ItemArticle

Novel preemptive intelligent artificial intelligence-model for detecting inconsistency during software testing

Title
Novel preemptive intelligent artificial intelligence-model for detecting inconsistency during software testing
Creator
Govinda, Sangeetha; Prasanthi, B.G.; Vincent, Agnes Nalini
Description
The contribution of artificial intelligence (AI)-based modelling is highly significant in automating the software testing process; thereby enhancing the cost, resources, and productivity while performing testing. Review of existing AI-models towards software testing showcases yet an open-scope for further improvement as yet the conventional AI-model suffers from various challenges especially in perspective of test case generation. Therefore, the proposed scheme presents a novel preemptive intelligent computational framework that harnesses a unique ensembled AI-model for generating and executing highly precise and optimized test-cases resulting in an outcome of adversary or inconsistencies associated with test cases. The ensembled AI-model uses both unsupervised and supervised learning approaches on publicly available outlier dataset. The benchmarked outcome exhibits supervised learning-based AI-model to offer 21% of reduced error and 1.6% of reduced processing time in contrast to unsupervised scheme while performing software testing. 2025, Institute of Advanced Engineering and Science. All rights reserved.
Source
IAES International Journal of Artificial Intelligence;Volume;14;Issue;3;pp.1781-1789
Date
01-01-2025
Publisher
Institute of Advanced Engineering and Science
Subject
Artificial intelligence; Automation; Error; Inconsistency; Software testing
Coverage
Govinda S., Department of Computer Science, Christ University, Bangalore Central Campus, Bangaluru, India; Prasanthi B.G., Department of Computer Science and Applications, St. Josephs University, Bangaluru, India; Vincent A.N., Faculty of Information Technology, AMITY Institute of Higher Education, Quatre Bornes, Mauritius
Rights
All Open Access; Gold Open Access
Relation
ISSN: 20894872;
Format
online
Language
English
Type
Article

Citation

Govinda, Sangeetha; Prasanthi, B.G.; Vincent, Agnes Nalini, “Novel preemptive intelligent artificial intelligence-model for detecting inconsistency during software testing,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/23074.

Output Formats