International business machines corporation (20240289260). TEST CASE GENERATION simplified abstract
Contents
TEST CASE GENERATION
Organization Name
international business machines corporation
Inventor(s)
Diptikalyan Saha of Bangalore (IN)
Kahini Wadhawan of Ferozepur (IN)
TEST CASE GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240289260 titled 'TEST CASE GENERATION
Simplified Explanation: The patent application describes a processor that can analyze input and output data from computer code to build a decision tree. The processor then uses a condition generator model to predict the next condition in the code, generate input instances, and detect new flow paths in the code.
Key Features and Innovation:
- Processor analyzes input and output data to build a decision tree.
- Utilizes a condition generator model to predict next conditions in the code.
- Generates input instances based on predicted conditions.
- Detects new flow paths in the code based on output instances.
Potential Applications: This technology could be applied in software development, debugging, and code optimization processes.
Problems Solved: The technology helps in automating the analysis of computer code, identifying new flow paths, and improving code efficiency.
Benefits:
- Streamlines software development processes.
- Enhances code optimization and debugging.
- Automates the detection of new flow paths in the code.
Commercial Applications: The technology could be used in software development companies, IT consulting firms, and organizations focusing on code optimization tools.
Prior Art: Researchers and developers can explore prior art related to decision tree analysis, code optimization, and machine learning models in software development.
Frequently Updated Research: Stay updated on advancements in machine learning models for code analysis and optimization in software development.
Questions about the Technology: 1. How does the processor predict the next condition in the code? 2. What are the potential limitations of using a condition generator model in analyzing computer code?
Original Abstract Submitted
a processor can receive input data to a computer code and output data that the computer code produces corresponding to the input data. based on the input data and the output data, the processor can build a decision tree that links input conditions to the output data. using the input conditions, the processor can run a condition generator model created by fine-tuning a pre-trained programming language model, where the condition generator model predicts next condition that is likely to occur in the computer code. based on the next condition, the processor can generate input instances and run the computer code using the generated input instances. based on output instances output by the computer code based on the generated input instances, the processor can detect at least one flow path in the computer code, which had not been previously built in the decision tree.
