End-to-End AI-Powered Automation Testing Framework: Architecture, Implementation, and Empirical Evaluation

Abstract

The rapid evolution of software systems has rendered traditional manual and rule-based test automation frameworks inadequate for modern enterprise-scale applications. This paper presents EATAF (End-to-End AI-Powered Test Automation Framework), a novel multi-layer architecture that integrates machine learning, natural language processing (NLP), computer vision, and self-healing mechanisms to comprehensively automate the software testing lifecycle. EATAF encompasses intelligent test case generation, dynamic test prioritization, autonomous fault localization, and AI-driven reporting. The framework is evaluated across three real-world industrial case studies comprising over 50,000 test scenarios spanning web, API, and mobile application domains. Experimental results demonstrate a 93% improvement in test coverage, a 78% reduction in maintenance effort, an 81% reduction in mean time to detect (MTTD), and a 120% return on investment (ROI) within 12 months compared to conventional automation approaches. Our findings establish EATAF as a transformative paradigm shift in quality assurance engineering.