Computer Vision Software Functional Testing
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Solution Overview
Problem
Current software testing methods are inefficient and costly, as they require extensive manual intervention and resource allocation for functional testing, especially in simulating user interactions and identifying graphical elements within software outputs.
Innovation Solution
A computer vision-based system that captures user interactions with software outputs, generates testing packages using computer vision to recognize graphical elements, and executes these packages on virtualized testbed machines for automated functional testing, reducing manual effort and improving testing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual testing methods are used to simulate user interactions and identify graphical elements, then testing accuracy can be maintained, but testing efficiency and productivity are significantly reduced
Solution Approach 1:
The patent replaces manual mechanical testing operations with an automated computer vision system that uses optical recognition to identify graphical elements and simulate user interactions. The system captures screenshots, processes images through computer vision algorithms, and automatically determines element positions and properties, eliminating the need for manual visual inspection and interaction simulation.
Solution Approach 2:
The testing system performs self-service by automatically capturing its own screenshots, processing the images through computer vision to identify graphical elements, and generating test results without requiring external manual intervention. The system independently executes the testing workflow from screenshot capture to element identification to test case generation.
2Reliability
If extensive manual intervention is applied for functional testing, then testing thoroughness can be ensured, but resource allocation costs and time consumption increase
Solution Approach 1:
The patent implements continuous automated testing by establishing a workflow that continuously captures screenshots, processes images through computer vision, and generates test results without interruption. The system maintains continuous operation by automatically transitioning between testing steps, eliminating the gaps and delays inherent in manual testing processes.
Solution Approach 2:
The system performs preliminary actions by pre-configuring test environments, pre-processing image capture parameters, and pre-establishing computer vision recognition models before actual testing begins. This preparation work is done in advance to enable rapid execution of the actual testing process, reducing time consumption during the critical testing phase.
3Productivity
If automated testing is implemented without computer vision, then productivity increases, but the ability to accurately identify graphical elements and simulate user interactions deteriorates
Solution Approach 1:
The patent introduces computer vision technology as an intermediary between the automated testing system and the graphical user interface. The computer vision system acts as a mediator that bridges the gap between automated control and visual recognition, enabling the system to accurately identify graphical elements through image processing while maintaining full automation. This intermediary layer preserves measurement precision without sacrificing productivity.
Data Source
AI summary
Systems and methods for functionally testing software using computer vision. Systems can include a functional testing computer vision system and a computer vision-based functional testbed system. Methods can include generating a computer vision-based testing package and functionally testing software on at least one virtualized testbed machine using the computer vision-based testing package.