Artificial Intelligence for Continuous Quality Assurance in Cloud-Native Applications: A Review

Purpose: This research aims to explore the role of Artificial Intelligence (AI) in enhancing quality assurance (QA) processes within cloud-native applications (CNAs). Specifically, it investigates how AI can address the challenges posed by the increasing complexity of cloud-native ecosystems and their associated quality control issues. Methodology: The study adopts a qualitative research approach, involving case studies of organizations implementing AI-driven QA tools in cloud-native environments. Data is collected through interviews with industry experts, analysis of existing AI QA tools, and a review of relevant academic and industry literature. The research focuses on identifying the key AI technologies used, their integration into existing QA processes, and the impact on QA efficiency and accuracy. Findings: The integration of AI in QA processes within CNAs has shown promising results in automating test generation, defect prediction, anomaly detection, and incident response. Machine learning, deep learning, and transformer-based models have enabled advanced functions such as log analysis, future failure prediction, and real-time test script maintenance. These technologies have significantly improved test reliability, coverage, and efficiency, particularly in highly automated and distributed cloud environments. Implications to Theory, Practice, and Policy: From a theoretical perspective, this research contributes to the understanding of how AI can reshape traditional QA methodologies in the context of cloud-native applications. It highlights the need for a shift from manual, static testing approaches to intelligent, adaptive, and continuous QA processes. Practically, the study provides insights for organizations looking to integrate AI into their QA workflows, offering a roadmap for implementation and potential pitfalls. Policymakers can use these findings to guide the development of standards and best practices for AI-driven QA in cloud-native applications, ensuring that performance, resiliency, and efficiency are maintained across distributed cloud environments.