual validation of requirements is time-consuming, error-prone, and resource-intensive, underscoring the need for more efficient and reliable approaches. This thesis investigates the automation of test case generation through the application of Retrieval-Augmented Generation (RAG) in combination with Large Language Models (LLMs). A complete RAG workflow was implemented in Python, incorporating LangChain, LangGraph, Ollama, and ChromaDB to facilitate indexing, retrieval, and generation. The system was trained and evaluated on datasets comprising automotive requirements and test cases, with experiments examining embedding quality, retrieval strategies, prompt engineering techniques, and generative model parameters. The results demonstrate that RAG is capable of generating high-quality, contextually relevant test cases on consumer-grade hardware, thereby significantly enhancing efficiency, consistency, and productivity relative to manual methods. Furthermore, the findings suggest that RAG-based systems are best positioned as complementary tools that support, rather than replace, human engineers. This research provides a foundation for future work on hybrid retrieval methods, advanced embedding techniques, and the integration of more powerful LLMs into requirement and test case management processes.[M. Krepek]20252025-09-04 17:17:48Magistrsko delo/naloga95104UDK: 629.3.05.018(043.2)COBISS_ID: 258059779sl