Req2Vec: Learning Distributed Representation of Non-Functional Software Requirements

Rahman K. Ghani A. Khashan O.A. Alzahrani N. Rashid J. Rahman A.U.
2025 Institute of Electrical and Electronics Engineers Inc.

IEEE Access
2025 #13 202906 - 202918 pp.

Past studies have proposed machine learning-based solutions for classifying software requirements, helping analysts, software architects, and developers efficiently and effectively identify requirement types. A common step performed by these solutions is to represent requirements as meaningful vectors. These vectors are used to identify and classify software requirements. The quality of the vector representations of software requirements determines the effectiveness, efficiency, and performance of the proposed solution. This study introduced a domain-specific pretrained model, Req2Vec, that extracts a distributed representation of non-functional software requirements (NFRs). Req2Vec is an unsupervised, domain-specific pre-trained model trained on thirty million samples of functional and non-functional requirements (FR and NFR). Req2Vec is aware of various requirements, including both functional and non-functional requirements, and seamlessly integrates them to learn representations of requirements. Using a limited supervised dataset, Req2Vec was investigated for various requirement classification tasks, including binary classification, reduced sets of NFR classifications, and all NFR classifications. In binary classification, the Req2Vec model achieved precision, recall, and F1 scores of 0.91, 0.87, and 0.89, respectively. For the reduced set of NFR classification, the average scores are 0.89, 0.85, and 0.87. For all classes, the average scores are 0.83, 0.77, and 0.80. A comparative analysis revealed that Req2Vec yields promising results in advancing technologies, demonstrating its effectiveness in accurately classifying NFRs.

deep learning , natural language process , NFR classifications , Requirement engineering , vector representations

Text of the article Перейти на текст статьи

University of Swat, Department of Computer and Software Technology, Saidu Sharif, 19130, Pakistan
Nazarbayev University, School of Engineering and Digital Sciences, Department of Computer Science, Astana, 010000, Kazakhstan
Rabdan Academy, Research and Innovation Centers, Abu Dhabi, United Arab Emirates
University of Jeddah, College of Computer Science and Engineering, Department of Cybersecurity, Jeddah, 23218, Saudi Arabia
University of Okara, Department of Information Technology Services, Punjab, Okara, 56300, Pakistan
Machine Learning Code Research Laboratory, Punjab, Okara, 56300, Pakistan
Bahria University, Department of Computer Science, Islamabad, 44000, Pakistan

University of Swat
Nazarbayev University
Rabdan Academy
University of Jeddah
University of Okara
Machine Learning Code Research Laboratory
Bahria University

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026