17948625. GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING simplified abstract (International Business Machines Corporation)
Contents
- 1 GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING
Organization Name
International Business Machines Corporation
Inventor(s)
Swagatam Haldar of Kolkata (IN)
Devika Sondhi of New Delhi (IN)
Diptikalyan Saha of Bangalore (IN)
GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17948625 titled 'GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING
Simplified Explanation
The abstract describes a method for providing a surrogate program for a program endpoint by generating transformations based on input/output pairs, creating a model to classify inputs to transformations, selecting a transformation for a new input, and generating a new output using the selected transformation.
- Obtaining a set of input/output pairs generated from the program endpoint
- Generating transformations based on the input/output pairs
- Creating a model to classify inputs to transformations based on parameters of the inputs
- Receiving a new input
- Selecting a transformation for the new input using the model
- Generating a new output by applying the selected transformation to the new input
Potential Applications
This technology could be applied in software development, automated testing, and machine learning applications.
Problems Solved
This technology helps in automating the process of generating surrogate programs for program endpoints, saving time and resources in software development.
Benefits
The method provides a systematic approach to creating surrogate programs, improving efficiency and accuracy in software development tasks.
Potential Commercial Applications
- Software development tools
- Automated testing platforms
- Machine learning model training systems
Possible Prior Art
One possible prior art could be the use of machine learning models to classify inputs in software development tasks.
Unanswered Questions
How does this method handle complex input/output relationships?
The method seems to rely on a model to classify inputs to transformations, but it is not clear how it deals with intricate relationships between inputs and outputs.
Can this method be applied to real-time systems?
It is not specified in the abstract whether this method can be used in real-time systems where quick response times are crucial.
Original Abstract Submitted
A method of providing a surrogate program for a program endpoint includes: obtaining, by a processor set, a set of plural input/output pairs generated using the program endpoint; generating, by the processor set, transformations based on the input/output pairs; generating, by the processor set, a model that classifies inputs of the input/output pairs to ones of the transformations based on parameters of one or more strings of the inputs; receiving, by the processor set, a new input; selecting, by the processor set and using the model, one of the transformations based on parameters of one or more strings of the new input; and generating, by the processor set, a new output by applying the selected one of the transformations to the new input.
