Self-Diagnosing Test Suite with Double Pairwise Coverage
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Solution Overview
Problem
Existing fault detection methods require external analysis and resources, making them impractical for edge computing and embedded hardware tests, and they struggle to efficiently generate test cases for complex systems with vast attribute-value combinations.
Innovation Solution
A method for generating augmented test cases using combinatorial testing, where missing counterparts are identified and added to ensure complete n-wise coverage, allowing for automated fault detection without external supplementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If external analysis and resources are used for fault detection, then fault detection capability is improved, but device complexity and resource requirements increase
Solution Approach 1:
The system performs self-diagnosis by generating test cases internally using its own execution results. The fault detection mechanism uses the system's own operational data to create test cases, eliminating the need for external analysis resources while maintaining reliable fault detection capability.
Solution Approach 2:
The system combines multiple functions into a single self-contained mechanism: test case generation, test execution, result analysis, and fault detection all occur within the same system. This multi-functional approach replaces the need for separate external analysis tools and resources.
2Reliability
If comprehensive test cases are generated for all attribute-value combinations, then test coverage is improved, but the number of test cases and time required increase exponentially
Solution Approach 1:
Instead of generating test cases for all possible attribute-value combinations, the system generates test cases only for combinations that are actually observed during system execution. This partial action approach achieves sufficient test coverage while avoiding the exponential growth of test cases associated with comprehensive combinatorial testing.
Solution Approach 2:
The system performs preliminary execution of operations to collect actual attribute-value combinations before generating test cases. This preliminary action ensures that test cases are generated based on real system behavior rather than theoretical possibilities, reducing the total number of test cases needed.
3Measurement precision
If manual test case generation and analysis are performed, then test case accuracy is improved, but labor time and human resources increase
Solution Approach 1:
The system replaces manual mechanical processes with automated computational processes. Test case generation, execution, and analysis are performed automatically by the system using algorithmic processing of execution results, eliminating manual labor while maintaining or improving accuracy through consistent automated analysis.
Solution Approach 2:
The system uses feedback from actual execution results to automatically refine and generate test cases. The feedback loop captures real system behavior and uses it to improve test case accuracy automatically, replacing manual analysis with automated learning from system responses.
Data Source
AI summary
Techniques for testing and fault detection are disclosed. These techniques include generating a set of test cases for a system under test (SUT), the set of test cases based on attribute-value pairs modeled as input to the SUT. The techniques further include augmenting the set of test cases. This includes locating a missing counterpart for a first combination of values in a first test case in the set of test cases, based on identifying a number of instances of the first combination of values in the set of test cases, generating a new test case based on modifying the first test case to act as the missing counterpart, and adding the new test case to the set of test cases. The techniques further include identifying a fault for the SUT based on executing the augmented set of test cases.