Requirement Engineering has radicalized data analytics by playing a pivotal role in planning requirements strategies and activities. It has emerged as a leading domain of software engineering that branches into 2 categories namely functional requirements (FRs) and non-functional requirements (NFRs). Functional Requirements have gained eminence and prominence while NFRs have always faced a step-sisterly treatment. It is a lit large in the literature review that NFRs are very crucial and never considered appropriately and consequently, the systems have failed. The present study presents an automated system for efficient NFRs prediction. The proposed system consists of 4 layers for the prediction of NFRs significance including Data acquisition layer (DA), feature selection and extraction layer (FSE), data extraction and mining layer (DEM), and data analysis and visualization layer (DAV). Moreover, the current research considers the probability measure of the level of NFR significance in terms of LoNFRS, which is cumulatively quantified as NFRs significance measure (NFRINDEX). NFRINDEX has been quantified for prediction purposes using an convolution neural network (CNN). Additionally, the existence of NFRs is visualized based on a self organized mapping (SOM) procedure. To validate the proposed system, a primary dataset is collected from several IT professionals and academicians. The NFRs dataset of 312 IT professionals and academicians has been gathered for 17 attributes resulting in 5304 instances. Numerous experimental simulations were performed to assess performance in terms of classification, reliability, stability, feature selection, and prediction efficiency.
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