Identifying Non-Functional Requirements from Unconstrained Documents using Natural Language Processing & Machine Language Techniques

Manual identification of Non-Functional Requirements (NFRs) from software documents is often laborious, errorprone, and inconsistent. This paper presents a system that leverages Natural Language Processing (NLP) and Machine Learning (ML) to automate the extraction and classification of NFRs from unstructured documents. The approach supports various formats such as .txt, .pdf, and .docx, and categorizes extracted requirements into standard NFR categories such as performance, security, usability, and maintainability. Our model demonstrates promising accuracy using a labeled data set and provides a user-friendly web interface built with Flask to facilitate input and output visualization. Results confirm the effectiveness of automated NFR detection for enhancing early-stage software requirement engineering.