用大模型把汽车需求自动转成测试用例，省时高效。
GenAI-based test case generation and execution in SDV platform
- 用大语言模型和视觉语言模型将自然语言需求转为Gherkin格式测试用例。
- 在数字汽车沙盒中执行，儿童存在检测系统测试效率提升显著。
- 适合智能汽车软件测试团队快速验证功能，降低人工编写成本。
本文提出一种基于生成式人工智能的自动化测试用例生成方法，利用大语言模型与视觉语言模型，将自然语言需求和系统图转换为结构化的Gherkin测试用例。该方法结合车辆信号规范建模，统一车辆信号定义，提升各子系统兼容性，并便于与第三方测试工具集成。生成的测试用例在digital.auto沙盒环境中执行，这是一个开放且厂商无关的平台，用于快速验证软件定义汽车功能。通过儿童存在检测系统用例评估，证明了手动测试规格工作量大幅减少，测试用例生成与执行速度明显加快。尽管实现高度自动化，当前仍需人工干预生成测试用例和脚本，受限于生成式AI管道能力和digital.auto平台约束。
原文摘要 · Abstract (English)
This paper introduces a GenAI-driven approach for automated test case generation, leveraging Large Language Models and Vision-Language Models to translate natural language requirements and system diagrams into structured Gherkin test cases. The methodology integrates Vehicle Signal Specification modeling to standardize vehicle signal definitions, improve compatibility across automotive subsystems, and streamline integration with third-party testing tools. Generated test cases are executed within the digital.auto playground, an open and vendor-neutral environment designed to facilitate rapid validation of software-defined vehicle functionalities. We evaluate our approach using the Child Presence Detection System use case, demonstrating substantial reductions in manual test specification effort and rapid execution of generated tests. Despite significant automation, the generation of test cases and test scripts still requires manual intervention due to current limitations in the GenAI pipeline and constraints of the digital.auto platform.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。