Workload Profiling Baselines for Software Testing Accuracy
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Solution Overview
Problem
Software testing often fails to identify issues in customer software before release, due to non-productive test environments that do not accurately represent customer environments, leading to wasted resources and time.
Innovation Solution
A method and system that compares customer data with test data in real-time during normal test runs, using statistical measures to identify and mitigate test deficiencies, allowing for agile workload profiling and immediate adjustment of test conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software testing methods are used with static test environments, then testing can be performed with simple setups, but the test environments do not accurately represent customer environments leading to undetected problems
Solution Approach 1:
The system performs preliminary workload profiling and environment characterization during the testing phase, collecting and analyzing test workload data before customer deployment. This preliminary action enables the test environment to be proactively configured with representative workloads, improving testing accuracy without requiring complex real-time adjustments during customer operation
Solution Approach 2:
The invention creates virtual copies of customer production environments including workload characteristics, system configuration, and operational patterns. These copied environments are used for testing, allowing accurate representation of customer conditions while maintaining separate test infrastructure, thus improving reliability without proportionally increasing physical complexity
2Reliability
If test environments are made more representative of customer environments, then more problems can be detected before release, but resources and time are wasted due to non-productive test runs
Solution Approach 1:
The system implements continuous feedback loops where test results, workload profiling data, and environment characterization information are analyzed to automatically adjust and optimize subsequent test runs. This feedback mechanism ensures that testing time is focused on high-value scenarios that are most likely to detect problems, preventing waste of time on non-productive test executions while maintaining high problem detection capability
Solution Approach 2:
The test environment is made dynamic and adaptable, allowing automatic adjustment of workload characteristics, test parameters, and execution schedules based on real-time analysis of profiling data and emerging test results. This dynamic approach enables the system to concentrate testing efforts on critical areas, improving problem detection efficiency without requiring excessive total testing time
3Measurement precision
If comprehensive workload profiling and data mining are performed, then more accurate test improvements can be achieved, but the complexity of the testing process increases
Solution Approach 1:
The system employs universal tools and methodologies for workload profiling and environment characterization that can be applied across different software systems and customer environments. By using standardized data collection, analysis, and modeling approaches, the system achieves high measurement precision without requiring separate complex solutions for each testing scenario, thus reducing overall system complexity while maintaining accuracy
Data Source
AI summary
Aspects of the present invention include a method, system and computer program product. The method includes obtaining, by a processor, customer data relating to a software workload environment of a customer, and obtaining, by the processor, test data relating to a test of a software system. The method also includes comparing, by the processor, the obtained customer data with the obtained test data to determine one or more statistical measures between the obtained customer data and the obtained test data, and displaying, by the processor, the determined one or more statistical measures between the obtained customer data and the obtained test data. The method further includes wherein the customer data contains multiple data points arranged in groups of analysis points, and wherein the test data contains multiple data points arranged in groups of analysis points.