Automated Machine Learning Test System Using Latin Hypercube Sampling
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Solution Overview
Problem
Existing methods for testing software predictive models are inefficient, relying heavily on manual experience and resource-intensive processes to define and evaluate various test configurations, leading to high computational costs and limited ability to identify errors or performance degradations effectively.
Innovation Solution
A system that automatically selects and generates new test configurations using random seed values and distance metric calculations, employing Latin hypercube sampling to ensure a more random distribution of test parameters, allowing for parallel execution and adaptive restarts to optimize the testing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual experience-based test configuration definition is used, then tester expertise can guide testing, but the process becomes resource-intensive and computationally expensive
Solution Approach 1:
The system automatically generates test configurations using Latin hypercube sampling and distance metric calculations, eliminating the need for manual tester intervention. The algorithm self-selects parameter values and combinations, making the testing process autonomous and reducing computational overhead associated with manual configuration management.
Solution Approach 2:
The system transforms the test configuration generation process by changing from manual parameter selection to automated random sampling with distance-based filtering. This parameter change in the generation method improves efficiency while maintaining or enhancing test configuration quality through systematic exploration of the parameter space.
2Reliability
If comprehensive test configurations are generated manually, then test coverage can be improved, but user time and computational cost increase significantly
Solution Approach 1:
The automated system performs test configuration generation and evaluation without requiring user time for manual setup. The algorithm independently explores the parameter space, identifies meaningful test configurations, and executes testing, thereby improving error detection capability while eliminating user time investment.
Solution Approach 2:
The system performs preliminary random sampling and distance metric calculations to pre-select optimal test configurations before actual software testing. This preliminary action ensures comprehensive test coverage is achieved automatically, improving reliability without requiring user time during the testing phase.
3Device complexity
If traditional testing methods are used, then simplicity is maintained, but the ability to identify performance degradations is limited
Solution Approach 1:
The system changes the testing approach by introducing automated parameter sampling and distance metric evaluation, which enhances performance degradation detection capability. While the underlying software remains unchanged, the testing methodology becomes more sophisticated, enabling better identification of performance issues without significantly increasing user-perceived complexity.
Data Source
AI summary
A computing device selects new test configurations for testing software. (A) First test configurations are generated using a random seed value. (B) Software under test is executed with the first test configurations to generate a test result for each. (C) Second test configurations are generated from the first test configurations and the test results generated for each. (D) The software under test is executed with the second test configurations to generate the test result for each. (E) When a restart is triggered based on a distance metric value computed between the second test configurations, a next random seed value is selected as the random seed value and (A) through (E) are repeated. (F) When the restart is not triggered, (C) through (F) are repeated until a stop criterion is satisfied. (G) When the stop criterion is satisfied, the test result is output for each test configuration.