ML-Based Wireless Test Suite Optimization
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Solution Overview
Problem
Existing testing methods for wireless carrier systems are inefficient, unreliable, and resource-intensive, often leading to false confidence in test results and prolonged testing times due to human error and unnecessary tests, and are difficult to set up and execute, especially for complex systems.
Innovation Solution
Implementing a trained machine learning model to classify and predict the reliability and necessity of tests, using chaos testing and machine learning algorithms to optimize the testing process by skipping redundant tests and increasing the frequency of downstream tests to compensate for unreliable results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a suite of tests is executed to determine operability of a new component, then reliability of testing is improved, but testing time and resource consumption increase
Solution Approach 1:
The machine learning model is trained in advance on historical test data to learn patterns and predict test outcomes. This preliminary training enables the system to make informed decisions about which tests to execute and which to skip, avoiding the need to run all tests while maintaining reliability.
Solution Approach 2:
The system identifies and skips tests that are likely to pass based on ML predictions, allowing the testing process to rush through redundant tests while still executing necessary tests to ensure component operability.
2Reliability
If complex tests are executed to ensure system operability, then testing coverage is improved, but device complexity and resource consumption increase
Solution Approach 1:
The system performs partial testing by selectively executing only the necessary subset of tests based on ML predictions, rather than running the full suite of complex tests. This partial action maintains adequate testing coverage while reducing complexity.
Solution Approach 2:
The machine learning model acts as an intermediary between the test suite and the actual testing process, analyzing historical data and making intelligent decisions about test selection, thereby simplifying the overall testing complexity.
3Reliability
If more tests are executed to determine operability, then testing thoroughness is improved, but productivity decreases
Solution Approach 1:
The ML model performs preliminary analysis of test patterns and component characteristics before the actual testing phase, enabling faster decision-making during deployment and improving overall productivity.
Solution Approach 2:
The system skips redundant tests that would slow down the deployment process, rushing through the testing phase by executing only the critical subset of tests needed to ensure component operability.
4Adaptability or versatility
If human contractors conduct tests, then testing flexibility is improved, but user error and reliability decrease
Solution Approach 1:
The system performs testing autonomously using the ML model to predict outcomes and select tests, eliminating human contractors from the execution process. This self-service approach removes human error while maintaining flexibility through automated decision-making.
Solution Approach 2:
The patent replaces the mechanical human-operated testing process with an automated ML-based system, substituting human judgment and manual execution with algorithmic predictions and automated test selection.
Data Source
AI summary
Described herein are techniques, devices, and systems for using a machine learning model(s) and/or artificial intelligence algorithm(s) to optimize testing of components of a system operated by a wireless carrier. For example, data generated as a result of executing a first test of a suite of tests may be provided as input to a trained machine learning model(s) to classify one or more tests of the suite of tests as having a particular characteristic. A to-be-executed test may be classified as likely to pass or likely to fail when executed, for example. An already-executed test may be classified as reliable or unreliable, as another example. Based on the classification of the test(s), the suite of tests may be modified to optimize testing of the wireless carrier's system.