ML Error Categorization Engine for IT Testing
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Solution Overview
Problem
The existing communication system testing processes are complex and costly, often resulting in false defect reports that waste developer time and resources, as many reported defects are not actual application issues but rather test data, environment, or test case errors.
Innovation Solution
A machine learning test error categorization engine categorizes error reports as test data, environment, test case, or valid errors, automatically remediating test data errors, notifying for environment issues, and routing error reports to appropriate personnel, while synthesizing new categorization rules from analyzed defects to improve testing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional testing processes are used to thoroughly test communication applications, then testing coverage and reliability are improved, but complexity and cost increase significantly
Solution Approach 1:
The patent replaces manual testing processes with an automated testing system that uses machine learning models to generate test cases, execute tests, and analyze results. This substitution of mechanical/manual operations with automated intelligent systems reduces testing complexity while maintaining or improving reliability through consistent, repeatable test execution and systematic defect analysis.
Solution Approach 2:
The testing system performs self-service through automated test case generation, self-execution of tests, automatic result analysis, and self-reporting of defects. The system independently manages the entire testing workflow without requiring continuous human intervention, thereby reducing operational complexity while ensuring thorough testing coverage through systematic automated processes.
2Reliability
If traditional testing processes are used to thoroughly test communication applications, then testing coverage is improved, but cost increases significantly
Solution Approach 1:
The patent changes key testing parameters by using machine learning models to optimize test case generation and selection. The system dynamically adjusts testing parameters such as test case priority, execution order, and resource allocation based on learned patterns from historical testing data, thereby achieving comprehensive coverage more efficiently and reducing overall testing costs through intelligent parameter optimization.
Solution Approach 2:
The system performs preliminary actions by pre-generating test cases using machine learning models before actual testing begins. The ML models analyze historical defect data and application characteristics in advance to create optimized test suites, which reduces the time and resources needed during actual testing execution while maintaining thorough coverage.
3Reliability
If all error reports are treated as valid defects, then no defects are missed, but developer time is wasted on false positives
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously learns from defect validation outcomes. When developers mark errors as false positives or validate them as real defects, this feedback is fed back into the machine learning models to improve future error classification accuracy. This reduces false positives and saves developer time while maintaining high defect detection accuracy through iterative learning.
Solution Approach 2:
The system introduces an intermediary automated error analysis component between test execution and developer review. This intermediary uses machine learning to pre-filter and categorize errors, providing developers with prioritized, pre-analyzed defect reports. This intermediary layer reduces the time developers spend on false positives while ensuring real defects are accurately identified and presented.
4Measurement precision
If manual error analysis is performed for all test failures, then accurate defect identification is achieved, but productivity decreases
Solution Approach 1:
The patent applies partial action by using automated machine learning-based error analysis for the majority of test failures, reserving manual analysis only for complex or ambiguous cases that the automated system cannot confidently resolve. This partial automation approach maintains high productivity through automated processing while preserving measurement precision by applying human expertise selectively where needed.
Solution Approach 2:
The system replaces manual error analysis with automated machine learning-based analysis for routine test failures. The ML models automatically categorize errors, identify root causes, and generate defect reports, thereby increasing testing productivity significantly while maintaining accurate error identification through sophisticated automated analysis algorithms.
Data Source
AI summary
A method of performing root cause analysis of communication system testing results and submitting communication system defect tickets along with associated previously resolved communication system defect tickets that provide insights for repairing the communication system defects. The method comprises categorizing by a categorization engine error messages in logs received from applications undergoing test, remediating the test data errors and rerunning a test case by a test data error handling script, for each error message categorized as a valid error, searching a data store by a valid error handling script to identify previously resolved defect tickets that match the valid error, and, for each error message categorized as a valid error, generating a defect ticket by the valid error handling script that describes the error message categorized as a valid error and identifies at least some of the previously resolved defect tickets that match the valid error.