AI-Augmented Test Automation: Transforming Enterprise Quality Assurance Through Natural Language Processing

Abstract

Business systems rely more and more on interdependent microservices, application programming interfaces, and multi-tenant solutions for which exhaustive testing frameworks are mandatory. Legacy automation solutions based on keyword-driven or code-based paradigms necessitate significant technical proficiency and ongoing maintenance investments. As businesses accelerate software delivery cycles and increase cross-platform integrations that cut across cloud infrastructures, enterprise resource planning platforms, and financial applications, legacy testing approaches find it difficult to keep up with contemporary development speeds. Large Language Models offer visionary possibilities for test automation redefinition by allowing quality assurance experts to describe test situations in natural language, which smart systems then translate into executable test scripts. Such a shift in paradigm holds the promise of democratized automation power while overcoming scalability issues that are typical of modern-day enterprise environments. Effective adoption requires thoughtful examination of governance models, validation processes, and human-artificial intelligence partnership models. The article surveys architectural bases for test frameworks that incorporate language models, investigates prompt engineering tactics that increase generation trustworthiness, assesses data governance needs for the safeguarding of sensitive data, and examines human-artificial intelligence collaboration patterns that ensure quality. Experiments illustrate that methodical prompting strategies, contextual augmentation practices, and multi-degree verification strategies considerably enhance the accuracy and trustworthiness of mechanically produced test artifacts without sacrificing critical human intervention for protection-critical packages.