Problem
Research questions and friction points this paper is trying to address.
embedded software
system complexity
decentralized architectures
safety and performance constraints
A Fully Automated, Deployment-Aware Testing Pipeline for IoT-Based Automotive Applications
🤖 AI Summary
本文提出一种结合大语言模型和视觉-语言模型的全自动测试管道，用于解决IoT汽车应用中的软件测试难题，通过分布式部署优化节点可用性和跨组织协调。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
deployment-aware testing pipeline
large language model (LLM)
vision-language model (VLM)
requirement-driven test and code generation
Eclipse openDuT
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