AI-Generated Test Automation for Autonomous Software Verification: Enhancing Quality Assurance Through AI-Driven Testing

Abstract

Test automation must be intelligent, scalable, and efficient due to the growing complexity of
software systems. With the use of machine learning (ML), natural language processing (NLP),
and reinforcement learning (RL), this study offers an AI-Generated Test Automation for
Autonomous Software Verification that maximizes test case creation, defect detection, and
execution speed. The suggested framework reduces execution time (110.7 ms) and resource
use (310.5 MB) while improving test coverage (94.8%), defect detection rate (91.2%), and
correctness (96.7%). The AI-driven method ensures minimal human interaction by automating
the production of test cases, self-healing test scripts, and adapting to changing software
modifications. The Full Model (Base + ML + NLP + RL) is the most effective method, with
98.2% test coverage, 99.4% accuracy, and 95.4 ms execution time, according to performance
comparisons of ML-based, NLP-based, RL-based, and combined AI-driven automation.
According to the ablation study, hybrid AI models perform better than solo techniques in terms
of fault discovery, testing effectiveness, and verification accuracy.