Imagen de Portada

ClassEval-T: Evaluating Large Language Models in Class-Level Code Translation

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Xue, Pengyu, Wu, Linhao, Yang, Zhen, Wang, Chengyi, Li, Xiang, Zhang, Yuxiang, Li, Jia, Jin, Ruikai, Pei, Yifei, Shen, Zhaoyan, Lyu, Xiran, Keung, Jacky Wai
Formato:	Preprint
Publicado:	2024
Materias:	Software Engineering
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

ClassEval-Pro: A Cross-Domain Benchmark for Class-Level Code Generation
por: Chen, Yeheng, et al.
Publicado: (2026)

TransLibEval: Demystify Large Language Models' Capability in Third-party Library-targeted Code Translation
por: Xue, Pengyu, et al.
Publicado: (2025)

Fixturize: Bridging the Fixture Gap in Test Generation
por: Wang, Chengyi, et al.
Publicado: (2026)

DebugRepair: Enhancing LLM-Based Automated Program Repair via Self-Directed Debugging
por: Wu, Linhao, et al.
Publicado: (2026)

Exploring and Unleashing the Power of Large Language Models in Automated Code Translation
por: Yang, Zhen, et al.
Publicado: (2024)

R2Code: A Self-Reflective LLM Framework for Requirements-to-Code Traceability
por: Wang, Yifei, et al.
Publicado: (2026)

Chart2Code-MoLA: Efficient Multi-Modal Code Generation via Adaptive Expert Routing
por: Wang, Yifei, et al.
Publicado: (2025)

Hybrid Privacy Policy-Code Consistency Check using Knowledge Graphs and LLMs
por: Mao, Zhenyu, et al.
Publicado: (2025)

DISTINCT: A Description-Guided Branch-Consistency Analysis Framework for Non-Regressive Test Case Generation
por: Xue, Pengyu, et al.
Publicado: (2025)

SysTradeBench: An Iterative Build-Test-Patch Benchmark for Strategy-to-Code Trading Systems with Drift-Aware Diagnostics
por: Cao, Yuchen, et al.
Publicado: (2026)

Data Preparation for Deep Learning based Code Smell Detection: A Systematic Literature Review
por: Zhang, Fengji, et al.
Publicado: (2024)

Fight Fire with Fire: How Much Can We Trust ChatGPT on Source Code-Related Tasks?
por: Yu, Xiao, et al.
Publicado: (2024)

Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study
por: Liu, Shuo, et al.
Publicado: (2024)

Unlocking LLM Repair Capabilities Through Cross-Language Translation and Multi-Agent Refinement
por: Luo, Wenqiang, et al.
Publicado: (2025)

R2ComSync: Improving Code-Comment Synchronization with In-Context Learning and Reranking
por: Yang, Zhen, et al.
Publicado: (2025)

Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach
por: Mao, Zhenyu, et al.
Publicado: (2025)

Towards Requirements Engineering for GenAI-Enabled Software: Bridging Responsibility Gaps through Human Oversight Requirements
por: Mao, Zhenyu, et al.
Publicado: (2025)

Evaluating Software Process Models for Multi-Agent Class-Level Code Generation
por: Shafin, Wasique Islam, et al.
Publicado: (2025)

UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems
por: Zhang, Jingyu, et al.
Publicado: (2026)

Towards Understanding Bugs in Distributed Training and Inference Frameworks for Large Language Models
por: Yu, Xiao, et al.
Publicado: (2025)

Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond
por: Xue, Pengyu, et al.
Publicado: (2024)

RLCoder: Reinforcement Learning for Repository-Level Code Completion
por: Wang, Yanlin, et al.
Publicado: (2024)

ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation
por: Liu, Kaiyuan, et al.
Publicado: (2025)

FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detection
por: Liao, Yihan, et al.
Publicado: (2025)

From Laboratory to Real-World Applications: Benchmarking Agentic Code Reasoning at the Repository Level
por: Li, Jia, et al.
Publicado: (2026)

Practitioners' Expectations on Log Anomaly Detection
por: Ma, Xiaoxue, et al.
Publicado: (2024)

A Comprehensive Study of Bugs in Modern Distributed Deep Learning Systems
por: Ma, Xiaoxue, et al.
Publicado: (2025)

NL in the Middle: Code Translation with LLMs and Intermediate Representations
por: Tai, Chi-en Amy, et al.
Publicado: (2025)

A Systematic Literature Review on Neural Code Translation
por: Chen, Xiang, et al.
Publicado: (2025)

The Influence of Code Smells in Efferent Neighbors on Class Stability
por: Zhang, Zushuai, et al.
Publicado: (2026)

Exploring and Lifting the Robustness of LLM-powered Automated Program Repair with Metamorphic Testing
por: Xue, Pengyu, et al.
Publicado: (2024)

On the Influence of Data Resampling for Deep Learning-Based Log Anomaly Detection: Insights and Recommendations
por: Ma, Xiaoxue, et al.
Publicado: (2024)

InterTrans: Leveraging Transitive Intermediate Translations to Enhance LLM-based Code Translation
por: Macedo, Marcos, et al.
Publicado: (2024)

Scalable Thread-Safety Analysis of Java Classes with CodeQL
por: Jåtten, Bjørnar Haugstad, et al.
Publicado: (2025)

ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code
por: Feng, Jia, et al.
Publicado: (2024)

SolContractEval: A Benchmark for Evaluating Contract-Level Solidity Code Generation
por: Ye, Zhifan, et al.
Publicado: (2025)

When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair
por: Luo, Wenqiang, et al.
Publicado: (2024)

Exposing and Defending Membership Leakage in Vulnerability Prediction Models
por: Liao, Yihan, et al.
Publicado: (2025)

Empirical Insights of Test Selection Metrics under Multiple Testing Objectives and Distribution Shifts
por: Zhang, Jingyu, et al.
Publicado: (2026)

An Empirical Study of Perceptions of General LLMs and Multimodal LLMs on Hugging Face
por: Liu, Yujian, et al.
Publicado: (2026)