Open Access

ARTICLE

Classification and Comprehension of Software Requirements Using Ensemble Learning

by Jalil Abbas^1,*, Arshad Ahmad^2,*, Syed Muqsit Shaheed³, Rubia Fatima⁴, Sajid Shah⁵, Mohammad Elaffendi⁵, Gauhar Ali⁵

1 School of Computer Science and Technology, Anhui University, Hefei, 230039, China
2 School of Computing Sciences, Pak Austria Fachhochschule, Institute of Applied Sciences and Technology, Haripur, 22620, Pakistan
3 Department of Computer Science and IT, University of Lahore, Lahore, 55150, Pakistan
4 Department of Computer Science, Emerson University, Punjab, Multan, 60000, Pakistan
5 EIAS (Emerging Intelligent Autonomous Systems) Data Science Lab, Prince Sultan University, Riyad, 12435, Saudi Arabia

* Corresponding Author: Jalil Abbas. Email:

(This article belongs to the Special Issue: Requirements Engineering: Bridging Theory, Research and Practice)

Computers, Materials & Continua 2024, 80(2), 2839-2855. https://doi.org/10.32604/cmc.2024.052218

Received 26 March 2024; Accepted 18 June 2024; Issue published 15 August 2024

Abstract

The software development process mostly depends on accurately identifying both essential and optional features. Initially, user needs are typically expressed in free-form language, requiring significant time and human resources to translate these into clear functional and non-functional requirements. To address this challenge, various machine learning (ML) methods have been explored to automate the understanding of these requirements, aiming to reduce time and human effort. However, existing techniques often struggle with complex instructions and large-scale projects. In our study, we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier (FNRC). By combining the traditional random forest algorithm with the Accuracy Sliding Window (ASW) technique, we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer trees. Experimental results demonstrate that our FNRC methodology performs robustly across different datasets, achieving a balanced Precision of 75% on the PROMISE dataset and an impressive Recall of 85% on the CCHIT dataset. Both datasets consistently maintain an F-measure around 64%, highlighting FNRC’s ability to effectively balance precision and recall in diverse scenarios. These findings contribute to more accurate and efficient software development processes, increasing the probability of achieving successful project outcomes.

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Keywords

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