Novel artificial intelligence-based ensemble learning for optimized software quality
subject: Accuracy; Artificial intelligence; Defect density; Software engineering; Software quality
description: Artificial intelligence (AI) contributes towards improving software engineering quality; however, existing AI models are witnessed to deploy learning-based approaches without addressing various complexities associated with datasets. A literature review showcases an unequilbrium between addressing the accuracy and computational burden. Therefore, the proposed manuscript presents a novel AI-based ensemble learning model that is capable of performing an effective prediction of software quality. The presented scheme adopts correlation-based and multicollinearity-based attributes to select essential feature selection. At the same time, the scheme also introduces a hybrid learning approach integrated with a bio-inspired algorithm for constructing the ensemble learning scheme. The quantified outcome of the proposed study showcases 65% minimized defect density, 94% minimized mean time to failure, 62% minimized processing time of the algorithm, and 43% enhanced predictive accuracy. 2025, Institute of Advanced Engineering and Science. All rights reserved.
creator: Govinda, Sangeetha; Vincent, Agnes Nalini; Babu, Merwa Ramesh
source: IAES International Journal of Artificial Intelligence;Volume;14;Issue;3;pp.1820-1828
publisher: Institute of Advanced Engineering and Science
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 20894872;
format: online
language: English
type: Article
coverage: Govinda S., Department of Computer Science, Christ University, Bangalore, India; Vincent A.N., Faculty of Information Technology, AMITY Institute of Higher Education, Quatre Bornes, Mauritius; Babu M.R., Department of Computer Science, Bharathiar University, Coimbatore, India