Cloud-based machine learning for scalable classification of software requirements: Insights from the PROMISE dataset

Hashim Ali, Umer Tanveer, Amir Saeed, Hend Khalid Alkahtani, Khalid J. Alzahrani, Bekarystankzy Akbayan,
Cloud-based machine learning for scalable classification of software requirements: Insights from the PROMISE dataset,
Systems and Soft Computing,
Volume 7,
2025,
200405,
ISSN 2772-9419,
https://doi.org/10.1016/j.sasc.2025.200405.
(https://www.sciencedirect.com/science/article/pii/S2772941925002248)
Abstract: 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.
Keywords: Software requirement classification; Machine learning; Cloud computing; PROMISE dataset; Natural language processing; Scalable architecture; Requirement engineering