Cover Image

Evaluating the Effectiveness of Small Language Models in Detecting Refactoring Bugs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gheyi, Rohit, Ribeiro, Marcio, Oliveira, Jonhnanthan
Format:	Preprint
Published:	2025
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bugs in the Shadows: Static Detection of Faulty Python Refactorings
by: Oliveira, Jonhnanthan, et al.
Published: (2025)

Foundation Models as Oracles for Refactoring Correctness Detection
by: Gheyi, Rohit, et al.
Published: (2026)

RefModel: Detecting Refactorings using Foundation Models
by: Simões, Pedro, et al.
Published: (2025)

Evaluating Large Language Models in Detecting Test Smells
by: Lucas, Keila, et al.
Published: (2024)

Evaluating the Capability of LLMs in Identifying Compilation Errors in Configurable Systems
by: Albuquerque, Lucas, et al.
Published: (2024)

Code Generation with Small Language Models: A Codeforces-Based Study
by: Souza, Débora, et al.
Published: (2025)

Investigating the Performance of Small Language Models in Detecting Test Smells in Manual Test Cases
by: Lucas, Keila, et al.
Published: (2025)

Variability-Aware Detection and Repair of Compilation Errors Using Foundation Models in Configurable Systems
by: Gheyi, Rohit, et al.
Published: (2026)

Refactoring for Novices in Java: An Eye Tracking Study on the Extract vs. Inline Methods
by: da Costa, José Aldo Silva, et al.
Published: (2026)

Assessing Python Style Guides: An Eye-Tracking Study with Novice Developers
by: Roberto, Pablo, et al.
Published: (2024)

An Empirical Study of Refactoring Engine Bugs
by: Wang, Haibo, et al.
Published: (2024)

Testing Refactoring Engine via Historical Bug Report driven LLM
by: Wang, Haibo, et al.
Published: (2025)

A Catalog of Transformations to Remove Smells From Natural Language Tests
by: Aranda, Manoel, et al.
Published: (2024)

Agentic LMs: Hunting Down Test Smells
by: Melo, Rian, et al.
Published: (2025)

Refactoring Detection in C++ Programs with RefactoringMiner++
by: Ritz, Benjamin, et al.
Published: (2025)

Assessing the Bug-Proneness of Refactored Code: A Longitudinal Multi-Project Study
by: Ferreira, Isabella, et al.
Published: (2025)

Adoption of Large Language Models in Scrum Management: Insights from Brazilian Practitioners
by: Perkusich, Mirko, et al.
Published: (2026)

Refactoring $\neq$ Bug-Inducing: Improving Defect Prediction with Code Change Tactics Analysis
by: Niu, Feifei, et al.
Published: (2025)

Automata Models for Effective Bug Pattern Description
by: Yaacov, Tom, et al.
Published: (2025)

Isolating Compiler Bugs by Generating Effective Witness Programs with Large Language Models
by: Tu, Haoxin, et al.
Published: (2023)

An Empirical Study on the Code Refactoring Capability of Large Language Models
by: Cordeiro, Jonathan, et al.
Published: (2024)

ChatGPT for Code Refactoring: Analyzing Topics, Interaction, and Effective Prompts
by: AlOmar, Eman Abdullah, et al.
Published: (2025)

ACE: Automated Technical Debt Remediation with Validated Large Language Model Refactorings
by: Tornhill, Adam, et al.
Published: (2025)

Yuga: Automatically Detecting Lifetime Annotation Bugs in the Rust Language
by: Nitin, Vikram, et al.
Published: (2023)

The Role of the Retrospective Meetings in Detecting, Refactoring and Monitoring Community Smells
by: Dantas, Carlos, et al.
Published: (2025)

Assessing the Impact of Refactoring Energy-Inefficient Code Patterns on Software Sustainability: An Industry Case Study
by: Mehra, Rohit, et al.
Published: (2025)

"Refactoring Runaway": Understanding and Mitigating Tangled Refactorings in Coding Agents for Issue Resolution
by: Tian, Zhao, et al.
Published: (2026)

How to Refactor this Code? An Exploratory Study on Developer-ChatGPT Refactoring Conversations
by: AlOmar, Eman Abdullah, et al.
Published: (2024)

Characterizing Real-World Bugs in Tile Programs for Automated Bug Detection
by: Rathnasuriya, Ravishka, et al.
Published: (2026)

Relating Complexity, Explicitness, Effectiveness of Refactorings and Non-Functional Requirements: A Replication Study
by: Soares, Vinícius, et al.
Published: (2025)

Fine-Tuning Code Language Models to Detect Cross-Language Bugs
by: Li, Zengyang, et al.
Published: (2025)

Code Refactoring with LLM: A Comprehensive Evaluation With Few-Shot Settings
by: Tapader, Md. Raihan, et al.
Published: (2025)

Exploring the Capabilities of Vision-Language Models to Detect Visual Bugs in HTML5 <canvas> Applications
by: Macklon, Finlay, et al.
Published: (2025)

Does Programming Language Matter? An Empirical Study of Fuzzing Bug Detection
by: Shirai, Tatsuya, et al.
Published: (2026)

Vulnerability Detection with Interprocedural Context in Multiple Languages: Assessing Effectiveness and Cost of Modern LLMs
by: Lira, Kevin, et al.
Published: (2026)

Automated Unit Test Refactoring
by: Gao, Yi, et al.
Published: (2024)

SWE-Refactor: A Repository-Level Benchmark for Real-World LLM-Based Code Refactoring
by: Xu, Yisen, et al.
Published: (2026)

Refactoring to Pythonic Idioms: A Hybrid Knowledge-Driven Approach Leveraging Large Language Models
by: Zhang, Zejun, et al.
Published: (2024)

GitBugs: Bug Reports for Duplicate Detection, Retrieval Augmented Generation, Triage, and More
by: Patil, Avinash, et al.
Published: (2025)

HAFix: History-Augmented Large Language Models for Bug Fixing
by: Shi, Yu, et al.
Published: (2025)