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LLM-Based Design Pattern Detection

Authors:
Christian Schindler
Andreas Rausch

Keywords: Design Pattern detection; Large Language Model

Abstract:
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 imple- mentations. In this paper, we present a novel approach leveraging Large Language Models (LLMs) 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.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2025

Publication date: April 6, 2025

Published in: conference

ISSN: 2308-3557

ISBN: 978-1-68558-263-0

Location: Valencia, Spain

Dates: from April 6, 2025 to April 10, 2025