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Artificial Intelligence (AI) is redefining the paradigm of software test automation, offering innovative capabilities that transcend traditional script-based approaches.This article provides an in-depth exploration of AI-powered testing by synthesizing two key sources-(1) a systematic review of 55 AI-assisted test automation tools and (2) empirical evaluations of real-world applications in finance and healthcare.The study highlights major features of AI-driven solutions, such as self-healing scripts, AI-based test generation, and visual AI testing.Empirical evidence suggests that these innovations significantly enhance test coverage, reduce maintenance effort, and improve defect detection rates.Nevertheless, AI-driven testing faces challenges including overgeneration of test cases, limited domain context, and false positives in visual checks.Looking ahead, advances in large language models, deeper predictive analytics, and a -human-in-the-loop model are anticipated to mitigate these constraints, paving the way for more intelligent, context-aware, and trustworthy AI solutions.
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Anatolii Tymoshchuk (2025) studied this question.
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