Applying Natural Language Processing for Requirement Analysis and Traceability in Software Engineering

The process of requirement analysis and traceability plays a crucial role in the software development life cycle (SDLC), determining the alignment of a system with user needs, regulatory standards, and functional objectives. Traditional methods of requirement analysis and traceability can be tedious and prone to inconsistencies, leading to gaps in understanding and improper implementation. However, the emergence of Natural Language Processing (NLP) has revolutionized this domain by automating key tasks and enhancing accuracy. This paper explores how NLP techniques can be applied to improve requirement analysis and traceability in software engineering. By processing and interpreting natural language documents, NLP can assist in extracting, organizing, and managing requirements in a systematic and more efficient manner. Furthermore, NLP techniques facilitate better traceability, ensuring that every requirement can be traced throughout the SDLC, from initial specification through design, testing, and deployment. This paper reviews various NLP techniques, including tokenization, semantic analysis, and dependency parsing, that contribute to these tasks. We also discuss the integration of machine learning models with NLP for automated requirement classification, sentiment analysis, and impact analysis. Case studies of successful implementations are examined to highlight the practical benefits of NLP-based systems in real-world software projects. The paper concludes with a discussion on the challenges, such as data ambiguity and model limitations, and future research directions that can lead to more robust and intelligent tools for software requirement engineering. DOI - https://doi.org/10.65525/SVUP.9788199565463.2026.25-30