Box Testing

Box testing, a crucial aspect of software and machine learning model validation, focuses on evaluating system behavior without internal knowledge, aiming to uncover vulnerabilities and ensure reliability. Current research emphasizes developing effective black-box test strategies for diverse models, including transformer networks in natural language processing and adaptive neural networks in resource-constrained environments, often employing techniques like reinforcement learning, game theory, and input diversity metrics to improve test suite generation and efficiency. These advancements are significant for enhancing the trustworthiness and robustness of complex systems across various domains, from autonomous vehicles to web application security.