Abstract
This paper focuses on Self-healing automation frameworks enabled by Artificial Intelligence and their application in enhancing Quality Assurance Testing. The mentioned architecture combines machine learning and adaptive techniques to solve several issues that are associated with the use of scripts in traditional testing approaches: the need for script updates, the identification of failure triggers, or the diagnosis of errors. Since these frameworks identify changes in software behavior and modify the testing scripts in real-time, they bring testing benefits of efficiency and effectiveness to the process. Also, the study explains techniques like anomaly detection and automated script modification to depict how these things could be useful for QA teams. In a nutshell, incorporation of AI self- healing automation testing technique helps in minimizing the time and efforts put in testing processes apart from making the whole process way more robust, ready to tackle the challenges of modern-day software development.
Published Version
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