Test Sequence Optimization for Stop-at-First-Fail
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Solution Overview
Problem
Stop-at-first-fail testing in manufacturing and software development is costly due to the inefficiencies in determining the optimal sequence of tests, which often results in unnecessary resource expenditure and prolonged testing times, despite efforts to prevent redundant testing.
Innovation Solution
A computer-implemented method and system for determining a test sequence that optimizes the order of tests by using test data instances and cost values, accounting for dependencies between tests, and employing graph optimization algorithms or solvers with black-box functionality to minimize expected costs, thereby reducing the overall testing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If tests are performed in a fixed sequential order, then the testing process is simple to implement, but the average test cost and time increase due to unnecessary expensive tests being performed on defective instances
Solution Approach 1:
The test sequence is made dynamic by using optimization algorithms to determine the optimal order based on test costs, failure probabilities, and dependencies. The system calculates expected costs for different test sequences and dynamically adjusts the order to minimize average testing time, rather than using a static fixed sequence.
Solution Approach 2:
The invention changes the parameters of the testing system by introducing cost values and failure probabilities as key parameters. These parameters are used in optimization algorithms to determine the test sequence, transforming the system from a simple sequential approach to a parameter-driven optimized approach.
2Reliability
If expensive tests are performed early in the sequence, then comprehensive quality checking is achieved, but resource expenditure increases and testing efficiency decreases
Solution Approach 1:
The system performs preliminary analysis of test costs, failure probabilities, and dependencies before determining the optimal sequence. By calculating expected costs in advance using optimization algorithms, the system prepares the most efficient test order beforehand, ensuring that expensive tests are positioned optimally rather than arbitrarily.
Solution Approach 2:
The invention uses feedback from historical test data and failure probabilities to continuously improve the test sequence. The system monitors actual test outcomes and uses this information to refine the optimization parameters, creating a feedback loop that enhances both reliability and resource efficiency over time.
3Reliability
If all tests are performed on every product instance, then complete quality assurance is achieved, but testing costs and time increase significantly
Solution Approach 1:
The system applies local quality by tailoring the test sequence to each specific product instance based on its characteristics and failure probabilities. Instead of applying the same comprehensive test suite to all instances, the optimization algorithm determines the locally optimal sequence for each case, ensuring adequate quality assurance while improving throughput.
4Loss of time
If test sequence optimization is performed using complex algorithms, then the average test cost is reduced, but the complexity of the testing system increases
Solution Approach 1:
The invention introduces an intermediary optimization layer that sits between the test definitions and the actual test execution. This intermediary component handles the complex optimization calculations using algorithms that consider costs, failures, and dependencies, while presenting a simplified interface to the rest of the testing system. The intermediary absorbs the complexity, allowing the core testing functionality to remain relatively simple.
Data Source
AI summary
A computer-implemented method for determining a test sequence. The test sequence defines a sequential order of performing multiple tests on an instance of a product using stop-at-first-fail. One or more test data instances are accessed. A test data instance represents respective outcomes of the respective tests for an instance of the product. Test cost values are accessed representing costs of performing the respective tests. An optimization is performed of the sequential order. The optimization is configured to minimize an expected cost of performing the multiple tests according to the sequential order using stop-at-first-fail. The expected cost is determined from the one or more test data instances and the test cost values. Data is output representing the determined sequential order of the test sequence.