Combinatorial Test Suite Failure Analysis via Weighted Cause Indicators
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Solution Overview
Problem
In complex systems, identifying the specific combination of components or options that cause a failure is challenging due to the numerous possible configurations, making it difficult to predict and fix potential failures effectively.
Innovation Solution
A computer-program product that generates a test suite with test cases to analyze different components and their options, using input weights and failure indications to determine cause indicators, which helps identify the most likely potential cause of a system failure by comparing commonalities in failed test cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a comprehensive test suite is constructed to cover all component combinations, then the reliability of failure identification improves, but the complexity of analyzing test results worsens
Solution Approach 1:
The patent segments the complex test suite analysis into individual test case evaluations. Each test case is analyzed separately to determine its contribution to failures, rather than attempting to analyze all combinations simultaneously. This segmentation allows the system to handle complexity by breaking it into manageable units while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces an intermediary computational model that acts as a mediator between the test suite execution and failure analysis. This model computes weights and probabilities that translate complex test results into interpretable failure causes, serving as an intermediary layer that simplifies the relationship between test configurations and failure outcomes.
2Reliability
If all possible component options are tested, then the completeness of failure detection improves, but the time required for testing worsens
Solution Approach 1:
The patent performs preliminary actions by pre-computing weights for each test condition based on historical data and system knowledge before actual failure analysis. This preliminary weighting allows the system to quickly evaluate which test conditions are most likely to cause failures, enabling faster identification without requiring exhaustive testing of all combinations.
Solution Approach 2:
The patent changes parameters by introducing weight values and probability metrics that transform the testing approach from brute-force enumeration to weighted evaluation. By adjusting and computing these parameters dynamically, the system can focus computational resources on the most probable failure causes, reducing overall testing time while maintaining detection completeness.
3Measurement precision
If detailed analysis of each test condition is performed, then the precision of cause identification improves, but the computational resources required worsens
Solution Approach 1:
The patent applies partial action by focusing computational effort only on the most relevant test conditions rather than analyzing every single test case in detail. The system computes weights and probabilities for all conditions but concentrates detailed analysis on those with highest failure probabilities, achieving high precision while reducing overall computational energy consumption.
Data Source
AI summary
The computing device generates a test suite that provides test cases for testing a system. A test condition in the test suite comprises one of different levels representing different options assigned to a categorical factor for the system. The computing device receives input weights for one or more levels of the test suite. The computing device receives a failure indication indicating a test conducted according to the test cases failed. The computing device determines a plurality of cause indicators based on the input weights and any commonalities between test conditions of any failed test cases of the test suite that resulted in a respective failed test outcome. The computing device identifies, based on comparing the plurality of cause indicators, a most likely potential cause for a potential failure of the system. The computing device outputs an indication of the most likely potential cause for the potential failure of the system.