Machine learning-based software requirements identification for a large number of features
by Pratvina Talele; Rashmi Phalnikar
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 6, No. 6, 2021

Abstract: Software is extremely important in today's market. The complexity of software identification is a serious requirement engineering problem. As the number of software requirements (SR) for software increases, conflicts arise in categorising SR and necessitating the use of intelligent techniques to discover and fix inconsistencies. The aim of this study is to compare the existing machine learning (ML) algorithms to understand which of the existing ML algorithms is likely to identify the SR efficiently. Different natural language processing methods are used for text pre-processing phase and term frequency-inverse document frequency is used for feature extraction phase. We employ ML algorithms on the dataset used to identify the requirements and extracted from publicly available SRS and empirically analysed to show that they are successful in identifying SR. Inconsistencies are found and rectified using the different ML methods. Furthermore, our study aids in identifying discrepancies during classification of software requirements.

Online publication date: Mon, 27-Jun-2022

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