Automation-Driven Cloud-Native Quality Engineering: Reconfiguring Legacy Testing Architectures Through AI-Augmented Digital Transformation

Abstract

The contemporary enterprise software landscape is undergoing an unprecedented phase of structural reconfiguration driven by the convergence of artificial intelligence, cloud computing, platform engineering, and automation-centric operational models. Quality assurance, historically positioned as a downstream validation activity, is being repositioned as a continuous, intelligence-driven control layer embedded across digital delivery pipelines. This transformation has been accelerated by the increasing inadequacy of legacy quality assurance architectures to cope with the scale, velocity, heterogeneity, and regulatory complexity of cloud-native systems. Against this background, this research develops a comprehensive analytical framework for understanding how automation-driven digital transformation reshapes quality engineering when legacy testing ecosystems are migrated into AI-augmented, cloud-native pipelines. Drawing upon the theoretical foundations of socio-technical systems, digital transformation theory, and cloud service models, the study integrates insights from industry reports, governance frameworks, and emerging scholarly work. A central conceptual anchor is the automation-centric transformation blueprint articulated by Tiwari (2025), which positions artificial intelligence not merely as a tool but as an architectural principle for modernizing quality operations across enterprise delivery ecosystems.

Through an interpretive synthesis of cloud migration literature, DevOps maturity models, platform engineering research, and cloud-native security analyses, this article demonstrates that AI-augmented quality engineering constitutes a paradigmatic shift from procedural testing toward adaptive, predictive, and self-optimizing verification systems. The methodology employs qualitative analytical modeling and cross-source triangulation to examine how organizations operationalize this shift across infrastructure, organizational governance, and risk management. The results show that AI-driven automation enables continuous quality intelligence, while cloud platforms provide the elasticity and observability required to operationalize such intelligence at scale. However, the findings also reveal deep structural tensions between legacy control models and algorithmic governance, particularly in regulated and safety-critical environments.

The discussion situates these findings within broader debates on digital transformation success, platform engineering maturity, and cloud security governance. It argues that sustainable quality modernization requires the co-evolution of technical architectures, organizational capabilities, and ethical oversight mechanisms. By positioning quality engineering as a strategic function within digital enterprises, the article contributes a theoretically grounded, empirically informed framework for guiding future research and enterprise practice in AI-enabled cloud transformation.