Abstract:

Advances in AI-driven test automation frameworks are leading the way for enhanced efficiency, accuracy, and adaptability in testing processes. The paper is based on the synergy of artificial intelligence with test automation, focusing on theoretical underpinnings, technological enablers, and architectural designs. Algorithmic techniques include machine learning, neural networks, and decision trees, and their application in the generation, execution, and debugging of test cases is analyzed. Secondly, we talk over performance metrics, challenges, and future trends in order to depict profound capabilities of AI towards transforming the methodologies of modern software development.

Description:

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