Empirical Research on Utilizing LLM-based Agents for Automated Bug Fixing via LangGraph

TL;DR

This paper introduces a comprehensive framework combining LangGraph, GLM4 Flash, and ChromaDB to automate code generation and debugging, enhancing accuracy and efficiency in software development workflows.

Contribution

It presents a novel multi-component system integrating graph-based task orchestration, large language models, and semantic memory for automated bug fixing and code generation.

Findings

Improved bug detection accuracy through semantic search.

Enhanced code repair efficiency with iterative refinement.

Seamless integration of components for scalable software debugging.

Abstract

This paper presents a novel framework for automated code generation and debugging, designed to improve accuracy, efficiency, and scalability in software development. The proposed system integrates three core components LangGraph, GLM4 Flash, and ChromaDB within a four step iterative workflow to deliver robust performance and seamless functionality. LangGraph serves as a graph-based library for orchestrating tasks, providing precise control and execution while maintaining a unified state object for dynamic updates and consistency. It supports multi-agent, hierarchical, and sequential processes, making it highly adaptable to complex software engineering workflows. GLM4 Flash, a large language model, leverages its advanced capabilities in natural language understanding, contextual reasoning, and multilingual support to generate accurate code snippets based on user prompts. ChromaDB acts as a vector database for semantic search and contextual memory storage, enabling the identification of patterns and the generation of context-aware bug fixes based on historical data. The system operates through a structured four-step process: (1) Code Generation, which translates natural language descriptions into executable code; (2) Code Execution, which validates the code by identifying runtime errors and inconsistencies; (3) Code Repair, which iteratively refines buggy code using ChromaDB's memory capabilities and LangGraph's state tracking; and (4) Code Update, which ensures the code meets functional and performance requirements through iterative modifications.