Test Automation Framework Performance Analysis
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Solution Overview
Problem
Existing performance tests provide little to no analysis for application program developers, leading to performance issues being identified late in the development cycle, which can adversely affect the release schedule.
Innovation Solution
Implementing performance analysis by test automation frameworks that execute tests, log performance indicators, and identify correlations between them, such as CPU utilization and data transfer rates, to output performance issues early in the development cycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional performance tests are used, then the testing process is simple, but performance issues are identified late in the development cycle
Solution Approach 1:
The patent implements preliminary action by performing performance analysis during the testing phase itself, rather than waiting until later in the development cycle. The test automation framework proactively collects performance indicators, computes correlations, and identifies performance issues while tests are executing, enabling early detection without requiring separate analysis phases.
Solution Approach 2:
The patent applies feedback by continuously monitoring performance indicators during test execution and using correlation analysis to provide actionable insights back to developers. The system computes correlations between performance indicators and provides feedback about potential performance issues, enabling iterative improvement throughout the development cycle.
2Reliability
If performance analysis is implemented, then performance issues are identified early, but the test automation framework becomes more complex
Solution Approach 1:
The patent applies segmentation by dividing the performance analysis function into distinct modular components: performance indicator collection, correlation computation, and issue identification. This modular architecture allows the system to provide comprehensive performance analysis while maintaining manageable complexity through separated, reusable components.
Solution Approach 2:
The patent uses an intermediary approach by introducing a performance analysis layer that sits between the test execution engine and the developers. This intermediary component processes raw performance data, computes correlations, and translates them into actionable insights, shielding developers from complexity while delivering reliable performance information.
3Loss of information
If multiple performance indicators are monitored, then more insights are provided, but more data needs to be logged and analyzed
Solution Approach 1:
The patent applies feedback by implementing correlation analysis that automatically processes multiple performance indicators and provides meaningful insights about their relationships. The system computes correlations between different performance metrics and feeds this analyzed information back to developers, transforming raw data into actionable performance intelligence without requiring manual analysis.
Solution Approach 2:
The patent implements self-service by enabling the test automation framework to automatically analyze its own performance data. The system collects performance indicators, computes correlations between them, and identifies performance issues autonomously without requiring external analysis tools or manual intervention, reducing the complexity burden on external systems.
Data Source
AI summary
Performance analysis for test automation frameworks is described. A test is executed of an application. Performance indicators are logged during execution of the test, wherein the performance indicators include a first performance indicator. A correlation is identified between the first performance indicator and a second performance indicator recorded during the execution of the test or an execution of a previous test of the application. An output device outputs the correlation as a performance issue.