Acceleration of Automotive Software Development by Retrieval Augmented Integration Test Script Generation

Improving the efficiency of software integration testing is a critical challenge in the automotive industry, particularly as Electronic Control Unit (ECU) architectures become increasingly complex. This paper addresses the automation of integration test script generation by leveraging Large Language Models (LLMs) with Retrieval Augmented Generation (RAG). Specifically, we target the phase in which test engineers translate natural language test case specifications into executable scripts for integration test environments containing hardware debug interfaces. To bridge the knowledge gap between LLMs and domain-specific test tool APIs, we construct a task-oriented vector store that incorporates both API manuals and supplemental, workflow-centric information. By combining these with prompts containing code prefixes, our method enables LLMs to generate robust and correct integration test scripts. We evaluated our approach on typical test scenarios reflecting industry practices for multi-core ECUs. While the test cases used were not directly taken from a specific development project, they closely mirror those routinely employed across numerous automotive ECU development initiatives. The proposed method successfully generated executable scripts for all cases and reduced total test execution man-hours by 43% compared to a realistic baseline of manual execution. These results demonstrate the practical benefit of context-enriched LLMs in accelerating specialized software engineering tasks within the automotive domain, and it also identifies remaining challenges in extending automation to broader aspects such as test coverage, maintainability, and seamless process integration.