AI in QA: Transforming test automation and software quality through intelligent solutions

Artificial intelligence is revolutionizing quality assurance processes in the rapidly evolving software development landscape, offering unprecedented enhancements to test automation and overall software quality. This technical article explores the transformative impact of AI across multiple dimensions of QA, including test case generation based on user behavior analytics, self-healing test automation frameworks that adapt to UI changes, advanced defect prediction systems that identify high-risk code modifications, and computer vision applications for visual regression testing. The article provides a comprehensive analysis of current capabilities and implementation strategies by examining industry-leading tools such as Testim, Applitools, Selenium with Healenium, and SonarQube with AI anomaly detection; the discussion culminates in a real-world enterprise case study demonstrating significant efficiency improvements, offering readers practical insights for integrating AI-powered testing methodologies into their development workflows.

Authors

Publication Details

Journal
World Journal of Advanced Engineering Technology and Sciences
Published
2025-04-11
DOI
https://doi.org/10.30574/wjaets.2025.15.1.0244
Primary Topic
Software Testing and Debugging Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

AI in QA: Transforming test automation and software quality through intelligent solutions

Ajay Seelamneni
World Journal of Advanced Engineering Technology and Sciences
Software Testing and Debugging Techniques
article

AI in QA: Transforming test automation and software quality through intelligent solutions

Ajay Seelamneni
article en

Abstract

Artificial intelligence is revolutionizing quality assurance processes in the rapidly evolving software development landscape, offering unprecedented enhancements to test automation and overall software quality. This technical article explores the transformative impact of AI across multiple dimensions of QA, including test case generation based on user behavior analytics, self-healing test automation frameworks that adapt to UI changes, advanced defect prediction systems that identify high-risk code modifications, and computer vision applications for visual regression testing. The article provides a comprehensive analysis of current capabilities and implementation strategies by examining industry-leading tools such as Testim, Applitools, Selenium with Healenium, and SonarQube with AI anomaly detection; the discussion culminates in a real-world enterprise case study demonstrating significant efficiency improvements, offering readers practical insights for integrating AI-powered testing methodologies into their development workflows.

World Journal of Advanced Engineering Technology and SciencesVol. 15(1)
Openalex Percentile: Top 93%
Software Testing and Debugging Techniques
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AI in QA: Transforming test automation and software quality through intelligent solutions — Ajay Seelamneni · World Journal of Advanced Engineering Technology and Sciences (2025) | TGRS Research Map | TGRS