5 citations · 15 across the 25 of their papers we have counts for
11 papers · 1 filter
Top General Performance = Top Domain Performance? DomainCodeBench: A Multi-domain Code Generation Benchmark
Dewu Zheng, Yanlin Wang, Ensheng Shi +4
With the rapid advancement of large language models (LLMs), extensive research has been conducted to investigate the code generation capabilities of LLMs. However, existing efforts…
RepoTransBench: A Real-World Multilingual Benchmark for Repository-Level Code Translation
Yanli Wang, Yanlin Wang, Suiquan Wang +8
Repository-level code translation refers to translating an entire code repository from one programming language to another while preserving the functionality of the source reposito…
FastFixer: An Efficient and Effective Approach for Repairing Programming Assignments
Fang Liu, Zhenwei Liu, Qianhui Zhao +6
Providing personalized and timely feedback for student's programming assignments is useful for programming education. Automated program repair (APR) techniques have been used to fi…
Agents in Software Engineering: Survey, Landscape, and Vision
Yanlin Wang, Wanjun Zhong, Yanxian Huang +7
In recent years, Large Language Models (LLMs) have achieved remarkable success and have been widely used in various downstream tasks, especially in the tasks of the software engine…
LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation
Ziyao Zhang, Yanlin Wang, Chong Wang +2
Code generation aims to automatically generate code from input requirements, significantly enhancing development efficiency. Recent large language models (LLMs) based approaches ha…
Smaller but Better: Self-Paced Knowledge Distillation for Lightweight yet Effective LCMs
Yujia Chen, Yang Ye, Zhongqi Li +2
Large code models (LCMs) have remarkably advanced the field of code generation. Despite their impressive capabilities, they still face practical deployment issues, such as high inf…
When to Stop? Towards Efficient Code Generation in LLMs with Excess Token Prevention
Lianghong Guo, Yanlin Wang, Ensheng Shi +6
Code generation aims to automatically generate code snippets that meet given natural language requirements and plays an important role in software development. Although Code LLMs h…
RLCoder: Reinforcement Learning for Repository-Level Code Completion
Yanlin Wang, Yanli Wang, Daya Guo +4
Repository-level code completion aims to generate code for unfinished code snippets within the context of a specified repository. Existing approaches mainly rely on retrieval-augme…
Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models
Yanlin Wang, Tianyue Jiang, Mingwei Liu +5
Large language models (LLMs) have brought a paradigm shift to the field of code generation, offering the potential to enhance the software development process. However, previous re…
HumanEvo: An Evolution-aware Benchmark for More Realistic Evaluation of Repository-level Code Generation
Dewu Zheng, Yanlin Wang, Ensheng Shi +4
To evaluate the repository-level code generation capabilities of Large Language Models (LLMs) in complex real-world software development scenarios, many evaluation methods have bee…
Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments
Qianhui Zhao, Fang Liu, Li Zhang +6
Automated generation of feedback on programming assignments holds significant benefits for programming education, especially when it comes to advanced assignments. Automated Progra…