面向物联网汽车应用的全自动、感知部署的测试流水线
A Fully Automated, Deployment-Aware Testing Pipeline for IoT-Based Automotive Applications
AI总结：
针对物联网汽车应用的测试难题，提出结合LLM、VLM及人在回路机制的感知部署测试流水线，经CPDS案例验证可实现全需求覆盖与高准确率，适配OEM-供应商测试工作流。
AI中文摘要：
现代车辆中嵌入式软件的测试颇具挑战，原因在于系统复杂度高、架构分散，且存在严格的安全与性能约束。本研究提出一种面向物联网汽车应用的端到端、感知部署的测试流水线。该流水线结合需求驱动的测试与代码生成，依托大语言模型（LLM）和视觉语言模型（VLM）提供辅助，并引入人在回路的审核机制，以减少人工工作量并提升一致性。借助Eclipse openDuT，该流水线支持在地理上分散的网络实体与物联网基础设施间实现灵活、分布式部署，同时针对节点可用性及跨组织协调进行优化。为验证其有效性，本研究以儿童乘员检测系统（CPDS）为对象开展案例研究，在全部9项需求上实现了完整的功能需求覆盖，且在受控需求集上达成100%的Gherkin生成准确率。通过Eclipse openDuT在地理分散的电子控制单元（ECU）上开展的分布式测试执行，证实了该流水线适用于原始设备制造商（OEM）-供应商测试工作流。
英文摘要：
Testing embedded software in modern vehicles is challenging due to system complexity, decentralized architectures, and strict safety and performance constraints. In this work, we present an end-to-end, deployment-aware testing pipeline for IoT-based automotive applications. The pipeline combines requirement-driven test and code generation with large language model (LLM) and vision-language model (VLM) assistance, and human-in-the-loop curation to reduce manual effort and improve consistency. Using Eclipse openDuT, it supports flexible, distributed deployment across geographically separated cyber-physical and IoT infrastructures, optimizing for node availability and cross-organizational coordination. For validation, we conduct a case study using a Child Presence Detection System (CPDS), achieving full functional requirement coverage across all 9 requirements and 100% Gherkin generation accuracy on the controlled requirement set. Distributed test execution across geographically separated ECUs via Eclipse openDuT confirms the pipeline's applicability to OEM--supplier testing workflows.
