Automation Driven Digital Transformation Of Legacy Quality Assurance Into AI Augmented Testing Pipelines: A Multidisciplinary Systems Engineering Perspective

Abstract

The contemporary software industry is undergoing a profound transformation driven by the convergence of automation, artificial intelligence, and large-scale digitalization initiatives. Among the most affected domains is quality assurance, which historically relied on manual, rule-based, and labor-intensive testing practices rooted in legacy system architectures. As organizations increasingly migrate from monolithic systems to cloud-native, microservice-oriented, and data-intensive platforms, traditional quality assurance frameworks struggle to provide adequate coverage, speed, and reliability. The emergence of AI-augmented testing pipelines introduces not merely incremental efficiency gains but a paradigmatic reconfiguration of how software quality is conceptualized, governed, and operationalized. This study develops a theoretically grounded and empirically informed analysis of how automation-driven digital transformation enables the migration of legacy quality assurance into intelligent, self-adaptive testing ecosystems.

Anchored in the automation-driven digital transformation blueprint proposed by Tiwari (2025), this article constructs a comprehensive interpretive framework that integrates software engineering, systems theory, organizational change, and data-driven decision-making. The analysis synthesizes contributions from automation studies, artificial intelligence in testing, microservices migration, defect prediction, reinforcement learning, and master data management to reveal how AI does not merely automate existing processes but fundamentally redefines the epistemology of software quality. The paper demonstrates that AI-augmented testing is not a technological overlay on legacy practices but a structural realignment in which data, models, and feedback loops replace scripts, heuristics, and static test plans.

Using an interpretive qualitative methodology grounded in literature-based analytical synthesis, this study traces how legacy quality assurance emerges from historical software engineering paradigms and why these paradigms collapse under modern digital complexity. It then articulates how AI-driven pipelines reconstruct quality assurance as a continuous, predictive, and self-optimizing system. Particular attention is given to the role of risk management, master data integrity, microservice scalability, and federated learning in enabling autonomous testing environments. The results show that organizations adopting AI-augmented testing achieve not only improved defect detection and cost efficiency but also enhanced epistemic control over software reliability.

The discussion situates these findings within broader scholarly debates on automation, human–machine collaboration, and digital transformation. It critically evaluates concerns regarding transparency, trust, and governance while arguing that these challenges are best understood as design and institutional issues rather than inherent limitations of AI-based testing. The paper concludes that the future of quality assurance lies in the strategic orchestration of automation, artificial intelligence, and organizational learning, transforming testing from a reactive validation function into a proactive, intelligence-driven pillar of digital enterprise architecture.