Microsoft technology licensing, llc. (20240192927). CODE GENERATION THROUGH REINFORCEMENT LEARNING USING CODE-QUALITY REWARDS simplified abstract
Contents
- 1 CODE GENERATION THROUGH REINFORCEMENT LEARNING USING CODE-QUALITY REWARDS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CODE GENERATION THROUGH REINFORCEMENT LEARNING USING CODE-QUALITY REWARDS - 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 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
CODE GENERATION THROUGH REINFORCEMENT LEARNING USING CODE-QUALITY REWARDS
Organization Name
microsoft technology licensing, llc.
Inventor(s)
SHAO KUN Deng of NEW YORK CITY NY (US)
NEELAKANTAN Sundaresan of BELLEVUE WA (US)
ALEXEY Svyatkovskiy of BELLEVUE WA (US)
MICHELE Tufano of BELLEVUE WA (US)
CODE GENERATION THROUGH REINFORCEMENT LEARNING USING CODE-QUALITY REWARDS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240192927 titled 'CODE GENERATION THROUGH REINFORCEMENT LEARNING USING CODE-QUALITY REWARDS
Simplified Explanation
A deep learning model is trained to predict source code and then fine-tuned for a specific code generation task using reinforcement learning with a reward system based on code quality factors and metrics.
- The deep learning model is trained to predict source code.
- The model is adjusted for a target code generation task through reinforcement learning.
- A reward score is used to evaluate the quality of the predicted source code.
- Code-quality factors such as syntactic correctness, successful compilation, execution, invocation, readability, functional correctness, and coverage are considered.
- Source code metrics generate a score based on the similarity to a ground truth code.
Key Features and Innovation
- Training a deep learning model to predict source code.
- Fine-tuning the model for a specific code generation task using reinforcement learning.
- Introducing a reward system based on code quality factors and metrics.
- Considering syntactic correctness, successful compilation, execution, invocation, readability, functional correctness, and coverage in evaluating the predicted source code.
Potential Applications
This technology can be applied in software development, code generation automation, code quality assessment, and programming education.
Problems Solved
This technology addresses the challenges of accurately predicting and generating source code for specific tasks, improving code quality, and automating code generation processes.
Benefits
- Enhanced accuracy in predicting source code.
- Improved code quality through evaluation based on various factors.
- Automation of code generation tasks for efficiency and productivity.
Commercial Applications
Title: Automated Code Generation and Quality Assessment Technology This technology can be utilized in software development companies, educational institutions offering programming courses, and any organization requiring automated code generation and quality assessment tools.
Prior Art
Further research can be conducted in the field of deep learning models for source code prediction and generation, reinforcement learning for tuning models, and quality assessment in software development.
Frequently Updated Research
Stay updated on advancements in deep learning models for source code prediction, reinforcement learning techniques for model tuning, and code quality assessment methodologies in software development.
Questions about the Technology
What are the potential limitations of using deep learning models for source code prediction?
Deep learning models may struggle with understanding complex logic and context in source code, leading to inaccuracies in predictions.
How can the reward system be further optimized to improve the quality of predicted source code?
The reward system can be fine-tuned by adjusting the weights assigned to different code-quality factors and metrics, based on the specific requirements of the code generation task.
Original Abstract Submitted
a deep learning model trained to learn to predict source code is tuned for a target source code generation task through reinforcement learning using a reward score that considers the quality of the source code predicted during the tuning process. the reward score is adjusted to consider code-quality factors and source code metrics. the code-quality factors account for the predicted source code having syntactic correctness, successful compilation, successful execution, successful invocation, readability, functional correctness, and coverage. the source code metrics generate a score based on how close the predicted source code is to a ground truth code.
