Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflow
<|reference_start|>Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflow: We propose a novel model- and feature-based approach to development of vehicle software systems, where the end architecture is not explicitly defined. Instead, it emerges from an iterative process of search and optimization given certain constraints, requirements and hardware architecture, while retaining the property of single-system illusion, where applications run in a logically uniform environment. One of the key points of the presented approach is the inclusion of modern generative AI, specifically Large Language Models (LLMs), in the loop. With the recent advances in the field, we expect that the LLMs will be able to assist in processing of requirements, generation of formal system models, as well as generation of software deployment specification and test code. The resulting pipeline is automated to a large extent, with feedback being generated at each step.<|reference_end|>
@article{lebioda2024towards,
    title={Towards Single-System Illusion in Software-Defined Vehicles --
  Automated, AI-Powered Workflow},
    author={Krzysztof Lebioda, Viktor Vorobev, Nenad Petrovic, Fengjunjie Pan,
  Vahid Zolfaghari, Alois Knoll},
    journal={arXiv preprint arXiv:2403.14460},
    year={2024},
    number={TUM-I24108},
    archivePrefix={arXiv},
    eprint={2403.14460},
    primaryClass={cs.SE cs.AI cs.CL}
}