AI-driven network configuration and test automation framework: Enhancing feature qualification efficiency while preserving intellectual property

This article presents an AI-driven framework for streamlining network feature qualification by automating the generation of base configurations and test scripts. The framework addresses critical challenges in network testing, where engineers spend substantial time configuring test environments rather than performing actual feature validation. By leveraging advanced machine learning techniques, the system automatically derives optimal network topologies based on features to be tested, generates platform-specific device configurations, creates feature-specific test scripts, and operates within a secure organizational environment to protect intellectual property. The framework's implementation demonstrates significant improvements in time efficiency, configuration accuracy, test coverage, and cross-platform compatibility while reducing dependency on specialized expertise. Through a phased implementation approach, organizations can progressively enhance their testing capabilities, ultimately allowing engineering talent to focus on validating new functionality rather than managing test environments.