Identification and Optimization of Redundant Code Using Large Language Models (2505.04040v1)

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 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 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 Our research aims to identify recurring patterns of redundancy and analyze their underlying causes, such as outdated practices or insufficient awareness of best coding principles.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.

Identification and Optimization of Redundant Code Using LLMs

The paper "Identification and Optimization of Redundant Code Using LLMs" investigates the pervasive issue of redundant code within software development, particularly focusing on its identification and optimization in AI system codebases via LLMs. Redundant code exacerbates technical debt, complicates maintenance, and poses challenges such as bug introduction and overlooked dependencies when removal is attempted manually. Despite recognition of these impacts, there is a noted deficit in research specifically targeting AI codebases and the origins of redundancy within them. The research outlined in this paper addresses these lapses using LLMs to automate the detection and refactoring process, aiming to preserve functionality while optimizing the code for readability and scalability.

Research Context and Problem Statement

Redundant code is identified as a significant barrier to code quality and maintainability due to its propensity to increase complexity and workload without delivering additional functionality. Prior studies have extensively cataloged the negative impacts of dead or unused code. For example, redundant code creates inefficiencies and privacy risks, predominantly within large Python codebases, as identified by Shackleton et al. Furthermore, Dandan et al. recognized redundancy's contributions to debugging complications and bug generation risks. Suzuki et al. discovered functional redundancies across multiple projects, emphasizing the recurrent nature of these inefficiencies in source code.

Despite the reported prevalence, there remains a gap in understanding the coding patterns that contribute to redundancy. Existing literature primarily addresses specific types of redundancies like dead code, omitting broader patterns of code inefficiencies. The reasons behind the introduction of such redundancies also require further exploration to contextualize developer challenges and practices that inadvertently foster repetition.

Methodology and Objectives

The researchers propose leveraging LLMs, prominent in code-related applications, to identify redundant code patterns in AI systems' source code. The goal extends to developing a framework that integrates code optimization without sacrificing functionality. The paper will address the prevalence of redundancy and analyze the impacts on code quality, explore coding patterns leading to redundancy, garner developers' perspectives on handling redundant code, and evaluate the effectiveness of LLMs in optimizing these inefficiencies.

Expected Outcomes and Contributions

The paper anticipates two primary outcomes:

1. A deeper understanding of redundancy prevalence and its impact on code quality, alongside insights into developer practices contributing to redundancy.
2. The development of a tool utilizing LLMs to automatically eliminate redundant code while maintaining original functionality.

The contributions of this research include a detailed analysis and cataloging of redundancy reasons and common patterns, informed by developer feedback and existing studies. This will facilitate building a prototype tool capable of automated code refactoring, aiming to revolutionize redundancy detection and optimization methods.

Evaluation and Limitations

The evaluation framework will involve ensuring that optimized code by LLMs passes functional tests and conducting systematic literature reviews alongside developer interviews for qualitative insights. User studies will also assess the functionality and usability of the automated tool. A noted limitation is the focus on open-source AI projects, potentially affecting the broader applicability. LLM biases, test coverage incompleteness, and reliance on specific software quality metrics are identified constraints, with future strategies proposed to mitigate these issues.

Implications and Future Directions

The implications of this research are both practical and theoretical. Practically, developers could achieve more efficient codebases through automated redundancy optimization, facilitating better maintenance practices and improved software system stability. Theoretically, it provides a foundational approach utilizing LLMs for code optimization, which may inform further studies on AI in software engineering tasks. The proposed advancements in detecting and refactoring code redundancies present future avenues for innovation in AI system development, notably within varying software environments beyond open-source projects.