Title: Boosting Verifiable Industrial Code Generation by Reliable Task Generation at Scale
Primary area: datasets and benchmarks
Keywords: Industrial Control Systems, Programmable Logic Controllers, Data Augmentation, Code Generation

Abstract: Recent advances in industrial copilots (e.g., from Siemens, Rockwell and Schneider) for Programmable Logic Controllers (PLCs) have the potential to transform the way control engineers program. However, the closed-source nature and scarcity of data for Industrial Control System (ICS) programming tasks cast fundamental challenges to utilize LLMs for generating verifiable industrial code (e.g., free of both syntactic and logical errors), which is of vital importance in industrial control applications.To address this critical gap, we introduce  PLC-Spec-Syn, the first evolutionary framework to generate high-fidelity PLC programming tasks. Each task consists of a detailed specification—a structured, natural language engineering document—and its corresponding verified PLC code.The core idea is to guide LLM-based task generation (specification–code pair) with practical industrial engineering principles through a multi-axis evolutionary process considering six dimensions:functionality, safety, performance, maintenance, interoperability, and contextual complication.To ensure data quality, each generated specification–code pair will undergo rigorous auditing including compilation check and formal verification of semantic consistency between the specification and the code.The whole process yields PLC-Spec-Code, the first large-scale corpus of 11,669 PLC programming tasks with strict quality control.Besides, PLC-Spec-Code has 84.3% syntactic diversity, substantially exceeding that of existing corpus like OSCAT (29.2%). Importantly, fine-tuning multiple (code) LLMs using our corpus improves their performance on verifiable PLC code generation in unseen tasks by an average of 16.4% compared to the previous models, confirming the effectiveness of our task generation approach and the practical usefulness of our corpus.