A Comprehensive Analysis of Many Non-Functional Requirement Prediction Techniques based on Machine Learning

Abstract

The most critical and crucial field in computer science is SE(software engineering). NFRs(Non Functional Requirements) are very essential but are often overlooked. NFRs prioritization and prediction is required at large extent. ML(Machine Learning) models shows the efficient way to predict NFRs and offers the better result outcomes as compared to NLP(Natural Language Processing). The focus of the study is to present various techniques to predict NFRs offered by several researchers. The work presented in the study focusing on classification and clustering techniques available to predict NFRs. But most of the researchers have used the Naïve Bayes algorithm. Precision and Recall have been used as their performance measure by most researchers. Scientists have ignored machine learning methods such as Ensemble and Parameter Tuning. The ultimate objective is to determine the various vulnerabilities in predictive techniques based on machine learning NFRs and to draw correct future avenues.