Software Testing Benchmark Comparison System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current software testing methods are inadequate for determining the performance of software applications under multiple user loads, as they provide limited insights into system performance over time, are time-consuming, and prone to errors, lacking an automated process for tracking data from previous tests.
Innovation Solution
A system and method for comparing test data results with benchmark data to identify deviations, using historical data to predict future performance, and analyzing trends, which automates the analysis and reduces subjective judgment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated testing is implemented to track data from previous tests, then measurement precision and reliability of performance assessment are improved, but device complexity and initial time investment increase
Solution Approach 1:
The testing system is segmented into distinct functional modules: a data collection module that gathers test data, a data storage module that maintains historical benchmarks, a data retrieval module that accesses stored data, a comparison module that analyzes current vs. historical performance, and a reporting module that presents results. This modular architecture improves measurement precision while managing system complexity through organized, independent components.
Solution Approach 2:
Benchmark data from previous tests is collected, stored, and prepared in advance before actual performance assessment begins. Historical test results are pre-processed and organized into usable formats, allowing for efficient comparison with current test data and reducing analysis time during execution.
2Productivity
If manual analysis of test data is used, then ease of operation is maintained, but productivity and time efficiency deteriorate
Solution Approach 1:
The testing system performs self-service through automated data collection, storage, retrieval, and comparison operations. The system independently manages its own test data lifecycle, automatically comparing current test results against historical benchmarks and generating performance assessments without requiring manual intervention for these core functions.
Solution Approach 2:
The system implements automated feedback loops where test results are immediately compared against historical benchmarks, and performance deviations are automatically identified and reported. This continuous feedback mechanism enhances productivity by eliminating manual analysis cycles while maintaining operational simplicity through automated decision-support.
3Reliability
If comprehensive test data collection is performed, then reliability of performance assessment is improved, but loss of time in data processing increases
Solution Approach 1:
Test data is collected, validated, and stored as benchmark data in advance before it is needed for comparison. Historical test results are pre-processed and organized into efficient storage formats, allowing the system to rapidly retrieve and compare data during actual testing without experiencing processing delays.
Solution Approach 2:
The system extracts only the essential and relevant features from comprehensive test data for storage and comparison purposes. By identifying and extracting key performance indicators and critical data points, the system maintains assessment reliability while reducing the volume of data that requires processing and comparison.
Data Source
AI summary
Computer-implemented systems and methods for testing one or more software applications. For example, a system and method can be configured to perform a comparison between test data results and benchmark test data in order to determine test result differences between the received test data results and the retrieved benchmark test data. Testing criteria is applied to the determined test result differences in order to identify test deviations from the benchmark test data.