AI-driven mobile and web automation: The CI/CD integration revolution

Amisetty, Venkata Amarnath Rayudu (2025) AI-driven mobile and web automation: The CI/CD integration revolution. World Journal of Advanced Research and Reviews, 26 (2). pp. 3554-3562. ISSN 2581-9615

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Abstract

Artificial intelligence has fundamentally transformed mobile and web automation practices when integrated with modern CI/CD pipelines, creating unprecedented efficiency gains throughout the software development lifecycle. This technical article examines cutting-edge advancements in self-healing test frameworks powered by neural networks that autonomously repair broken test scripts while maintaining exceptional recognition rates across dynamically changing interfaces. The integration of Jenkins within cloud environments enables remarkable scalability improvements through containerized infrastructures, allowing organizations to dramatically reduce test execution time and accelerate deployment cycles. Leading automation frameworks like Testim, Appium, and Functionize leverage sophisticated machine learning algorithms to enhance test stability, enable cross-platform compatibility, and provide autonomous test maintenance. Implementation strategies focusing on hybrid framework adoption, containerized test environments, progressive testing rollouts, and continuous model refinement yield substantial benefits across enterprise organizations. Despite technical challenges involving training data requirements, pipeline scalability, result interpretation, and cross-platform consistency, effective solutions have emerged to address these barriers. Future directions point toward zero-code test generation, predictive quality assurance, self-optimizing pipelines, and federated learning networks.

Item Type: Article
Official URL: https://doi.org/10.30574/wjarr.2025.26.2.2039
Uncontrolled Keywords: Artificial Intelligence; Automation Frameworks; Continuous Integration; Machine Learning; Test Maintenance
Depositing User: Editor WJARR
Date Deposited: 20 Aug 2025 11:33
Related URLs:
URI: https://eprint.scholarsrepository.com/id/eprint/3499