Integrating AI in testing automation: Enhancing test coverage and predictive analysis for improved software quality
Independent Researcher, USA.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(01), 769–782.
Article DOI: 10.30574/wjaets.2024.13.1.0486
Publication history:
Received on 29 August 2024; revised on 05 October 2024; accepted on 08 October 2024
Abstract:
This research explores the integration of Artificial Intelligence (AI) in testing automation, focusing on its ability to enhance test coverage and enable predictive analysis for improved software quality. As software systems become increasingly complex, traditional testing methods often struggle to meet quality demands. This study evaluates various AI techniques, including machine learning and natural language processing, and their applications in generating test cases, optimizing testing processes, and predicting defects. Through empirical case studies from diverse organizations, we demonstrate significant improvements in test coverage and defect detection rates following AI implementation. The findings highlight the efficiency gains and quality enhancements achieved through AI-driven testing, while also addressing challenges such as data dependency, complexity of implementation, and the need for skilled personnel. This research contributes to the understanding of AI's role in software testing and encourages organizations to adopt these technologies for better quality assurance and faster development cycles.
Keywords:
Artificial Intelligence; Testing Automation; Software Quality; Test Coverage; Predictive Analysis
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0