mlsanityData to 14 Oct 2026

AutoBridge: Automating Smart Device Integration with Centralized Platform

arXiv 2507.23178 ↗First posted 31 Jul 2025cs.SE cs.AIcited by 0

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Abstract (arXiv)

Multimodal IoT systems coordinate diverse IoT devices to deliver human-centered services. The ability to incorporate new IoT devices under the management of a centralized platform is an essential requirement. However, it requires significant human expertise and effort to program the complex IoT integration code that enables the platform to understand and control the device functions. Therefore, we propose AutoBridge to automate IoT integration code generation. Specifically, AutoBridge adopts a divide-and-conquer strategy: it first generates device control logic by progressively retrieving device-specific knowledge, then synthesizes platformcompliant integration code using platform-specific knowledge. To ensure correctness, AutoBridge features a multi-stage debugging pipeline, including an automated debugger for virtual IoT device testing and an interactive hardware-in-the-loop debugger that requires only binary user feedback (yes and no) for real-device verification. We evaluate AutoBridge on a benchmark of 34 IoT devices across two open-source IoT platforms. The results demonstrate that AutoBridge can achieves an average success rate of 93.87% and an average function coverage of 94.87%, without any human involvement. With minimal binary yes and no feedback from users, the code is then revised to reach 100% function coverage. A user study with 15 participants further shows that AutoBridge outperforms expert programmers by 50% to 80% in code accuracy, even when the programmers are allowed to use commercial code LLMs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 56 references that are in the map. First posted on arXiv 31 Jul 2025.

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References · 21

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
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9 more
  1. 2023RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generationcited by 367
  2. 2023Self-collaboration Code Generation via ChatGPTcited by 258
  3. 2024CodeGemma: Open Code Models Based on Gemmacited by 150
  4. 2021VLM: Task-agnostic Video-Language Model Pre-training for Video Understandingcited by 102
  5. 2024WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code Visualizationcited by 39
  6. 2023Exploring and Characterizing Large Language Models For Embedded System Development and Debuggingcited by 20
  7. 2024ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Codecited by 19
  8. 2025AutoIOT: LLM-Driven Automated Natural Language Programming for AIoT Applicationscited by 17
  9. 2025GPIoT: Tailoring Small Language Models for IoT Program Synthesis and Developmentcited by 10

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