AI-Augmented Software Testing for Large-Scale Systems: A Comprehensive Framework and Empirical Analysis

Abstract

The exponential growth in software system complexity necessitates innovative testing methodologies that transcend traditional approaches. This paper presents a comprehensive framework for AI-augmented software testing specifically designed for large-scale distributed systems. We introduce a hybrid architecture integrating deep learning models, reinforcement learning agents, and evolutionary algorithms to automate test case generation, execution, and defect prediction. Our empirical evaluation across 15 enterprise-level applications demonstrates a 34.7% improvement in defect detection rates, 42.3% reduction in testing time, and 28.9% increase in code coverage compared to conventional testing frameworks. The proposed system employs transformer-based models for test oracle generation and graph neural networks for dependency analysis. We validate our approach through controlled experiments involving 2.3 million test cases across systems ranging from 500K to 5M lines of code. Results indicate significant improvements in regression testing efficiency, with the AI system identifying 87.6% of critical bugs within the first 20% of test execution time. This research contributes both theoretical foundations and practical implementation strategies for next-generation software quality assurance.