Automated Control Logic Test Case Generation using Large Language Models

TL;DR

This paper introduces a novel method for generating PLC control logic test cases by leveraging Large Language Models, demonstrating efficiency and high coverage but also highlighting issues with erroneous assertions.

Contribution

The paper presents a new approach that uses LLMs for automatic PLC test case generation, reducing complexity compared to traditional symbolic and search-based methods.

Findings

Approach is fast and easy to use.

Achieves high statement coverage for simple to medium complexity programs.

Generated test cases often contain erroneous assertions needing manual correction.

Abstract

Testing PLC and DCS control logic in industrial automation is laborious and challenging since appropriate test cases are often complex and difficult to formulate. Researchers have previously proposed several automated test case generation approaches for PLC software applying symbolic execution and search-based techniques. Often requiring formal specifications and performing a mechanical analysis of programs, these approaches may uncover specific programming errors but sometimes suffer from state space explosion and cannot process rather informal specifications. We proposed a novel approach for the automatic generation of PLC test cases that queries a Large Language Model (LLM) to synthesize test cases for code provided in a prompt. Experiments with ten open-source function blocks from the OSCAT automation library showed that the approach is fast, easy to use, and can yield test cases with high statement coverage for low-to-medium complex programs. However, we also found that LLM-generated test cases suffer from erroneous assertions in many cases, which still require manual adaption.