# Automated Control Logic Test Case Generation using Large Language Models

arXiv: https://arxiv.org/abs/2405.01874 · HTML: https://mlsanity.com/paper/2405.01874/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2024.

## Built on by (most cited first, 6 of 6)

- [Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents](https://mlsanity.com/paper/2410.14209/) (2024)
- [AutoPLC: Generating Vendor-Aware Structured Text for Programmable Logic Controllers](https://mlsanity.com/paper/2412.02410/) (2024)
- [A Systematic Approach for Assessing Large Language Models' Test Case Generation Capability](https://mlsanity.com/paper/2502.02866/) (2025)
- [PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generation](https://mlsanity.com/paper/2605.00942/) (2026)
- [Cluster-Aware Dual-Level Test Specification Generation for Large-Scale Automotive Software Requirements](https://mlsanity.com/paper/2606.17197/) (2026)
- [An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models](https://mlsanity.com/paper/2607.21292/) (2026)

## Abstract (arXiv)

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.