Network Test Data Collection for Generative AI Test Configuration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network testing systems lack efficient methods for collecting generative artificial intelligence training data to configure and optimize network testing, leading to suboptimal performance and security vulnerabilities.
Innovation Solution
A network test system with a generative AI model training data collector that integrates with network test components to capture and process data for training, utilizing federated learning to create a model capable of generating network tests based on natural language inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional network testing methods are used, then the system can execute basic test cases, but the system lacks the capability to efficiently collect and process training data for AI model configuration
Solution Approach 1:
The patent combines the training data collector functionality with the existing network test controller into a single integrated system. The training data collector is implemented as a software module that runs on the same hardware platform as the test controller, merging data collection, processing, and AI model training capabilities into one unified system architecture.
Solution Approach 2:
The test controller is designed to perform multiple functions: executing traditional network test cases, collecting training data through the integrated training data collector, processing the collected data, and supporting AI model configuration. This multi-functional approach allows the system to serve both conventional testing needs and emerging AI-driven testing requirements.
2Reliability
If manual network testing configuration is used, then the system can operate with simple tools, but the system performance and security are suboptimal
Solution Approach 1:
The system implements feedback mechanisms where test results and system performance data are continuously collected and fed back into the AI model for retraining and optimization. The training data collector gathers information about test outcomes, system configurations, and performance metrics, which are then used to improve future test case generation and configuration recommendations.
Solution Approach 2:
The AI model enables the system to automatically generate and configure test cases based on learned patterns from training data, reducing dependence on manual configuration expertise. The system can autonomously identify testing requirements, generate appropriate test cases, and optimize testing parameters without constant human intervention.
3Measurement precision
If comprehensive training data is collected from all network components, then the AI model can be highly accurate, but the data collection process becomes complex and resource-intensive
Solution Approach 1:
The training data collection process is divided into distinct segments or modules, each responsible for collecting data from specific network components or test aspects. The training data collector is implemented as a software module that can independently collect, process, and manage training data without requiring complete system reconfiguration.
Solution Approach 2:
The training data collector acts as an intermediary layer between the network test components and the AI model. It collects raw data from various sources, processes and standardizes the data format, and prepares it for model training, thereby simplifying the overall data collection process and reducing direct complexity between diverse components and the AI system.
Data Source
AI summary
Methods, systems, and computer readable media for networking testing. In some examples, a system includes a test controller and a training data collector. The test controller is configured for receiving a test case including test case definition information defining a network test for a system under test (SUT); determining test system resource information for test system resources configured to execute the test case; and executing the test case on the SUT. The training data collector is configured for collecting at least a portion of the test case definition information; collecting SUT status information or SUT configuration information or both for the SUT; collecting metadata associated with the test case including at least one test context label; and processing collected data to produce artificial intelligence training data.