HARNESSING AI FOR ROBUST SOFTWARE TESTING: THE FUTURE OF AUTOMATED RELIABILITY AND PERFORMANCE ASSURANCE

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Dr. Min-Jun Lee
Ji-Eun Park

Abstract

In an era where software development cycles are accelerating, ensuring the reliability and performance of applications has never been more critical. Traditional testing methods often struggle to keep up with the pace of development and the increasing complexity of modern software systems. This article explores how Artificial Intelligence (AI) can revolutionize the software testing landscape by automating and optimizing the testing process. We examine AI-driven tools and techniques, including machine learning algorithms, natural language processing, and predictive analytics, which enable faster identification of bugs, vulnerabilities, and performance bottlenecks. The integration of AI in software testing not only enhances test coverage but also improves accuracy and efficiency, leading to more robust software products. By discussing real-world applications, challenges, and future trends, this article provides a comprehensive overview of how AI is reshaping the future of software testing and performance assurance, offering a pathway toward achieving higher quality and more reliable software at scale.

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How to Cite

HARNESSING AI FOR ROBUST SOFTWARE TESTING: THE FUTURE OF AUTOMATED RELIABILITY AND PERFORMANCE ASSURANCE. (2023). Synergy: Cross-Disciplinary Journal of  Digital Investigation (2995-4827), 1(1), 78-98. https://multijournals.org/index.php/synergy/article/view/63