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.

Abstract Just‐In‐Time (JIT) defect prediction aims to predict the defect proneness of software changes when they are initially submitted. It has become a hot topic in software defect prediction due to its timely manner and traceability. Researchers have proposed many JIT defect prediction approaches. However, these approaches cannot effectively utilise line labels representing added or removed lines and ignore the noise caused by defect‐irrelevant files. Therefore, a JIT defect prediction model enhanced by the joint method of line label Fusion and file Filtering (JIT‐FF) is proposed. Firstly, to distinguish added and removed lines while preserving the original software changes information, the authors represent the code changes as original, added, and removed codes according to line labels. Secondly, to obtain semantics‐enhanced code representation, a cross‐attention‐based line label fusion method to perform complementary feature enhancement is proposed. Thirdly, to generate code changes containing fewer defect‐irrelevant files, the authors formalise the file filtering as a sequential decision problem and propose a reinforcement learning‐based file filtering method. Finally, based on generated code changes, CodeBERT‐based commit representation and multi‐layer perceptron‐based defect prediction are performed to identify the defective software changes. The experiments demonstrate that JIT‐FF can predict defective software changes more effectively.