Test Subset Optimization for Shorter Total Test Time
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Solution Overview
Problem
Current testing methods require extensive time and resources without ensuring significant reductions in test duration while maintaining reliability, especially in identifying defective products.
Innovation Solution
A computer-implemented method using linear optimization to determine a reduced subset of tests from a larger set, minimizing total test time by defining a cost function and applying constraints to identify relevant tests, allowing for iterative optimization and consideration of test durations and safety requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a reduced subset of tests is selected to minimize total test time, then productivity increases, but measurement precision deteriorates
Solution Approach 1:
The test suite is segmented into critical and non-critical tests based on their contribution to defect detection. The linear optimization model divides the original test set into different categories, allowing selective execution of only the most essential tests while maintaining adequate defect detection capability.
Solution Approach 2:
The approach changes the parameter of test selection from exhaustive to optimized subset. By introducing weighting factors and reliability thresholds as adjustable parameters, the system can dynamically determine the optimal test subset that balances speed and accuracy requirements.
2Reliability
If all tests are performed to ensure high reliability, then measurement precision improves, but loss of time increases
Solution Approach 1:
Instead of performing all tests (excessive action), the system performs a carefully selected partial set of tests that provides sufficient reliability for the application. The linear optimization determines the minimum necessary test coverage required to achieve acceptable reliability levels.
Solution Approach 2:
The linear optimization model is pre-computed based on historical test data and defect patterns. This preliminary analysis identifies which tests are most valuable for detecting defects, allowing the system to execute only those pre-identified critical tests in real-time production scenarios.
3Productivity
If test variables are optimized using linear programming, then productivity improves, but device complexity increases
Solution Approach 1:
The system replaces complex mechanical or manual test selection processes with a mathematical optimization model. Linear programming algorithms automatically determine the optimal test subset based on objective criteria, eliminating the need for manual analysis and reducing operational complexity despite the mathematical sophistication required.
Data Source
AI summary
A method is for determining a reduced first amount of testing from a second amount of testing. The method includes provision of a binary matrix, and optimization of a cost function, which is dependent on test variables. Each of test of the second amount of testing is associated with a test variable, and the test variable characterizes whether the test is relevant. The cost function defines constraints: a first constraint is defined in that a result of a matrix/vector multiplication must be greater than or equal to a vector comprising only ones. The matrix of the matrix/vector multiplication is a matrix whose entries all have the value one. The entries of the binary matrix are subtracted, and the vector of matrix/vector multiplication is a vector comprising the test variables. A second constraint is defined in that the test variables are binary.