Extraction of UML class diagrams using deep learning: Comparative study and critical analysis
Zakaria Babaalla, Hamza Abdelmalek, Abdeslam Jakimi, Mohamed Oualla
Abstract
Several approaches and tools have been proposed to facilitate the automatic generation of Unified Modeling Language (UML) class diagrams from natural language specifications, based on advances in Natural Language Processing (NLP). However, these tools suffer from difficulties due to the inherent imprecision and ambiguity commonly found in natural language expressions. In this article, we present an overview and a study of approaches and tools designed to extract UML diagrams from textual requirements using NLP and computational linguistics techniques. Furthermore, we introduce approaches employing deep learning techniques. Next, we provide a descriptive study and comparative analysis of the limitations and contributions of these automatic and semiautomatic tools, as well as solutions for improving the existing state of the art.