Large Language Models (LLMs) possess significant capabilities for debugging and generating code. However, users must still possess a foundational understanding of programming and be able to integrate the generated code into the existing codebase to fulfill specific objectives. To address this challenge, my teammate and I undertook an investigation into the potential of LLMs to directly modify the codebase in accordance with user requirements. We put our focus on a Warehouse Management System codebase, developed using Django and Vue.js.

We began with an in-depth analysis of the stages and workflows within the WMS codebase, aiming to understand its architecture and operational dynamics. Subsequently, we explored the SWE-agent, a tool designed to interface with large language models (LLMs), to enable automated codebase modifications based on user-generated prompts. To provide accurate overview of the codebase, we developed both human-written and machine-generated overview, which was generated by the Repo-agent. A significant portion of our work focused on developing and optimizing prompt inputs and configurations to enhance the accuracy and efficacy of these automated modifications. This study explores the potential for LLM-driven automation in complex codebases, and offers insights into the optimization of LLM interactions for precise and reliable software modifications.

Technologies Used

• Django
• Large Language Model
• HTML & CSS & JavaScript
• Prompt-based Learning
• SWE-agent & Repo-agent

Collaborator

• Ziang Gu: guziang@msu.edu
• Juyuan Huang: juyuanhuang24@gmail.com