Leveraging Large Language Models for Intelligent Code Maintenance and Development

Abstract

"This research introduces an innovative approach that integrates Structured Indexing and Retrieval (SIR) techniques with Large Language Models (LLMs) to enhance the process of codebase retrieval and interaction. By employing Abstract Syntax Tree (AST) parsing, this method maintains the structural integrity of code, enabling a rich representation that captures both static and dynamic aspects of software projects. This structured representation facilitates the extraction of key software elements and their relationships, which are efficiently queried using a sequenced database. An LLM then interprets these structured inputs, improving context-awareness and precision in responding to user queries. The approach transcends traditional methods by treating codebases not as mere text but as complex structures that require understanding at both macro and micro levels. The integration of AST with LLMs marks a significant leap forward, making retrieval processes more intuitive and accurate. This system not only improves the relevance of the responses but also enhances the clarity and utility of the information retrieved, making it a powerful tool for developers. Preliminary evaluations, focusing on Python codebases, have demonstrated the system's effectiveness, achieving remarkable metrics in relevance, precision, clarity, and utility. This success establishes the proposed method as a significant advancement in the field of software development, setting a new standard for intelligent codebase management and interaction. Through its novel use of SIR techniques and LLMs, this research paves the way for more efficient and accurate codebase management solutions."