Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Abstract
Software testing plays a vital role in ensuring application reliability and quality. With the increasing complexity of software systems, traditional testing methods face challenges related to efficiency, accuracy, and adaptability. Artificial Intelligence (AI) has emerged as a promising approach to enhance automated software testing by leveraging techniques such as machine learning and natural language processing. This paper presents a comprehensive review of AI-powered automated testing methods, examining their applications, benefits, and challenges. The review aims to provide a deeper understanding of how AI contributes to improving defect detection, reducing testing time, and expanding test coverage. By synthesizing recent research and industry practices, this study offers insights to guide future developments and adoption of AI in software quality assurance.
Keywords:
Keywords – Artificial Intelligence (AI), Automated Software Testing, Defect Detection, Test Coverage, Test Automation, AI-powered Testing Tools
Cite Article:
"A Review of the Effectiveness of AI in Automated Software Testing", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.c11-c25, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506202.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator