As industrial PLC programs become more complex, automated testing and verification methods are needed to ensure their reliability and correctness. This paper presents PyLC+, a modular framework that translates PLC programs into Python, allowing for automated AI-driven test generation. PyLC+ builds upon our previous work, addressing limitations by adopting a class-based modular architecture that improves the tool’s scalability, maintainability, and extensibility. This structural refinement eliminates reliance on nested functions, facilitating the translation of large-scale, real-world PLC programs while maintaining precise use of cyclic execution. Furthermore, PyLC+ introduces automated handling of stateful FBs, ensuring compliance with IEC 61131-3 execution semantics.Additionally, the tool proposes integrating LLM-driven test generation with search-based test generation to improve the efficiency and effectiveness of testing PLC software. We tested PyLC+ in a large-scale company developing train control systems, demonstrating its efficiency and effectiveness in handling complex industrial PLC programs.

Numerous industrial sectors employ Programmable Logic Controllers (PLC) software to control safety-critical systems. These systems necessitate extensive testing and stringent coverage measurements, which can be facilitated by automated test-generation techniques. Existing such techniques have not been applied to PLC programs, and therefore do not directly support the latter regarding automated test-case generation. To address this deficit, in this work, we introduce PyLC, a tool designed to automate the conversion of PLC programs to Python code, assisted by an existing test generator called Pynguin. Our framework is capable of handling PLC programs written in the Function Block Diagram language. To demonstrate its capabilities, we employ PyLC to transform safety-critical programs from industry and illustrate how our approach can facilitate the manual and automatic creation of tests. Our study highlights the efficacy of leveraging Python as an intermediary language to bridge the gap between PLC development tools, Python-based unit testing, and automated test generation.