RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance
Haolin Jin, Zechao Sun, Yiheng Yang, Huaming Chen·October 02, 2024
Summary
RGD introduces a multi-LLM architecture for code generation and debugging, featuring Guide, Debug, and Feedback Agents. It iteratively refines code, surpassing existing methods on benchmark datasets. The framework addresses limitations in single-pass code generation by enabling a multi-step, self-reflective process. It integrates three processes: code generation, debugging, and evaluation, using LLMs for guide generation, code creation, and failure analysis. Evaluated on benchmarks, RGD outperforms existing frameworks, showcasing significant improvements in code quality and efficiency.
Introduction
Background
Overview of existing code generation and debugging methods
Challenges in single-pass code generation
Objective
To introduce a novel multi-agent architecture for code generation and debugging that iteratively refines code
To address limitations in single-pass code generation through a multi-step, self-reflective process
Method
Architecture Overview
Description of the RGD architecture featuring Guide, Debug, and Feedback Agents
Explanation of how these agents work together in a loop to generate, debug, and evaluate code
Code Generation
Role of LLMs in generating code
Techniques for generating high-quality code
Debugging Process
Methodology for identifying and correcting code failures
Utilization of LLMs for failure analysis
Evaluation and Feedback
Process for evaluating code quality and efficiency
Incorporation of feedback into the iterative refinement process
Implementation
Data Collection
Methods for collecting benchmark datasets
Importance of diverse and representative datasets
Data Preprocessing
Techniques for preparing data for the RGD architecture
Handling of code snippets, errors, and feedback
Results
Benchmark Evaluation
Metrics used for comparing RGD with existing frameworks
Detailed results showcasing RGD's performance improvements
Case Studies
Illustrative examples demonstrating RGD's effectiveness in code generation and debugging
Conclusion
Summary of RGD's contributions
Future Directions
Potential enhancements and extensions of the RGD architecture
Areas for further research
Basic info
papers
computation and language
software engineering
artificial intelligence
Advanced features