Constraint Solver Test Configuration Selection
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Solution Overview
Problem
The challenge lies in efficiently selecting a minimal set of test configurations that satisfy a desired coverage strength for testing software or hardware applications, given a large number of parameters and candidate values, while ensuring comprehensive coverage without testing every possible combination.
Innovation Solution
A data model is generated for application to a constraint solver, formulating the selection problem as a set of constraints to minimize the number of test configurations while ensuring each interaction is covered, using a column generation algorithm to iteratively determine the optimal subset of test configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If every possible combination of test configurations is used, then complete coverage of all parameter interactions is achieved, but the number of tests becomes excessively large and impractical
Solution Approach 1:
The patent segments the complete set of test configurations into a minimal subset that still satisfies the desired coverage strength. By dividing the full configuration space into manageable groups and selecting only the essential ones, the system achieves comprehensive coverage without executing all possible tests, thus resolving the contradiction between reliability and productivity.
Solution Approach 2:
The patent makes each selected test configuration serve multiple coverage purposes simultaneously. By carefully selecting configurations that cover multiple parameter interactions, the system maximizes the coverage efficiency of each test, reducing the total number of tests needed while maintaining complete coverage of all required interactions.
2Productivity
If a minimal set of test configurations is selected, then the number of tests is reduced for practicality, but ensuring comprehensive coverage becomes more difficult
Solution Approach 1:
The patent introduces constraint solvers and data models as intermediary tools that automatically handle the complex selection process. These intermediaries translate the coverage requirements into mathematical constraints and solve for the minimal test set, eliminating the need for manual complex analysis while ensuring comprehensive coverage is achieved.
Solution Approach 2:
The patent replaces manual test configuration selection with an automated computational system. By substituting human judgment and manual analysis with constraint solving algorithms, the system efficiently handles the complexity of selecting minimal test sets while guaranteeing coverage requirements are met, thus reducing both the number of tests and the complexity of the selection process.
3Reliability
If the complete set of candidate test configurations is used, then all parameter combinations are covered, but the testing process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent performs preliminary analysis by formulating data models and constraints before actual testing begins. By pre-calculating the minimal test set that satisfies coverage requirements using constraint solvers, the system eliminates unnecessary tests beforehand, ensuring complete coverage while significantly reducing the time and resources needed during the actual testing execution.
Data Source
AI summary
Techniques for selecting test configurations associated with a particular coverage strength using a constraint solver are disclosed. A set of parameters are configurable for conducting a test on a particular target application. A data model generator identifies one or more candidate test configurations based on the set of parameters. The data model generator determines a set of interactions based on a desired coverage strength. The data model generator specifies a selection variable indicating the candidate test configurations that are selected for testing the particular target application. The data model generator specifies constraint(s) minimizing the number of selected test configurations. The data model generator specifies constraint(s) requiring that each interaction be covered by at least one selected test configuration. The data model, including the selection variable and the constraints, are input to a constraint solver to obtain a set of selected test configurations associated with the desired coverage strength.