LLM-Enhanced Test Case Prioritization for Complex Software Systems

This paper presents a novel approach to test case prioritization using Large Language Models (LLMs) for complex software systems. Traditional prioritization methods often struggle with the dynamic nature of modern software development and the large amounts of unstructured data generated during the software lifecycle. Our method leverages LLMs to analyze diverse data sources, including code changes, user feedback, and system documentation, creating a more adaptive and context-aware prioritization strategy. We applied our approach to an Internet of Things (IoT) based system for motion tracking in ten-pin bowling. The experimental results show significant improvements over a baseline Additional Statement Coverage method. Our LLM-enhanced approach achieved a 12.12% higher Average Percentage of Faults Detected (APFD) score and reduced test suite execution time by 26 %. These findings demonstrate the potential of LLMs to enhance software testing practices, particularly in early fault detection and efficient resource utilization. The paper discusses implementation details, evaluation metrics, and future directions for integrating this approach into continuous integration and deployment pipelines.