Title: A novel scheme of classification for non-functional requirements using CNN with LSTM and GRU new hidden layer

Authors: Devendra Kumar; Anil Kumar; Laxman Singh

Addresses: Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India ' Department of Computer Science, Bundelkhand Institute of Engineering & Technology (B.I.E.T.), Jhansi, Uttar Pradesh, India ' Department of Computer Science (AI & ML), KIET Group of Institutions, Ghaziabad, Uttar Pradesh, India

Abstract: In software development processes, finding the requirement before developing the software is essential. There are two kinds of the requirements in software development: functional requirement and Non-Functional Requirement (NFR). For functional requirement a lot of research work has been done but for NFR very limited research has been done. NFR is critical for software development because it specifies quality and constraints of the system. A critical aspect of analysing NFRs is domain knowledge, expertise and significant human effort, since NFRs are written in natural language. To automate the software requirement classification many ML-based techniques are being developed. In this paper, the proposed CNN model obtained the accuracy, recall, precision, and F1-score of 0.984, 0.99, 0.984, 0.984 and 0.989, 0.99, 0.988, 0.999, performance respectively for BOWs and TF-IDF feature selection techniques. The proposed performance varies with respect to the number of requirement classes, but proposed CNN techniques performed better than the existing machine learning techniques.

Keywords: software specifications; functional requirements; non-functional requirements; hidden layer computation; machine learning; CNN; TFIDF.

DOI: 10.1504/IJGUC.2024.140982

International Journal of Grid and Utility Computing, 2024 Vol.15 No.5, pp.484 - 497

Received: 24 Feb 2023
Accepted: 20 Apr 2023
Published online: 05 Sep 2024 *

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