Automated Extraction of Non‐Functional Requirements From Text Files: A Supervised Learning Approach
M. Sunil Kumar, Ala Harika, C. Sushama, P. Neelima · 2022
Non-functional specifications are crucial in determining the alternative and final implementation criteria to use. Requirement engineering produces successful technology and eliminates system failure is extremely important in earlier software developments. Previous research has suggested that artificial intelligent approaches such as machine learning and text mining show that quality attributes are automatically extracted and classified from text files. We propose a supervised text categorization approach to automatically extract and classify non-functional requirements. To evaluate the accuracy of our approach to achieve interesting results, a well-known dataset is used.