Cloud-based machine learning for scalable classification of software requirements: Insights from the PROMISE dataset
Ali H. Tanveer U. Saeed A. Alkahtani H.K. Alzahrani K.J. Akbayan B.
December 2025Academic Press
Systems and Soft Computing
2025#7
Software requirement classification (SRC) is a critical yet challenging task in large-scale software development, where manual classification is time-consuming, error-prone, and unscalable, consuming significant project effort as reported by industry surveys. The urgent need for automated, scalable solutions motivates this research, which proposes a novel integration of advanced machine learning (ML) techniques and a cloud-based architecture to enhance SRC using the PROMISE dataset. Our approach leverages a hybrid cloud–edge deployment strategy, combining the precision of ML models, such as BERT, with dynamic resource allocation to achieve an F1-score of 89.2%, outperforming traditional methods. Key contributions include: (1) a comprehensive evaluation of five ML models for SRC, (2) a novel hybrid cloud–edge architecture balancing performance, latency, and privacy, and (3) a cost–benefit analysis demonstrating cost-effectiveness for enterprise applications. These advancements address scalability and accuracy challenges in requirement engineering, enabling more efficient, consistent, and automated SRC processes, with significant potential for widespread industry adoption.
Cloud computing , Machine learning , Natural language processing , PROMISE dataset , Requirement engineering , Scalable architecture , Software requirement classification
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Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
Department of Computer Science and IT, UET Peshawar (Jalozai Campus), Pakistan
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, 21944, Saudi Arabia
School of Digital Engineering, Narxoz University, Almaty, Kazakhstan
Department of Computer Science
Department of Computer Science and IT
Department of Information Systems
Department of Clinical Laboratories Sciences
School of Digital Engineering
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026