LLM-Based Code Refactoring Assistant for Large Codebases
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Solution Overview
Problem
Current refactoring tools are inadequate for large codebases and require significant effort to learn, leading to programmers deferring or avoiding code refactoring, resulting in software quality issues.
Innovation Solution
A refactoring assistant using a large language model (LLM) generates refactoring code based on original and modified code snapshots, applying code modifications to similar code instances across a codebase without additional user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current refactoring tools are used, then code refactoring can be performed, but the tools are not powerful enough to handle large codebases and require significant effort to learn
Solution Approach 1:
The patent introduces an intermediary system that acts as a bridge between the programmer and the refactoring tools. This intermediary automatically analyzes code changes, generates refactoring operations, and applies them without requiring the programmer to directly interact with complex refactoring tool interfaces. The intermediary translates high-level code modification intent into detailed refactoring steps, thereby reducing the perceived complexity while maintaining powerful refactoring capabilities.
Solution Approach 2:
The system enables self-service refactoring by automatically detecting code changes and generating appropriate refactoring operations without human intervention. The refactoring process becomes self-serve in the sense that the system serves itself by autonomously analyzing, planning, and executing refactoring tasks based on observed code modifications, freeing programmers from manual tool operation while handling large codebases effectively.
2Adaptability or versatility
If current refactoring tools are used, then code refactoring can be performed, but it is too time consuming for programmers to learn how to use the tool effectively
Solution Approach 1:
The intermediary system eliminates the need for programmers to learn complex refactoring tools by acting as a translation layer. It observes code changes and automatically converts them into refactoring operations, requiring no learning curve from the programmer. The intermediary handles the complexity internally while presenting a simple interface based on natural code modification patterns.
Solution Approach 2:
The system performs self-service by autonomously generating and applying refactoring operations without requiring programmer education or training. The refactoring capability serves itself by automatically adapting to code changes and executing appropriate transformations, eliminating the time investment needed for learning traditional refactoring tools while maintaining versatility.
3Loss of time
If programmers defer refactoring code, then less time is spent on learning tools, but software quality deteriorates due to accumulated technical debt
Solution Approach 1:
The system performs preliminary refactoring actions automatically as code changes are made. Instead of deferring refactoring to a later time when programmers might avoid it due to tool complexity, the system proactively analyzes code modifications and applies refactoring operations immediately or near-immediately, preventing technical debt accumulation before it becomes significant.
Solution Approach 2:
The system establishes continuous refactoring action by automatically monitoring code changes and applying refactoring operations continuously as development progresses. This continuous process ensures software quality is maintained throughout the development lifecycle rather than being deferred, eliminating the trade-off between time investment and quality by making refactoring an ongoing automated activity.
Data Source
AI summary
A computer-implemented method includes receiving an original code snapshot corresponding to original code from a first file of a plurality of files. The method also includes receiving a modified code snapshot corresponding to modified code that includes a code modification modifying the original code. The method also includes generating, using a large language model (LLM), refactoring code based on the original code snapshot and the modified code snapshot. The refactoring code is configured to apply the code modification to code from other files of the plurality of files associated with the original code. The method also includes identifying target code from a second file of the plurality of files where the target code is associated with the original code. The method also includes applying the code modification to the identified target code using the refactoring code.