一句话：提出通过切分测试流程让 LLM 专注子任务的车载 API 自动化测试系统，在 100 多个 API 上验证有效。
车载 API 测试用于验证车辆内部系统与外部应用之间的交互是否符合预期，但其本身高度复杂：需要协调 API 系统、通信协议乃至车辆仿真系统才能构造出有效的测试用例。在实际工业场景中，各类文档与系统规约之间还存在不一致、歧义和相互依赖等难题。本文提出了一套面向车载 API 自动化测试的系统。作者通过明确定义并切分测试流程，使大语言模型（LLM）只需聚焦于具体的子任务，从而保证整个测试工作流的稳定与可控。在 100 多个 API 上开展的实验表明，该系统能够有效实现车载 API 测试的自动化；结果还证实 LLM 可以高效处理那些原本需要人工判断的琐碎任务，因而适合在类似工业场景中实现完整自动化。
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 interde-pendencies 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.
DOI 原文 ·
@inproceedings{paperbot8782,
title = {Automating a Complete Software Test Process Using LLMs: An Automotive Case Study},
author = {Shuai Wang and Yinan Yu and Robert Feldt and Dhasarathy Parthasarathy},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00211}
}