Identification and Optimization of Redundant Code Using Large Language Models

TL;DR

This paper explores using large language models to automatically detect, analyze, and optimize redundant code in AI projects, aiming to improve code quality and maintainability.

Contribution

It introduces a novel approach leveraging LLMs for identifying and refactoring redundant code patterns in AI systems, addressing gaps in current research.

Findings

Identification of common redundancy patterns in AI codebases

Analysis of causes behind unintentional redundancy

Proposal of an LLM-based agent for automated refactoring

Abstract

Redundant code is a persistent challenge in software development that makes systems harder to maintain, scale, and update. It adds unnecessary complexity, hinders bug fixes, and increases technical debt. Despite their impact, removing redundant code manually is risky and error-prone, often introducing new bugs or missing dependencies. While studies highlight the prevalence and negative impact of redundant code, little focus has been given to Artificial Intelligence (AI) system codebases and the common patterns that cause redundancy. Additionally, the reasons behind developers unintentionally introducing redundant code remain largely unexplored. This research addresses these gaps by leveraging large language models (LLMs) to automatically detect and optimize redundant code in AI projects. Our research aims to identify recurring patterns of redundancy and analyze their underlying causes, such as outdated practices or insufficient awareness of best coding principles. Additionally, we plan to propose an LLM agent that will facilitate the detection and refactoring of redundancies on a large scale while preserving original functionality. This work advances the application of AI in identifying and optimizing redundant code, ultimately helping developers maintain cleaner, more readable, and scalable codebases.