State Diagram Extension and Test Case Generation Based on Large Language Models for Improving Test Engineers’ Efficiency in Safety Testing

Safety testing is a key method to ensure software quality. But the quality of the test depends on the level of the test engineers. The method of generating safety test cases based on state diagrams often perform poorly due to lack of personnel experience. Meanwhile, the manual construction of test cases constrains the improvement of safety testing efficiency. This article utilizes LLMs to achieve automated extension and testing of safety state diagrams. The main contributions include extracting safety properties from aviation standards and extending the basic state diagram with LLMs to enable safety testing capabilities. In addition, a genetic algorithm combining edge first depth traversal and LLMs optimization was proposed to improve the efficiency of test case generation. Compared to classical genetic algorithms, the average execution round achieved a 90% reduction. Our LLMs optimization strategy can also enhance other improved genetic algorithms, further improving the efficiency of safety testing.