Efficient XML Parsing: Enhancing Efficiency Through Design and Analysis
Omar Raad Alsammak, Ashraf Abdulmunim Abdulmajeed · IRAQI JOURNAL OF STATISTICAL SCIENCES · 2026
The development and analysis of UML class diagrams are fundamental aspects of software engineering, providing insights into system structure and design. This paper introduces a novel XML parser designed to efficiently parse and classify UML class diagrams, leveraging XML’s structured format for improved data extraction and evaluation. The proposed parser addresses limitations identified in previous research, particularly in handling large and complex UML structures, performance optimization, and integration with machine learning models for advanced diagram classification. The parser’s ability to process detailed relationships and hierarchies within the diagrams enhances the accuracy of classification, and the integration with machine learning models facilitates automated analysis and prediction of diagram quality. The results of this parser are presented as inputs for further machine learning models, contributing to enhanced software development processes. Through systematic testing and comparison with existing methods, this paper demonstrates the parser’s superior efficiency and scalability, making it a valuable tool for both UML diagram analysis and future research in software engineering.