Effortless Test Maintenance: A Critical Review of Self-Healing Frameworks

Abstract

As the intricacy of web applications continues to escalate, the difficulties linked with the upkeep of testing in web-based automation have grown more apparent. Conventional automation scripts frequently encounter difficulties in adjusting to frequent modifications in web components, resulting in substantial manual endeavours and diminished testing efficacy. In reaction to these challenges, self-correcting web-based automation frameworks have emerged as a promising resolution. This critical review paper offers a comprehensive examination of web-based automation frameworks that have the ability to selfheal. It provides a thorough analysis of their principles, mechanisms, and real-world applications. Through the categorization and analysis of these frameworks' essential components, this review aims to elucidate their effectiveness in addressing the continuously evolving nature of web applications.While acknowledging the advancements made possible by self-healing frameworks, this review also explores the inherent challenges and limitations they possess. It outlines potential areas for future research and highlights emerging trends, such as the integration of artificial intelligence and machine learning, which have the potential to further enhance the self-healing capabilities of these frameworks. In summary, this evaluative critique offers a valuable asset for researchers, practitioners, and organisations aiming to optimize the process of managing tests in web-based automation. Through comprehending the fundamental concepts and potential compromises of self-healing frameworks for web-based automation, testing teams can strive towards effortless test maintenance and enhanced quality of web applications

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“…This best practice, centered on a strategic framework, empowers organizations to effectively utilize AI in test automation. By combining clear objectives, comprehensive team training, strategic tool selection, robust data management, continuous improvement, and cross-functional collaboration, organizations can optimize the impact of AI on testing processes, resulting in enhanced efficiency, coverage, and reliability [5].…”