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An Empirical Study on the Code Refactoring Capability of Large Language Models
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Exploring the potential of general purpose LLMs in automated software refactoring: an empirical study
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Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
inferredcites →https://doi.org/10.1016/j.jss.2017.03.044openalex-snapshot
Source code metrics: A systematic mapping study
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Using metrics to evaluate software system maintainability
inferredcites →https://doi.org/10.1109/aiware69974.2025.00020openalex-snapshot
Automated Extract Method Refactoring with Open-Source LLMs: A Comparative Study
inferredcites →https://doi.org/10.1109/apsec60848.2023.00025openalex-snapshot
Refactoring Programs Using Large Language Models with Few-Shot Examples
inferredcites →https://doi.org/10.1109/ase56229.2023.00035openalex-snapshot
On Automated Assistants for Software Development: The Role of LLMs
inferredcites →https://doi.org/10.1109/dsaa49011.2020.00096openalex-snapshot
Cluster Quality Analysis Using Silhouette Score
inferredcites →https://doi.org/10.1109/icse-fose59343.2023.00008openalex-snapshot
Large Language Models for Software Engineering: Survey and Open Problems
inferredcites →https://doi.org/10.1109/icse48619.2023.00128openalex-snapshot
Automated Repair of Programs from Large Language Models
inferredcites →https://doi.org/10.1109/icsme52107.2021.00018openalex-snapshot
On the Evaluation of Commit Message Generation Models: An Experimental Study
inferredcites →https://doi.org/10.1109/tbdata.2019.2921572openalex-snapshot
Billion-Scale Similarity Search with GPUs
inferredcites →https://doi.org/10.1109/tse.2004.1265817openalex-snapshot
A survey of software refactoring
inferredcites →https://doi.org/10.1109/tse.2011.41openalex-snapshot
How We Refactor, and How We Know It
inferredcites →https://doi.org/10.1109/tse.2016.2584050openalex-snapshot
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models
inferredcites →https://doi.org/10.1109/tse.2024.3428972openalex-snapshot
LLM-Based Test-Driven Interactive Code Generation: User Study and Empirical Evaluation
inferredcites →https://doi.org/10.1145/163359.163375openalex-snapshot
Software complexity and maintenance costs
inferredcites →https://doi.org/10.1145/2597073.2597121openalex-snapshot
An insight into the pull requests of GitHub
inferredcites →https://doi.org/10.1145/2950290.2950305openalex-snapshot
Why we refactor? confessions of GitHub contributors
inferredcites →https://doi.org/10.1145/3106237.3106259openalex-snapshot
Understanding the impact of refactoring on smells: a longitudinal study of 23 software projects
inferredcites →https://doi.org/10.1145/3180155.3180206openalex-snapshot
Accurate and efficient refactoring detection in commit history
inferredcites →https://doi.org/10.1145/3180155.3182532openalex-snapshot
On the diffuseness and the impact on maintainability of code smells
inferredcites →https://doi.org/10.1145/3468264.3468535openalex-snapshot
IDE support for cloud-based static analyses
inferredcites →https://doi.org/10.1145/3487569openalex-snapshot
In-IDE Code Generation from Natural Language: Promise and Challenges
inferredcites →https://doi.org/10.1145/3520312.3534862openalex-snapshot
A systematic evaluation of large language models of code
inferredcites →https://doi.org/10.1145/3544548.3581388openalex-snapshot
Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts
inferredcites →https://doi.org/10.1145/3597503.3623316openalex-snapshot
CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models
inferredcites →https://doi.org/10.1145/3611643.3616271openalex-snapshot
Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair
inferredcites →https://doi.org/10.1145/361598.361623openalex-snapshot
On the criteria to be used in decomposing systems into modules
inferredcites →https://doi.org/10.1145/3641289openalex-snapshot
A Survey on Evaluation of Large Language Models
inferredcites →https://doi.org/10.1145/3643991.3644881openalex-snapshot
Multi-faceted Code Smell Detection at Scale using DesigniteJava 2.0
inferredcites →https://doi.org/10.1145/3663529.3663803openalex-snapshot
EM-Assist: Safe Automated ExtractMethod Refactoring with LLMs
inferredcites →https://doi.org/10.1145/3691620.3695508openalex-snapshot
iSMELL: Assembling LLMs with Expert Toolsets for Code Smell Detection and Refactoring
inferredcites →https://doi.org/10.1145/512035.512055openalex-snapshot
Evolution patterns of open-source software systems and communities
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.168openalex-snapshot
Learning to Update Natural Language Comments Based on Code Changes
inferredcites →https://doi.org/10.18653/v1/2024.eacl-long.5openalex-snapshot
Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs
inferredcites →https://doi.org/10.18653/v1/2024.findings-acl.833openalex-snapshot
Balanced Data Sampling for Language Model Training with Clustering
inferredcites →https://doi.org/10.2307/2346830openalex-snapshot
Algorithm AS 136: A K-Means Clustering Algorithm
inferredcites →https://doi.org/10.5555/2337223.2337226openalex-snapshot
Where should the bugs be fixed? - more accurate information retrieval-based bug localization based on bug reports
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1016/j.jss.2025.112519openalex-snapshot
Comparative analysis of design pattern implementation validity in LLM-based code refactoring
inferred← citeshttps://doi.org/10.1109/icsa-c68850.2026.00074openalex-snapshot
An Empirical Analysis of LLM-Driven Refactoring for Microservices
inferred← citeshttps://doi.org/10.1109/llm4code66737.2025.00027openalex-snapshot
Leveraging LLMs for Legacy Code Modernization: Evaluation of LLM-Generated Documentation
inferred← citeshttps://doi.org/10.1109/mipro70003.2026.11591524openalex-snapshot
AI-Assisted Unit Test Writing and Test-Driven Code Refactoring: A Case Study
inferred← citeshttps://doi.org/10.1109/saner67736.2026.00033openalex-snapshot
Design Pattern-Based Code Refactoring with LLMs
inferred← citeshttps://doi.org/10.1145/3769994.3770000openalex-snapshot
Uncovering Behavioral Patterns in Student–LLM Conversations during Code Refactoring Tasks
inferred← citeshttps://doi.org/10.1145/3793655.3793733openalex-snapshot
From Human to Machine Refactoring: Assessing GPT-4’s Impact on Python Class Quality and Readability
inferred← citeshttps://doi.org/10.1145/3832783.3834413openalex-snapshot
Understanding the Code and Architectural Smells Generated by LLMs and Autonomous Agents
inferred← citeshttps://doi.org/10.1145/3832783.3837451openalex-snapshot
Prism: A Multi-solution Reasoning and Synthesis Framework for Repository-Level Issue Resolution
inferred← citeshttps://doi.org/10.1145/3843779.3844637openalex-snapshot
Prompts Matter: On the Impact of Code Smell Remediation with Large Language Models
inferred← citeshttps://doi.org/10.5753/sast.2026.31279openalex-snapshot
Can LLMs Detect and Refactor Test Smells in Performance Tests?
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