Title: Machine learning-based software requirements identification for a large number of features

Authors: Pratvina Talele; Rashmi Phalnikar

Addresses: School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, India ' School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, India

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.

Keywords: software requirements; machine learning.

DOI: 10.1504/IJCSYSE.2021.123553

International Journal of Computational Systems Engineering, 2021 Vol.6 No.6, pp.255 - 260

Received: 05 Jul 2021
Accepted: 18 Aug 2021
Published online: 27 Jun 2022 *

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