用大模型自动完成车载API测试，提升效率与准确性。
Automating a Complete Software Test Process Using LLMs: An Automotive Case Study
- 分阶段设计测试流程，让大模型专注特定任务。
- 在100多个车载API上验证，实现高效自动化测试。
- 适合需要全流程自动化测试的汽车软件团队。
车载API测试旨在验证车辆内部系统与外部应用之间的交互是否符合预期，确保用户能访问和控制各类车辆功能与数据。然而，该任务本身极为复杂，需协调API系统、通信协议甚至车辆仿真系统以生成有效测试用例。在实际工业场景中，文档与系统规格间存在的不一致、模糊性和相互依赖性带来了巨大挑战。本文提出一套面向车载API自动测试的系统，通过清晰定义并拆分测试流程，使大型语言模型（LLMs）能够专注于特定任务，保障测试工作流的稳定与可控。在超过100个车载API上的实验表明，该系统能有效实现车载API测试的自动化。结果还证实，大模型可高效处理需要人工判断的重复性任务，适用于类似工业场景的全流程自动化。
原文摘要 · Abstract (English)
Vehicle API testing verifies whether the interactions between a vehicle's internal systems and external applications meet expectations, ensuring that users can access and control various vehicle functions and data. However, this task is inherently complex, requiring the alignment and coordination of API systems, communication protocols, and even vehicle simulation systems to develop valid test cases. In practical industrial scenarios, inconsistencies, ambiguities, and interdependencies across various documents and system specifications pose significant challenges. This paper presents a system designed for the automated testing of in-vehicle APIs. By clearly defining and segmenting the testing process, we enable Large Language Models (LLMs) to focus on specific tasks, ensuring a stable and controlled testing workflow. Experiments conducted on over 100 APIs demonstrate that our system effectively automates vehicle API testing. The results also confirm that LLMs can efficiently handle mundane tasks requiring human judgment, making them suitable for complete automation in similar industrial contexts.
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