Intelligent Quality Engineering: Leveraging AI-Augmented Pipelines For Next-Generation Software Testing
Keywords:
Artificial intelligence in quality assurance, AI-driven software testing, digital transformationAbstract
The accelerating convergence of artificial intelligence, automation, and digital transformation has redefined how modern enterprises design, deploy, validate, and evolve software systems. Quality assurance, historically perceived as a downstream verification activity, is now being reconceptualized as a continuous, intelligence-driven discipline embedded deeply within digital value chains. This research develops a comprehensive theoretical and analytical framework for understanding how AI-augmented quality engineering reshapes software development life cycles, organizational governance, economic performance, and innovation capacity. Drawing on an extensive synthesis of contemporary scholarship and industry evidence, this study positions AI-enabled testing not merely as a technological upgrade but as a systemic reconfiguration of how reliability, risk, and value are produced in digital enterprises. Central to this investigation is the automation-driven digital transformation blueprint proposed by Tiwari (2025), which articulates how legacy quality assurance architectures can be migrated into adaptive, AI-augmented pipelines that align with modern DevOps and continuous delivery paradigms. Building upon this foundation, the article integrates insights from DevOps maturity research, machine learning-driven defect detection, economic models of AI return on investment, and adoption theory to construct a multidimensional understanding of AI in quality engineering. Through interpretive and comparative analysis, the research identifies how AI-driven testing affects speed, coverage, resilience, and cost structures while also introducing new epistemic, organizational, and ethical challenges. The study further explores how AI-enabled quality engineering interacts with broader socio-technical systems, including software supply chains, autonomous agents, and platform ecosystems. By grounding each analytical layer in the literature, this work provides a theoretically rigorous and practically relevant contribution to the emerging field of AI-driven software assurance.
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