Advanced Requirement Classification and Standardization Model (ARCSM) for Enhanced Software Quality and Security

Abstract

Building on the foundational work of requirement classification and its impact on software quality, this research extends the Associated Requirement Classification Model (ARCM) to incorporate advanced techniques for enhanced software quality and security. The proposed Advanced Requirement Classification and Standardization Model (ARCSM) aims to address the evolving challenges in software engineering by integrating machine learning algorithms, natural language processing (NLP), and robust security protocols. This study focuses on improving the precision and consistency of requirement classification, encompassing both functional and non-functional requirements, while emphasizing security and data integrity. The methodology involves the use of state-of-the-art machine learning models, such as BERT and LSTM, for accurate classification of requirements. Additionally, the model employs advanced data preprocessing techniques to handle large datasets effectively, ensuring high-quality feature selection and extraction. The integration of security measures within the requirement classification process aims to mitigate potential vulnerabilities from the early stages of software development. The research evaluates the performance of ARCSM through comprehensive experiments and case studies, comparing it with traditional models like NLSSD-OSP and BERT-GSRE. The results indicate significant improvements in classification accuracy, reaching up to 99%, and a substantial reduction in processing time. Furthermore, the model demonstrates enhanced capability in identifying and addressing security requirements, thereby contributing to the development of secure and reliable software systems. This study underscores the importance of a holistic approach to requirement classification, incorporating both quality and security aspects, to meet the complex demands of modern software engineering. The findings provide valuable insights for practitioners and researchers, offering a robust framework for enhancing software quality and security through advanced requirement classification and standardization techniques.