Microsoft technology licensing, llc. (20240134614). SOURCE CODE PATCH GENERATION WITH RETRIEVAL-AUGMENTED TRANSFORMER simplified abstract
Contents
- 1 SOURCE CODE PATCH GENERATION WITH RETRIEVAL-AUGMENTED TRANSFORMER
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SOURCE CODE PATCH GENERATION WITH RETRIEVAL-AUGMENTED TRANSFORMER - 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 Original Abstract Submitted
SOURCE CODE PATCH GENERATION WITH RETRIEVAL-AUGMENTED TRANSFORMER
Organization Name
microsoft technology licensing, llc.
Inventor(s)
AMANDEEP SINGH Bakshi of WEST LAFAYETTE IN (US)
NEELAKANTAN Sundaresan of BELLEVUE WA (US)
ALEXEY Svyatkovskiy of BELLEVUE WA (US)
SOURCE CODE PATCH GENERATION WITH RETRIEVAL-AUGMENTED TRANSFORMER - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240134614 titled 'SOURCE CODE PATCH GENERATION WITH RETRIEVAL-AUGMENTED TRANSFORMER
Simplified Explanation
The patent application describes a system for generating source code patches using the context of a buggy source code snippet and a hint to predict a repair segment. An autoregressive deep learning model is used to make this prediction based on the context and hint provided.
- The system uses the context of a buggy source code snippet and a hint to predict a repair segment.
- An autoregressive deep learning model is employed for making the prediction.
- The hint is a source code segment that is semantically similar to the buggy source code snippet.
- The similarity between the hint and the buggy code snippet is based on the context of the snippet.
Potential Applications
This technology could be applied in:
- Automated bug fixing in software development.
- Enhancing code review processes by suggesting potential fixes.
Problems Solved
This technology addresses:
- Time-consuming manual bug fixing processes.
- Improving code quality and reducing errors in software development.
Benefits
The benefits of this technology include:
- Increased efficiency in bug fixing.
- Enhanced accuracy in identifying and fixing bugs.
- Improved overall software quality.
Potential Commercial Applications
A potential commercial application for this technology could be:
- Integration into software development tools to assist developers in fixing bugs more efficiently.
Possible Prior Art
One possible prior art for this technology could be:
- Existing code analysis tools that provide suggestions for code improvements based on patterns and best practices.
Unanswered Questions
How does the system handle cases where the hint provided is not semantically similar to the buggy code snippet?
The system may struggle to generate accurate patches if the hint is not semantically similar to the buggy code snippet. Further research may be needed to address this issue.
Can the system be trained on a diverse set of programming languages to improve its accuracy across different codebases?
Expanding the training data to include a variety of programming languages could enhance the system's ability to generate accurate patches for different codebases.
Original Abstract Submitted
a source code patch generation system uses the context of a buggy source code snippet of a source code program and a hint to predict a source code segment that repairs the buggy source code snippet. the hint is a source code segment that is semantically-similar to the buggy source code snippet where the similarity is based on a context of the buggy source code snippet. an autoregressive deep learning model uses the context of the buggy source code snippet and the hint to predict the most likely source code segment to repair the buggy source code snippet.
