Identification and Optimization of Redundant Code Using Large Language Models

2025-05-07

AI Summary

Redundant code is pervasive in AI system codebases, exacerbating maintenance overhead, technical debt, and error-fixing complexity—yet its characteristic patterns and developer-introduction mechanisms remain understudied. This paper presents the first systematic empirical analysis of redundancy patterns in AI projects—including duplicate implementations, obsolete paradigms, and inefficient structures—and identifies root causes such as lagging engineering practices and insufficient awareness of software design principles. We propose a scalable LLM-based agent framework integrating deep code semantic understanding, static analysis augmentation, multi-step prompt-driven reasoning, and formal refactoring validation to ensure functional equivalence during optimization. Evaluated on major AI framework repositories, our approach achieves 83% precision in redundancy detection, reduces code volume by 27% on average post-refactoring, and incurs zero functional regression—demonstrating both practical efficacy and production-readiness for AI software engineering.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Identifying redundant code patterns in AI system codebases
• Analyzing causes of unintentional redundant code introduction
• Automating detection and refactoring of redundancies using LLMs

Innovation

• Leveraging LLMs to detect redundant code
• Identifying redundancy patterns in AI projects
• Proposing LLM agent for large-scale refactoring