Computer Vision Software Performance Testing Automation
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Solution Overview
Problem
Current software performance testing methods are inefficient and costly, requiring extensive manual intervention and struggling to accurately simulate user interactions across diverse testing environments, leading to suboptimal testing outcomes.
Innovation Solution
A computer vision-based system that captures user interactions with software outputs, generates testing packages, and manages performance testing across virtualized testbed machines, enabling automated execution and analysis of software performance across multiple environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual performance testing methods are used, then testing accuracy can be maintained through human judgment, but testing efficiency is reduced and costs increase due to extensive manual intervention
Solution Approach 1:
The system enables automated self-testing by capturing screen recordings and using computer vision to automatically generate testing packages without human intervention. The testing system serves itself by autonomously navigating through software interfaces, identifying elements, and executing test cases based on recorded user interactions.
Solution Approach 2:
Manual mechanical testing operations are replaced with an automated computer vision-based system. The system uses optical capture (screen recording) and algorithmic processing to substitute human hands and eyes, transforming manual testing into an automated digital process that executes tests programmatically across multiple environments.
2Adaptability or versatility
If traditional testing methods are used, then testing processes can be simple to implement, but adaptability to diverse testing environments is reduced
Solution Approach 1:
The testing system is designed to operate universally across multiple testing environments including different operating systems, browsers, and device types. By capturing screen recordings and using computer vision to identify interface elements, the system adapts to diverse environments without requiring environment-specific test scripts, making it multi-functional and highly adaptable.
Solution Approach 2:
The system creates visual copies of user interactions through screen recording and reproduces them automatically in testing environments. By capturing the visual appearance and behavior of software interfaces, the system can replicate user actions across different platforms without needing to understand the underlying environment-specific implementations.
3Reliability
If comprehensive performance testing across multiple environments is conducted, then testing coverage is improved, but testing costs and time consumption increase
Solution Approach 1:
The system performs preliminary actions by capturing and analyzing user interactions in advance to generate comprehensive testing packages. By pre-recording user workflows and pre-processing this data into automated test cases, the system prepares extensive test coverage beforehand, which can then be executed efficiently across multiple environments without requiring proportional time investment for each test run.
Solution Approach 2:
The system merges multiple testing functions into a single integrated platform that handles cross-browser testing, cross-device testing, and performance analysis simultaneously. By combining these previously separate testing activities into one unified system, comprehensive coverage across multiple environments is achieved without proportionally increasing time or resource requirements.
Data Source
AI summary
Systems and methods for performance testing software using computer vision. Systems can include a performance testing computer vision system and a computer vision-based performance testbed system. Methods can include generating a computer vision-based testing package and performance testing software in one or more testing environments on at least one virtualized testbed machine according to testing constraints using the computer vision-based testing package.