Among the programming languages for Programmable Logic Controllers (PLCs), Structured Text (ST) is widely adopted for industrial automation due to its expressiveness and flexibility. However, major vendors implement ST with proprietary extensions and hardware-specific libraries - Siemens’ SCL and CODESYS’ ST each differ in syntax and functionality. This fragmentation forces engineers to relearn implementation details across platforms, creating substantial productivity barriers. To address this challenge, we developed AutoPLC, a framework capable of automatically generating vendor-aware ST code directly from natural language requirements. Our solution begins by building two essential knowledge sources tailored to each vendor’s specifications: a structured API library containing platform-exclusive functions, and an annotated case database that captures real-world implementation experience. Building on these foundations, we created a four-stage generation process that combines step-wise planning (enhanced with a lightweight natural language state machine support for control logic), contextual case retrieval using LLM-based reranking, API recommendation guided by industrial data, and dynamic validation through direct interaction with vendor IDEs. Implemented for Siemens TIA Portal and the CODESYS platform, AutoPLC achieves 90%+ compilation success on our 914-task benchmark (covering general-purpose and process control functions), outperforming all selected baselines, at an average cost of only $0.13 per task. Experienced PLC engineers positively assessed the practical utility of the generated code, including cases that failed compilation.

This paper studies the performance of large-scale wireless networks with finite queue lengths, an aspect that has been largely overlooked in previous studies focusing on single-link or single-queue models. By modeling packet transmission in networks with finite-length queues and employing meta distribution analysis, we derive precise expressions for delay and packet loss probability. Our findings show that when queue capacities are limited, the transmission success probability significantly influences packet loss behavior. Specifically, a sharp increase in packet loss occurs when the transmission success probability drops below a critical threshold. While increasing queue length does not mitigate packet loss in suboptimal transmission conditions, it can notably enhance network performance under typical scenarios. This study provides important insights for optimizing resource allocation in large-scale wireless networks, emphasizing the need to balance queue length and transmission success probability for better efficiency.