Testing functionality in Software-Defined Vehicles is challenging because requirements are written in natural language, specifications combine text, tables, and diagrams, while test assets are scattered across heterogeneous toolchains. Large Language Models and Vision-Language Models are used to extract signals and behavioral logic to automatically generate Gherkin scenarios, which are then converted into runnable test scripts. The Vehicle Signal Specification (VSS) integration standardizes signal references, supporting portability across subsystems and test benches. The pipeline uses retrieval-augmented generation to preselect candidate VSS signals before mapping. We evaluate the approach on the safety-relevant Child Presence Detection System, executing the generated tests in a virtual environment and on an actual vehicle. Our evaluation covers Gherkin validity, VSS mapping quality, and end-to-end executability. Results show that 32 of 36 requirements (89\%) can be transformed into executable scenarios in our setting, while human review and targeted substitutions remain necessary. This paper is a feasibility and architectural demonstration of an end-to-end requirements-to-test pipeline for SDV subsystems, evaluated on a CPDS case in simulation and Vehicle-in-the-Loop settings.

翻译：软件定义汽车的功能测试具有挑战性，因为需求以自然语言编写，规范结合了文本、表格和图表，而测试资产分散在异构的工具链中。本研究利用大型语言模型和视觉语言模型提取信号与行为逻辑，自动生成Gherkin场景，并将其转换为可运行的测试脚本。车辆信号规范（VSS）集成通过标准化信号引用，支持跨子系统与测试台的移植性。该流水线采用检索增强生成技术，在映射前预选候选VSS信号。我们在安全相关的儿童存在检测系统上评估该方法，在虚拟环境和实车上执行生成的测试。评估涵盖Gherkin有效性、VSS映射质量和端到端可执行性。结果表明，在我们的设定中，36项需求中有32项（89%）可转化为可执行场景，但仍需人工审查与针对性替换。本文通过仿真和车辆在环环境中的CPDS案例，论证了面向SDV子系统的端到端需求至测试流水线的可行性与架构设计。