Programmable Logic Controllers (PLCs) are microcomputers essential for automating factory operations. Structured Text (ST), a high-level language adhering to the IEC 61131-3 standard, is pivotal for PLCs due to its ability to express logic succinctly and to seamlessly integrate with other languages within the same standard. However, vendors develop their own customized versions of ST, and the lack of comprehensive and standardized documentation for the full semantics of ST has contributed to inconsistencies in how the language is implemented. Consequently, the steep learning curve associated with ST, combined with ever-evolving industrial requirements, presents significant challenges for developers. In response to these issues, we present AutoPLC, an LLM-based approach designed to automate the generation of vendor-specific ST code. To facilitate effective code generation, we first built a comprehensive knowledge base, including Rq2ST Case Library (requirements and corresponding implementations) and Instruction libraries. Then we developed a retrieval module to incorporate the domain-specific knowledge by identifying pertinent cases and instructions, guiding the LLM to generate code that meets the requirements. In order to verify and improve the quality of the generated code, we designed an adaptable code checker. If errors are detected, we initiate an iterative self-improvement process to instruct the LLM to revise the generated code. We evaluate AutoPLC's performance against seven state-of-the-art baselines using three benchmarks, one for open-source basic ST and two for commercial Structured Control Language (SCL) from Siemens. The results show that our approach consistently achieves superior performance across all benchmarks. Ablation study emphasizes the significance of our modules. Further manual analysis confirm the practical utility of the ST code generated by AutoPLC.

翻译：可编程逻辑控制器（PLCs）是实现工厂自动化操作不可或缺的微型计算机。结构化文本（ST）作为一种遵循IEC 61131-3标准的高级编程语言，因其能够简洁地表达逻辑并可与同一标准下的其他语言无缝集成，在PLC编程中具有关键作用。然而，各厂商开发了各自定制化的ST版本，且缺乏全面、标准化的完整ST语义文档，这导致了语言实现方式的不一致。因此，ST陡峭的学习曲线与不断演进的工业需求相结合，给开发者带来了重大挑战。针对这些问题，我们提出了AutoPLC——一种基于大语言模型的方法，旨在自动化生成厂商特定的ST代码。为促进有效的代码生成，我们首先构建了一个综合性知识库，包括需求到ST的案例库（需求及对应实现）与指令库。随后，我们开发了一个检索模块，通过识别相关案例与指令来融入领域特定知识，从而引导大语言模型生成满足需求的代码。为了验证并提升生成代码的质量，我们设计了一个自适应代码检查器。若检测到错误，系统将启动迭代式自我改进流程，指导大语言模型修正生成的代码。我们在三个基准测试上评估了AutoPLC的性能，并与七种最先进的基线方法进行了对比：一个基准针对开源基础ST，另外两个针对西门子的商用结构化控制语言（SCL）。结果表明，我们的方法在所有基准测试中均持续取得优越性能。消融实验凸显了各模块的重要性。进一步的人工分析证实了AutoPLC生成的ST代码具有实际应用价值。