Microsoft technology licensing, llc (20240303185). TEST CASE PRIORITIZATION simplified abstract

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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)]]

Kiran Rama of Bangalore (IN)

TEST CASE PRIORITIZATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240303185 titled 'TEST CASE PRIORITIZATION

Simplified Explanation: The computing system encodes a next graph based on modified source code files from the next code commit event. It then inputs this graph into a graph machine learning model to determine the order of test cases for the next code commit event.

  • **Key Features and Innovation:**
   - Encoding next graph based on modified source code files
   - Training graph machine learning model with graphs representing modified source code files and software test results
   - Determining order of test cases for the next code commit event using the graph machine learning model
  • **Potential Applications:**
   - Software development process optimization
   - Automated test case prioritization
   - Enhancing software quality and reliability
  • **Problems Solved:**
   - Efficient test case prioritization
   - Streamlining software development build process
   - Improving software testing accuracy
  • **Benefits:**
   - Faster software development cycles
   - Reduced testing time and effort
   - Enhanced software quality and reliability
  • **Commercial Applications:**
   - "Automated Test Case Prioritization System for Software Development"
  • **Prior Art:**
   - Prior research on graph-based machine learning models for software testing and development processes
  • **Frequently Updated Research:**
   - Ongoing advancements in graph machine learning models for software testing and development

Questions about Automated Test Case Prioritization System for Software Development:

1. How does the graph machine learning model determine the order of test cases for the next code commit event? 

   - The graph machine learning model analyzes the relationships between modified source code files and software test results to prioritize test cases effectively.

2. What are the potential challenges in implementing an automated test case prioritization system in a software development environment? 

   - Some challenges may include integrating the system with existing development tools, ensuring the accuracy of test case prioritization, and adapting to different software development workflows.


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.

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