Automated Test Selection for Mobile Device Maintenance Releases
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Solution Overview
Problem
Manual test case selection for mobile communication devices is inefficient, subjective, and lacks scalability due to the high number of maintenance releases (MRs) and candidate tests, leading to inconsistent results and limited fault detection capabilities.
Innovation Solution
An automated system using a knowledge base and recommendation engine that analyzes MRs to prioritize and select tests based on historical data, test histories, and OEM reports, leveraging natural language processing and machine learning to identify relevant and risky tests, ensuring objective and scalable test selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual test case selection is used by subject matter experts, then test selection can be performed with human judgment, but the process becomes subjective, inconsistent, and inefficient for fault detection
Solution Approach 1:
The patent replaces the manual mechanical process of test case selection by subject matter experts with an automated computer-based system. The system uses natural language processing to parse maintenance release information, machine learning models to analyze historical test data and defect patterns, and automated algorithms to generate and prioritize test case selections. This substitution eliminates human subjectivity and inconsistency while improving both fault detection capability and selection efficiency through data-driven objective criteria.
2Reliability
If all candidate tests are executed for each maintenance release, then comprehensive fault detection is achieved, but cost, effort and time constraints become unsustainable
Solution Approach 1:
The patent implements partial action by selecting and executing only a prioritized subset of test cases from the complete candidate pool. The system analyzes maintenance release information to identify changes, then uses machine learning models to predict which test cases are most likely to detect faults related to those changes. This approach executes fewer tests than the complete set while maintaining high fault detection effectiveness by focusing resources on the most relevant and risky test cases.
Solution Approach 2:
The patent changes the parameter of test selection from a static comprehensive approach to a dynamic prioritized approach. The system adjusts test case selection based on varying parameters including the type of maintenance release changes, historical defect patterns, test result histories, and risk assessments. This dynamic parameter adjustment enables the system to optimize the balance between fault detection completeness and certification time for each specific maintenance release scenario.
3Adaptability or versatility
If manual test selection is performed without unified criteria, then flexibility in test choice is maintained, but results become inconsistent across different subject matter experts and maintenance releases
Solution Approach 1:
The patent establishes unified selection criteria based on multiple parameters including change type classification, historical defect data, test result patterns, and risk metrics. These parameters provide consistent objective basis for test case selection across all maintenance releases and users. The system maintains flexibility by allowing parameter weights and thresholds to be adjusted based on organizational priorities and specific release characteristics, thus balancing consistency with adaptability.
Solution Approach 2:
The patent implements feedback mechanisms where test results, defect discoveries, and maintenance release outcomes are fed back into the machine learning models. This feedback loop continuously refines the selection criteria and parameters, improving consistency over time while adapting to new patterns and requirements. The system learns from historical data to optimize the balance between unified criteria and situational flexibility.
Data Source
AI summary
Techniques for automatically selecting device tests for testing devices configured for operation in wireless communication networks, based upon maintenance releases (MRs) received from original equipment manufacturers. When an MR with changes for a device is received, the MR may be analyzed in order to determine what the changes pertain to with respect to the device. The changes may be clustered with respect to requirements for the changes and a knowledge base may be consulted by a recommendation engine in order to determine candidate tests for testing the MR. The candidate tests may be based upon previous tests, failed tests and, relevant tests. Based at least in part on the identified previous tests, failed tests and relevant tests, one or more tests may be selected for testing devices with respect to the newly received MR.