LLM-Based Design Pattern Detection

--Detecting design pattern instances in unfamiliar codebases remains a challenging yet essential task for improving software quality and maintainability. Traditional static analysis tools often struggle with the complexity, variability, and lack of explicit annotations that characterize real-world pattern implementations. In this paper, we present a novel approach leveraging Large Language Models to automatically identify design pattern instances across diverse codebases. Our method focuses on recognizing the roles classes play within the pattern instances. By providing clearer insights into software structure and intent, this research aims to support developers, improve comprehension, and streamline tasks such as refactoring, maintenance, and adherence to best practices. Identifying design pattern instances in code is a valuable goal as it enables a deeper understanding of the structural and behavioral principles underlying software systems. By uncovering these patterns, developers and other stakeholders can gain insights into code quality, maintainability, and adherence to best practices, even in unfamiliar code bases. Automating this process can significantly reduce the time and effort required for code comprehension, facilitate knowledge transfer among teams, and improve software evolution and refactoring efforts.