Embedded IoT system development is crucial for enabling seamless connectivity and functionality across a wide range of applications. However, such a complex process requires cross-domain knowledge of hardware and software and hence often necessitates direct developer involvement, making it labor-intensive, time-consuming, and error-prone. To address this challenge, this paper introduces EmbedGenius, the first fully automated software development platform for general-purpose embedded IoT systems. The key idea is to leverage the reasoning ability of Large Language Models (LLMs) and embedded system expertise to automate the hardware-in-the-loop development process. The main methods include a component-aware library resolution method for addressing hardware dependencies, a library knowledge generation method that injects utility domain knowledge into LLMs, and an auto-programming method that ensures successful deployment. We evaluate EmbedGenius's performance across 71 modules and four mainstream embedded development platforms with over 350 IoT tasks. Experimental results show that EmbedGenius can generate codes with an accuracy of 95.7% and complete tasks with a success rate of 86.5%, surpassing human-in-the-loop baselines by 15.6%--37.7% and 25.5%--53.4%, respectively. We also show EmbedGenius's potential through case studies in environmental monitoring and remote control systems development.

翻译：嵌入式物联网系统开发对于实现广泛应用场景中的无缝连接与功能至关重要。然而，这一复杂过程需要硬件与软件的跨领域知识，因此通常需要开发者的直接参与，导致其具有劳动密集、耗时且易出错的特点。为应对这一挑战，本文提出了EmbedGenius——首个面向通用嵌入式物联网系统的全自动化软件开发平台。其核心思想是利用大语言模型的推理能力与嵌入式系统专业知识，实现硬件在环开发流程的自动化。主要方法包括：用于解决硬件依赖的组件感知库解析方法、向大语言模型注入实用领域知识的库知识生成方法，以及确保成功部署的自动编程方法。我们在涵盖71个模块和四种主流嵌入式开发平台的超过350项物联网任务上评估了EmbedGenius的性能。实验结果表明，EmbedGenius生成的代码准确率达95.7%，任务完成成功率达86.5%，分别比人在回路的基线方法高出15.6%–37.7%和25.5%–53.4%。我们还通过环境监测与远程控制系统开发的案例研究，展示了EmbedGenius的应用潜力。