LLM Code Refactoring With Iterative Validation Feedback
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Solution Overview
Problem
Existing software refactoring processes are inefficient and time-consuming, particularly when transitioning between different development platforms or frameworks, and lack effective mechanisms to handle errors and improve code maintainability.
Innovation Solution
A multi-stage management system integrating generative AI systems, such as large language models (LLMs), to iteratively refactor code, validate it against multiple stages, and address errors, with human intervention when necessary, to enhance efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional manual refactoring processes are used, then code quality can be maintained through human review, but the time and effort required for refactoring increases significantly
Solution Approach 1:
The system enables automated self-service refactoring where the AI agent independently performs code analysis, generates refactoring plans, executes transformations, and validates changes without requiring continuous human intervention. The agent monitors its own progress and handles errors autonomously, significantly reducing the time humans would need to manually perform these tasks while maintaining code quality through systematic validation.
2Productivity
If generative AI systems are used to automate refactoring, then refactoring speed increases, but the complexity of the system increases
Solution Approach 1:
The refactoring system is divided into distinct modular components: a code analyzer that examines source code, a refactoring planner that generates transformation plans, an executor that applies changes, and a validator that verifies results. Each component handles a specific aspect of refactoring independently, making the overall complex AI-driven system manageable and maintainable while achieving high automation speed.
Solution Approach 2:
The system introduces an intermediary validation layer that acts as a mediator between the AI-generated refactoring actions and the final code output. This validation component checks generated code against multiple criteria (syntax validity, semantic correctness, functional equivalence) before acceptance, simplifying the overall system by providing a clear decision boundary and reducing the complexity of ensuring AI-generated code quality.
3Reliability
If multiple validation stages are implemented, then code quality and error detection improve, but the time required for verification increases
Solution Approach 1:
The system performs preliminary validation actions automatically during the refactoring process itself rather than only after completion. Syntax checking, semantic analysis, and even execution testing are conducted in advance as the refactoring progresses, allowing early detection and correction of errors. This preliminary validation approach ensures high reliability while minimizing total verification time by catching issues before they can propagate through subsequent validation stages.
4Manufacturing precision
If iterative refactoring attempts are made to handle errors, then code correctness improves, but the number of iterations and time required increases
Solution Approach 1:
The system implements a feedback mechanism where validation results are immediately fed back into the refactoring process. When errors are detected during validation, the AI agent receives this feedback and automatically generates corrected refactoring plans, re-executes the transformations, and re-validates the code. This iterative feedback loop ensures high code correctness by continuously refining the output based on validation results, while the automation of the feedback cycle reduces the time compared to manual iterative correction.
Data Source
AI summary
In some aspects, a management system is provided that interfaces with software code to execute a refactoring process. The process executes iteratively, where a generative AI component (e.g., an LLM) is given the task to make a repository pass a given validation iteratively. In each step, the LLM in provided as an input a list of failed validations to resolve and the ability to interact with the repository. In some examples, the LLM is configured to read source files, write source files, and retrieves information from other data sources such as the Internet or semantically-indexed source repositories, including the repository that the process is currently operating on, among other operations. Further, external APIs and databases can also be included in the process. The LLM will then execute one or multiple of these operations and process one or multiple files to generate modified and/or new code.