Automated software requirement and design understanding using deep learning-based UML diagram recognition and LLM-based design pattern detection

Abstract

This thesis presents an AI-based framework for automated software requirement and design understanding by combining deep learning for UML diagram recognition and large language models (LLMs) for software design pattern identification. Using a dataset of 6,666 UML diagram images across six categories, YOLO achieved the highest recognition accuracy of approximately 99%. For design pattern recognition, GraphCodeBERT combined with a k-Nearest Neighbor classifier achieved the best F1-score of 0.969. The findings demonstrate that AI techniques can effectively analyse visual software artifacts and source code, reducing manual effort and supporting automated software analysis, reverse engineering, and intelligent software engineering applications.