18180484. TEST CASE PRIORITIZATION simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 TEST CASE PRIORITIZATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TEST CASE PRIORITIZATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about the Technology
- 1.11 Original Abstract Submitted
TEST CASE PRIORITIZATION
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
[[:Category:Laurent Bou� of Petah Tikva (IL)|Laurent Bou� of Petah Tikva (IL)]][[Category:Laurent Bou� of Petah Tikva (IL)]]
TEST CASE PRIORITIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18180484 titled 'TEST CASE PRIORITIZATION
Simplified Explanation
The patent application describes a computing system that uses modified source code files to create a graph, which is then inputted into a graph machine learning model. This model is trained using graphs representing code files and test results from previous code commit events. The system uses the model to determine the order of test cases for the next code commit event, executing them during the software development build process.
Key Features and Innovation
- Encoding a next graph based on modified source code files
- Training a graph machine learning model using graphs and test results from previous code commit events
- Determining the order of test cases for the next code commit event using the model
- Executing test cases during the software development build process
Potential Applications
This technology could be applied in software development environments to automate the testing process and improve efficiency in identifying and resolving bugs.
Problems Solved
- Streamlining the testing process in software development
- Enhancing the accuracy of test case execution
- Improving the overall quality of software products
Benefits
- Increased efficiency in software testing
- Faster identification and resolution of bugs
- Enhanced software quality and reliability
Commercial Applications
- Automated testing tools for software development companies
- Quality assurance solutions for software products
- Optimization of software development processes
Questions about the Technology
How does this technology improve the software development process?
This technology improves the software development process by automating the testing phase, leading to faster bug identification and resolution.
What are the potential implications of using a graph machine learning model in software development?
The use of a graph machine learning model can enhance the accuracy and efficiency of test case execution, ultimately improving the quality of software products.
Original Abstract Submitted
A computing system encodes a next graph based on modified source code files recorded by the next code commit event. The computing system inputs the next graph to a graph machine learning model, the graph machine learning model being trained by graphs representing modified source code files and software test results corresponding to multiple code commit events occurring prior to the next code commit event in the sequence of code commit events. The computing system determines an order of test cases of the next code commit event using the graph machine learning model in an inference mode. The computing system executes the test cases according to the order during the software development build process corresponding to the next code commit event.
