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ABSTRACT
Automated software requirements analysis and classification is a key topic in software engineering. Correct requirement classification is critical for good software development since it aids in organization, priority, and documentation. However, discrepancies in software requirement terminology make this work difficult. For the implementation of this project, Python programming language was used in the Jupyter environment, a Python Streamlit was also used to implement the interface to make it easier to access the model. To improve, keep in mind that finding optimal ways to realize a good automated classification is an ongoing challenge. The findings indicate that the model tackles manual approach limitations and automated classification of software requirements leads to reduced ambiguity, misunderstanding, and development cost.
