Risk-Controlled Migration of Large-Scale Manual Test Suites to Automation Pipelines Using Retrieval-Augmented Generation

The migration of large-scale manual test suites to automated testing pipelines represents one of the most significant challenges in contemporary software engineering, particularly for organizations with decades of legacy testing assets. This article presents a comprehensive framework for risk-controlled test suite migration leveraging Retrieval-Augmented Generation (RAG) to address the fundamental tension between migration velocity and quality preservation. We synthesize insights from software testing theory, risk management frameworks, and recent advances in generative AI to propose a structured methodology that systematically transforms manual test cases into executable automation scripts while maintaining rigorous quality control. The framework incorporates a multi-phase migration pipeline encompassing test case analysis, transformation planning, automated generation, validation, and continuous improvement. Through three detailed case studies across financial services, healthcare, and e-commerce domains, we demonstrate that RAG-based migration achieves 78% automation coverage with 94% accuracy, reducing migration effort by 65% compared to traditional approaches. The risk-controlled framework successfully identifies and mitigates migration risks across technical, operational, and organizational dimensions, enabling organizations to achieve near-zero regression defect introduction during the transition. Our findings contribute to the emerging body of knowledge on AI-assisted software maintenance and provide practical guidance for practitioners undertaking large-scale test modernization initiatives.