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The rapid evolution of software systems has made traditional testing methods unsuitable to provide quality, speed, and responsiveness to applications in modern development. This paper presents a detailed overview of artificial intelligencebased self-healing test automation frameworks capable of automatically adapting to evolving applications and reducing human involvement. The frameworks utilize machine learning, anomaly detection, natural language processing, and reinforcement learning to develop, execute, and change test scripts. By maximizing test coverage and premonitory identification of defects, AI enhances fault tolerance, scalability, and dependability in CI/CD infrastructures. During this study, peer-reviewed scholarly papers released from 2021 through 2025 are evaluated comprehensively to identify the impact, implementation process, challenges, and emerging patterns of autonomous quality testing. The results reveal that frameworks using AI do not just enhance testing automation but also resilient, adaptive, and smart approaches of testing which suit dynamic scenarios. The research confirms AI's transformative role in modern software testing, offering substantial gains in efficiency, performance, and reliability across various software domains.
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Abhichandani et al. (2025) studied this question.
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