Title: Multiple correlation based decision tree model for classification of software requirements

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: Recent research in requirements engineering (RE) includes requirements classification and use of machine learning (ML) algorithms to solve RE problems. The limitation of existing techniques is that they consider only one feature at a time to map the requirements without considering the correlation of two features and are biased. To understand these limitations, our study compares and extends the ML algorithms to classify requirements in terms of precision and accuracy. Our literature survey shows that decision tree (DT) algorithm can identify different requirements and outperforms existing ML algorithms. As the number of features increases, the accuracy using the DT is improved by 1.65%. To overcome the limitations of DT, we propose a multiple correlation coefficient based DT algorithm. When compared to existing ML approaches, the results showed that the proposed algorithm can improve classification performance. The accuracy of the proposed algorithm is improved by 5.49% compared to the DT algorithm.

Keywords: machine learning; requirement engineering; decision tree; multiple correlation coefficient.

DOI: 10.1504/IJCSE.2023.131502

International Journal of Computational Science and Engineering, 2023 Vol.26 No.3, pp.305 - 315

Received: 01 Nov 2021
Accepted: 23 Feb 2022
Published online: 15 Jun 2023 *

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