Machine Learning-Based Identification as Well as Classification of Functional and Non-functional Requirements

In software engineering, it has become necessary to divide needs into functional and non-functional categories. This article identifies and categorizes functional (F) and non-functional (NF) needs and subclasses of NF requirements. Additionally, the subclasses of NF requirements will be discussed. A chunk of the data was gathered from various resources found on the internet. In this investigation, we began by cleaning the data by employing the normalization processes, and we then moved on to the succeeding steps, which included text preparation and vectorization. The topics covered included confusion matrix, Bag of Words, Term Frequency-Inverse Document, Featurization and Machine Learning Models, ROC and AUC curves, Bi-Grams and n-Grams in Python, and Word2Vec. The early discovery of NFRs allows us to make preliminary design choices. We used machine learning algorithms to detect and categorize both functional and non-functional needs for application software development, based on the user requirements provided to us. This article aims to assist in the application development process to various software professionals, including software developers, software designers, software testers, and so on. This paper is also beneficial for developing software more quickly and delivering it to customers. Taking this step makes it easier to create an SRS document and helps the requirement analysis phase proceed more smoothly by reducing the amount of unneeded labor and complexity.