Computer Vision Unit Test Generation for Source Code
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Solution Overview
Problem
Organizations with large codebases often lack unit test cases, making it costly and impractical to maintain code quality through manual testing, especially with the demand for faster software releases and feature enhancements.
Innovation Solution
A computer-implemented method using computer vision and neural networks to automatically generate unit test cases by converting source code to images, identifying elements, and generating a key value map to create a scaffold for test cases, which can then be transformed by testers into viable test cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testers write unit test cases for large codebases, then code quality is maintained, but testing costs become prohibitively expensive
Solution Approach 1:
The system enables automated self-testing by converting source code to images, using neural networks to identify test elements, and automatically generating test case scaffolds without requiring extensive manual tester intervention
Solution Approach 2:
The patent replaces the mechanical process of manual test case writing with an automated computer vision-based system that uses neural networks to analyze code images and generate test cases, significantly reducing human labor costs
2Reliability
If more testers are hired to write unit test cases, then code quality improves, but project costs increase significantly
Solution Approach 1:
The system performs automated test generation without requiring large teams of human testers, using neural networks to autonomously analyze code and generate test case scaffolds
Solution Approach 2:
The system creates test case templates by copying and adapting patterns from analyzed source code structures, enabling automated generation of standardized test scaffolds that can be reused across different codebases
3Quantity of substance
If computer vision is used to automatically generate test cases, then testing cost is reduced, but the complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary conversion step that transforms source code into image format, enabling the use of computer vision techniques while maintaining a clear separation between code analysis and test generation processes
Solution Approach 2:
The system replaces complex manual test analysis with neural network-based image processing, using pre-trained models to automatically identify test elements without requiring complex custom analysis algorithms
Data Source
AI summary
A computer-implemented method, a computer program product, and a computer system for using computer vision to automatically generate a unit test case. A computer converts a text file of a source code to an image file of the source code. A computer inputs the image file to a neural network having been trained with source code images. The neural network identifies elements in the image file. The neural network generates a resulting image including identified elements in the image file. A computer extracts the identified elements from the resulting image. A computer generates a text file including a key value map of the identified elements. A computer generates a scaffold of a unit test case, based on the key value map.