Soft Failure Root Cause Isolation via Performance Vector Comparison
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Solution Overview
Problem
Current testing methods are inefficient in identifying and isolating the root cause of soft failures in systems under test, particularly in complex environments where errors are context-dependent and often masked by environmental conditions, leading to redundant test cases and poor coverage due to manual selection and incremental addition of test cases.
Innovation Solution
A method that generates test cases based on a coverage model, records performance vectors before and after execution on both baseline and system-under-test environments, identifies outlier records, and uses inverse combinatorial test design to isolate the root cause of soft failures, thereby reducing redundancy and improving test efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection and incremental addition of test cases is used, then test coverage is achieved, but test redundancy increases and efficiency decreases
Solution Approach 1:
The patent performs preliminary analysis of the system under test to identify performance parameters and establish baseline performance vectors before test execution. This preliminary characterization enables the system to automatically generate targeted test cases that directly address potential failure modes, eliminating the need for manual incremental addition of test cases while avoiding redundancy.
Solution Approach 2:
The patent implements a feedback mechanism where performance vectors are recorded during test execution and compared against baseline vectors. Outlier detection based on this feedback automatically identifies test cases that cause performance deviations, enabling the system to adaptively select and refine test cases. This feedback-driven approach replaces manual test case selection with an automated, efficient process that maintains high coverage without redundancy.
2Measurement precision
If comprehensive test cases are executed to ensure coverage, then soft failure detection capability is improved, but system stress increases and fragile system state is exacerbated
Solution Approach 1:
The patent extracts and monitors specific performance parameters from the complex system state, creating simplified performance vectors that capture essential system behavior. By focusing on these extracted key parameters rather than comprehensively testing all system aspects, the method achieves effective soft failure detection with minimal system stress, avoiding the need to execute exhaustive test cases that would exacerbate fragile system states.
Solution Approach 2:
The patent changes the approach from comprehensive functional testing to parameter-based performance monitoring. By measuring and comparing performance parameters (such as response time, resource utilization, and error rates) before and after test case execution, the system can detect soft failures through parameter deviations without subjecting the system to the high stress of exhaustive testing. This parameter-based method maintains detection precision while reducing harmful system stress.
3Measurement precision
If test cases are executed on system under test to identify root cause, then diagnostic accuracy is improved, but system state becomes more fragile and may cause additional failures
Solution Approach 1:
The patent records baseline performance vectors from the system under test before executing diagnostic test cases. These preliminary baseline measurements establish a reference state that enables accurate comparison and outlier detection during subsequent testing. By having this preliminary characterization, the system can identify root causes through subtle performance deviations without needing to execute aggressive stress tests that would further destabilize an already fragile system state.
Solution Approach 2:
The patent creates performance vector representations (copies) of the system state that can be analyzed without directly stressing the physical system. By working with these virtual copies of performance data, the diagnostic process achieves high accuracy in identifying root causes while the actual system remains relatively undisturbed, preventing additional failures from being induced during the diagnostic process.
4Reliability
If multiple test cases are executed to achieve coverage, then testing thoroughness is improved, but time consumption and resource usage increase
Solution Approach 1:
The patent performs preliminary analysis to identify the minimal set of performance parameters that are most indicative of system health and potential failures. By focusing testing efforts on these pre-identified key parameters rather than executing numerous comprehensive test cases, the system achieves thorough testing coverage with significantly reduced time consumption. The preliminary identification of critical parameters guides the selection of a smaller, more efficient test suite.
Solution Approach 2:
The patent extracts and monitors only the most critical performance parameters from the full system state, creating condensed performance vectors that capture essential system behavior. This extraction approach enables thorough testing by focusing computational resources on measuring and analyzing only the most diagnostic parameters, thereby achieving comprehensive coverage insight without the time penalty of executing and analyzing all possible test cases.
Data Source
AI summary
A method for testing a system under test (SUT) in an active environment to identify cause of a soft failure includes recording a first difference vector by executing a set of test cases on a baseline system and monitoring performance parameters of the baseline system before and after executing the test cases. Each performance record represents differences in the performance parameters of the baseline system from before and after the execution of a corresponding test case. The method further includes, similarly, recording a second difference vector by executing the test cases on the SUT and monitoring performance parameters of the SUT before and after executing the test cases. The method further includes identifying an outlier performance record from the second difference vector by comparing the difference vectors and further, determining a root cause of the soft failure by analyzing a test case corresponding to the outlier.