Quantitative Network Testing Framework for 5G APIs
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Solution Overview
Problem
Traditional network testing mechanisms are inadequate for testing the advanced use cases and applications supported by 5G networks and their APIs, particularly in simulating user interactions and validating responses in a deterministic and quantitative manner.
Innovation Solution
A framework for quantitative network testing that involves generating testing scripts parameterized with historical data to simulate user interactions with network services and APIs, analyzing responses, and iteratively refining the testing process using a data diffusion process to ensure comprehensive and efficient testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network testing mechanisms are used, then testing can be performed with simple methods, but testing coverage and determinism are insufficient for 5G network services and APIs
Solution Approach 1:
The testing framework is divided into distinct functional modules: test script generator, test executor, response analyzer, and data diffuser. Each module handles specific aspects of the testing process, enabling deterministic and comprehensive testing of 5G network services while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The testing framework dynamically adapts its behavior based on test results. The data diffuser modifies input data for subsequent testing cycles based on analyzed responses, and the test script generator creates parameterized scripts that adapt to different 5G service scenarios. This dynamic adjustment ensures deterministic coverage of various network conditions.
2Reliability
If comprehensive testing of all network conditions is performed, then testing coverage is improved, but testing time and redundancy increase
Solution Approach 1:
The framework performs partial testing in each cycle by using the data diffuser to selectively modify input data based on previous results. Rather than exhaustively testing all possible conditions in every cycle, it focuses on untested or poorly tested areas, reducing redundant testing while maintaining comprehensive coverage over multiple iterations.
Solution Approach 2:
The response analyzer examines test results and provides feedback to the data diffuser, which adjusts input data for subsequent cycles. This feedback loop enables the system to learn from previous testing cycles and efficiently direct future testing toward untested conditions, minimizing redundancy and reducing overall testing time.
3Reliability
If the same testing data is reused across testing cycles, then testing consistency is maintained, but untested conditions and functions remain undetected
Solution Approach 1:
The data diffuser systematically modifies testing parameters and input data between cycles based on analysis of previous results. By changing data parameters strategically, the framework discovers untested conditions and functions while maintaining deterministic testing behavior, thereby improving both condition coverage and testing efficiency.
4Extent of automation
If manual testing approaches are used, then flexibility in test design is maintained, but automation and repeatability are reduced
Solution Approach 1:
The test script generator automatically creates parameterized test scripts based on service definitions and historical data, eliminating the need for manual script creation. The framework self-manages the entire testing workflow from script generation through execution and analysis, providing high automation while keeping the framework structure manageable through modular design.
Data Source
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AI summary
The present disclosure provides a framework for quantitatively testing network services and their supporting APIs. To facilitate the testing, a testing script may be generated. The testing script may be parameterized with input data derived from a historical database. The testing script may be executed and responses received from the network during testing may be analyzed to determine metrics associated with execution of the testing script. The metrics may indicate successfully tested conditions, unsuccessfully tested conditions, untested conditions, other testing metrics, or a combination thereof. After the initial testing cycle a determination is made regarding whether a stop condition has been satisfied and if it has not been satisfied, another testing cycle may be executed. The next testing cycle may utilize a different set of input data that may be dissimilar to the input data used in the previous testing cycle so that additional conditions and functionality may be tested.