Software Requirements Classification using Deep-learning Approach with Various Hidden Layers

Software requirement classification is becoming increasingly crucial for the industry to keep up with the demand of growing project sizes. Based on client feedback or demand, software requirement classification is critical in segregating user needs into functional and quality requirements. However, because there are numerous machine learning (ML) and deep-learning (DL) models that require parameter tuning, the use of ML to facilitate decision-making across the software engineering pipeline is not well understood. Five distinct word embedding techniques were applied to the functional and quality software requirements in this study. The imbalanced classes in the dataset are balanced using Synthetic Minority Oversampling technique (SMOTE). Then, to reduce duplicate and unnecessary features, feature selection and dimensionality reduction techniques are used. Dimensionality reduction is accomplished with Principal Component Analysis (PCA), while feature selection is accomplished with the Rank-Sum Test (RST). For binary categorization into functional and non-functional needs, the generated vectors are provided as inputs to eight distinct Deep Learning classifiers. The findings of the research show that using a combination of word embedding and feature selection techniques in conjunction with various classifiers can accurately classify functional and quality software requirements.