Software Performance Testing via Behavior Model Correlation
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Solution Overview
Problem
Current performance testing systems struggle to accurately identify performance bottlenecks and root causes from log files, leading to increased time and resource costs in determining system issues, as existing technologies fail to systematically analyze and correlate behavior models with affected parameters.
Innovation Solution
A method and device for improving software performance testing by receiving input data from test management systems, identifying behavior models, correlating them with affected parameters to determine performance issues, and verifying these issues through reassessment, utilizing a performance test computing device with modules like a modeler engine, profiling engine, and modular analysis engine to analyze log files and provide detailed insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple performance tests are run to identify bottlenecks, then the accuracy of root-cause analysis is improved, but the time and resource costs increase
Solution Approach 1:
The system performs preliminary actions by pre-defining behavior models and their associated parameters before actual performance testing occurs. These pre-established models enable rapid correlation and identification during testing, eliminating the need for multiple iterative tests to understand system behavior patterns.
Solution Approach 2:
The patent introduces behavior models as intermediary structures that mediate between raw log data and performance analysis. These models act as a translation layer that transforms complex log files into actionable insights, enabling accurate root-cause analysis without requiring multiple exhaustive tests.
2Quantity of substance
If log files from multiple sources are collected, then the completeness of performance data is improved, but the difficulty of analyzing and correlating information increases
Solution Approach 1:
The system segments the analysis process into distinct modules: data collection, behavior model identification, parameter correlation, and root-cause determination. Each module handles a specific aspect of the complex analysis, making the overall system more manageable and easier to implement despite the complexity of multi-source data integration.
Solution Approach 2:
The behavior models serve multiple functions simultaneously: they represent system behavior patterns, define parameter relationships, and guide correlation analysis. This multi-functionality reduces the need for separate analysis mechanisms and simplifies the overall system architecture while maintaining data completeness.
3Measurement precision
If systematic root-cause analysis is performed, then the accuracy of identifying performance issues is improved, but the effort and resources required increase
Solution Approach 1:
The system implements feedback mechanisms where the analysis results are used to refine and update behavior models. This continuous feedback loop enables the system to learn from previous analyses and improve its accuracy over time, reducing the effort required for systematic analysis while maintaining high precision in issue identification.
Data Source
AI summary
Embodiments of the present disclosure disclose a method and a device for improving software performance testing. The method comprises receiving input data from one or more test management systems. The method further comprises identifying at least one behavior model based on the input data. The method further comprises correlating the at least one behavior model with at least one of affected parameters to determine one or more performance issues in the input data. The method further comprises verifying the one or more performance issues by reassessing the at least one behavior model.