Abstract

This article presents a review of emerging trends in software quality assurance (QA) automation, with a focus on the transformative role of artificial intelligence (AI) and machine learning (ML). Traditional test automation, reliant on static scripts and predefined logic, is increasingly challenged by the speed and complexity of modern software development. AI-driven test strategies are bridging this gap through intelligent test generation, adaptive execution, test prioritization, and smart defect analysis. We explore real-world applications such as self-healing tests, generative unit tests, predictive test selection, and AI-guided defect triage. Empirical data from industry surveys and research studies is used to quantify efficiency gains, improved test coverage, and faster defect resolution. We also address adoption challenges, including skill gaps, data quality issues, and trust in AI recommendations. The article concludes with a forward-looking perspective on the evolving role of testers and the growing synergy between human expertise and AI assistance in delivering scalable, efficient, and high-quality software testing. These insights aim to guide practitioners and researchers in understanding and leveraging AI’s full potential in QA automation.

Keywords

  • AI in QA
  • Test Automation
  • Self-Healing Tests
  • Generative AI
  • Test Case Prioritization
  • Machine Learning in Testing
  • Quality Engineering
  • Autonomous Testing
  • Software Testing Tools
  • QA Optimization

References

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