An Inclusive Study of Several Machine Learning Based Non functional Requirements Prediction Techniques
Requirement Engineering is a critical area in the arena of Software Engineering. Non Functional Requirements (NFRs) are very important as Functional Requirements but often ignored. Especially the automated correct prediction and prioritization of Non Functional Requirements (NFRs) is highly needed. Non Functional Requirements can be predicted with the help of machine learning models. The Machine Learning models have shown more promising output than Natural Language Processing. In this paper, the comprehensive review on the recently proposed NFRs prediction techniques has been presented. The output of the review showed that the different authors have used different Clustering and Classification algorithms. But Naive Bayes algorithm has used by most of the researchers. The most of researchers have used Precision and Recall as their performance metric. Machine learning techniques like Ensembling and Parameter Tuning has neglected by researchers. The overall objective is to evaluate the numerous shortcomings in machine learning based NFRs prediction techniques and to draw suitable future directions.