Survey of GenAI for Automotive Software Development: From Requirements to Executable Code
Abstract
Adoption of state-of-art Generative Artificial Intelligence (GenAI) aims to revolutionize many industrial areas by reducing the amount of human intervention needed and effort for handling complex underlying processes. Automotive software development is considered to be a significant area for GenAI adoption, taking into account lengthy and expensive procedures, resulting from the amount of requirements and strict standardization. In this paper, we explore the adoption of GenAI for various steps of automotive software development, mainly focusing on requirements handling, compliance aspects and code generation. Three GenAI-related technologies are covered within the state-of-art: Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Vision Language Models (VLMs), as well as overview of adopted prompting techniques in case of code generation. Additionally, we also derive a generalized GenAI-aided automotive software development workflow based on our findings from this literature review. Finally, we include a summary of a survey outcome, which was conducted among our automotive industry partners regarding the type of GenAI tools used for their daily work activities.
Cite
@article{arxiv.2507.15025,
title = {Survey of GenAI for Automotive Software Development: From Requirements to Executable Code},
author = {Nenad Petrovic and Vahid Zolfaghari and Andre Schamschurko and Sven Kirchner and Fengjunjie Pan and Chengdng Wu and Nils Purschke and Aleksei Velsh and Krzysztof Lebioda and Yinglei Song and Yi Zhang and Lukasz Mazur and Alois Knoll},
journal= {arXiv preprint arXiv:2507.15025},
year = {2025}
}
Comments
Conference paper accepted for GACLM 2025