International Journal of Contemporary Research in Multidisciplinary

Developing complex software systems often involves managing intricate requirements that must be accurate and unambiguous. Traditional methods for analyzing and validating software requirements can be labor-intensive and error-prone. This paper explores the use of semantic similarity analysis to enhance the clarity and effectiveness of software requirements and design. By applying advanced natural language processing (NLP) techniques, such as word embeddings, sentence embeddings, and transformer-based models, our approach aims to improve the identification of inconsistencies, ambiguities, and redundancies in requirement documents. The paper presents a methodology for integrating semantic similarity analysis into the requirements engineering process and demonstrates its benefits through a series of case studies. The results highlight the potential of semantic similarity to streamline requirements management and contribute to more effective software design.