AI Network Function Test Generation for 4G/5G Topology Discovery
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Solution Overview
Problem
Providing accurate and complete test coverage for complex 4G/5G network configurations is challenging due to the complexity of network functions and system configurations, requiring significant computing resources and engineering labor.
Innovation Solution
Utilizing trained AI models to automatically discover network topology and functions, differentiate between real and emulated NFs, and configure them for desired tests, generating packaged configurations for test cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to provide test coverage for network configurations, then test accuracy and completeness can be maintained, but computing resource consumption and engineering labor increase significantly
Solution Approach 1:
The patent replaces manual mechanical processes with AI-based automated systems. The AI model analyzes network configurations, generates test cases, and executes tests automatically, substituting human engineering labor with intelligent automation that achieves the same test coverage accuracy while consuming significantly fewer computing resources and time.
Solution Approach 2:
The system enables self-service testing where the AI model autonomously discovers network topology, identifies network functions, generates appropriate test cases, and executes tests without requiring extensive manual intervention. The system serves itself by automatically adapting to different network configurations and generating tailored test scenarios.
2Reliability
If comprehensive test coverage is provided for complex network functions, then test completeness is improved, but the time and labor required for testing increase
Solution Approach 1:
The AI model performs preliminary analysis of network configurations before testing begins. It discovers the network topology, identifies all network functions, determines their interdependencies, and pre-generates comprehensive test cases in advance. This preliminary action eliminates the need for time-consuming manual analysis during the actual testing process.
Solution Approach 2:
The patent substitutes manual test generation and execution processes with AI-based automated systems that can rapidly analyze complex network configurations and generate comprehensive test cases instantly, dramatically reducing the time required to achieve complete test coverage.
3Productivity
If automated testing is implemented, then testing efficiency is improved, but the complexity of configuring and managing the testing system increases
Solution Approach 1:
The AI model provides self-service functionality by automatically discovering network topology, identifying network functions, generating appropriate test cases, and executing tests without requiring manual configuration of complex testing infrastructure. The system manages its own complexity by autonomously adapting to different network configurations.
Solution Approach 2:
The patent implements a universal AI-based testing platform that can handle multiple network configurations, topologies, and function combinations through a single integrated system. The AI model learns from different network scenarios and generates appropriate tests automatically, eliminating the need for separate complex configuration systems for each network type.
Data Source
AI summary
A network function configuration and test generation framework are configured to generate a test script indicative of a configuration and test cases for verifying a virtual function implemented in a virtualized computing environment. A test input is received that encodes information for verifying a network function. The information includes identification of a test tool, a target testbed for testing the network function, and network information including parameters for network conditions to be operational during testing of the network function. A data parser translates and formats the test input. The formatted test input in input to a topology discoverer configured to identify a network topology of the testbed and which network functions deployed on the testbed are emulated and which network functions deployed on the testbed are real. A configuration generator generates a configuration file usable to configure applicable network functions and their functionalities.