Microsoft technology licensing, llc. (20240184570). CODE REVIEW COMMENT GENERATION VIA RETRIEVAL-AUGMENTED TRANSFORMER WITH CHUNK CROSS- ATTENTION simplified abstract
Contents
- 1 CODE REVIEW COMMENT GENERATION VIA RETRIEVAL-AUGMENTED TRANSFORMER WITH CHUNK CROSS- ATTENTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CODE REVIEW COMMENT GENERATION VIA RETRIEVAL-AUGMENTED TRANSFORMER WITH CHUNK CROSS- ATTENTION - 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 Code Review Prediction
- 1.13 Original Abstract Submitted
CODE REVIEW COMMENT GENERATION VIA RETRIEVAL-AUGMENTED TRANSFORMER WITH CHUNK CROSS- ATTENTION
Organization Name
microsoft technology licensing, llc.
Inventor(s)
XIAOYU Liu of SAMMAMISH WA (US)
NEELAKANTAN Sundaresan of BELLEVUE WA (US)
ALEXEY Svyatkovskiy of BELLEVUE WA (US)
CODE REVIEW COMMENT GENERATION VIA RETRIEVAL-AUGMENTED TRANSFORMER WITH CHUNK CROSS- ATTENTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240184570 titled 'CODE REVIEW COMMENT GENERATION VIA RETRIEVAL-AUGMENTED TRANSFORMER WITH CHUNK CROSS- ATTENTION
Simplified Explanation
A retrieval-augmented neural transformer model with chunk cross-attention predicts a code review based on a proposed source code change and historical code review comments.
- The model analyzes proposed code changes represented as code diff hunks and historical code review comments to make predictions.
- Code diff hunks show proposed edits to a code snippet with its surrounding context.
- Historical code review comments are associated with similar code edits.
- The model partitions the code diff hunk into chunks to find similar historical code review comments.
- It aggregates the historical code review comments to guide its predictions.
Key Features and Innovation
- Utilizes a retrieval-augmented neural transformer model.
- Incorporates chunk cross-attention mechanism.
- Predicts code reviews based on proposed code changes and historical comments.
- Partitions code diff hunks for better analysis.
- Aggregates historical code review comments for guidance.
Potential Applications
- Automated code review systems.
- Software development tools.
- Quality assurance in programming.
- Enhancing collaboration among developers.
Problems Solved
- Streamlines the code review process.
- Improves the accuracy of code review predictions.
- Helps identify relevant historical comments for new code changes.
- Enhances the efficiency of software development workflows.
Benefits
- Saves time in manual code review processes.
- Increases the quality of code reviews.
- Facilitates knowledge sharing among developers.
- Enhances the overall productivity of software development teams.
Commercial Applications
Automated Code Review System: Revolutionizing the way code reviews are conducted in software development
Prior Art
No prior art information available at this time.
Frequently Updated Research
Currently, there are ongoing studies on optimizing the chunk cross-attention mechanism in neural transformer models for more accurate code review predictions.
Questions about Code Review Prediction
Question 1
How does the model handle variations in coding styles when predicting code reviews? The model incorporates a robust training process that allows it to adapt to different coding styles and patterns, ensuring accurate predictions regardless of variations.
Question 2
Can the model handle large codebases with extensive historical code review comments? Yes, the model is designed to scale effectively to large codebases by efficiently processing and aggregating historical code review comments for accurate predictions.
Original Abstract Submitted
a retrieval-augmented neural transformer model with chunk cross-attention predicts a code review given a proposed source code change, represented as a code diff hunk, and a set of historical code review comments. the code diff hunk represents proposed edits to a source code snippet with its surrounding context that has not been changed. the historical code review comments are associated with code edits that are semantically similar to the proposed source code changes. the code diff hunk is partitioned into chunks which are used to find semantically similar historical code review comments. the set of historical code review comments is aggregated and used to guide the model in makings its predictions.
