Chao Peng
Chao Peng
Home
News
Featured Publications
Service
Teaching
Awards
Work Experience
CV
简历
Light
Dark
Automatic
Large Language Models
SoRFT: Issue Resolving with Subtask-oriented Reinforced Fine-Tuning
Mainstream issue-resolving frameworks predominantly rely on commercial models, leading to high costs and privacy concerns. Existing …
Zexiong Ma
,
Chao Peng
,
Pengfei Gao
,
Xiangxin Meng
,
Yanzhen Zou
,
Bing Xie
PDF
Cite
DOI
An LLM-based Agent for Reliable Docker Environment Configuration
Environment configuration is a critical yet time-consuming step in software development, especially when dealing with unfamiliar code …
Ruida Hu
,
Chao Peng
,
Xinchen Wang
,
Cuiyun Gao
PDF
Cite
Code
DOI
A Real-World Benchmark for Evaluating Fine-Grained Issue Solving Capabilities of Large Language Models
Automatically resolving software issues is crucial for software development in practice, impacting the software quality and user …
Ruida Hu
,
Chao Peng
,
Jingyi Ren
,
Bo Jiang
,
Xiangxin Meng
,
Qinyun Wu
,
Pengfei Gao
,
Xinchen Wang
,
Cuiyun Gao
PDF
Cite
DOI
AEGIS: An Agent-based Framework for General Bug Reproduction from Issue Descriptions
In software maintenance, bug reproduction is essential for effective fault localization and repair. Manually writing reproduction …
Xinchen Wang
,
Pengfei Gao
,
Xiangxin Meng
,
Chao Peng
,
Ruida Hu
,
Yun Lin
,
Cuiyun Gao
PDF
Cite
DOI
ContextModule: Improving Code Completion via Repository-level Contextual Information
Large Language Models (LLMs) have demonstrated impressive capabilities in code completion tasks, where they assist developers by …
Zhanming Guan
,
Junlin Liu
,
Jierui Liu
,
Chao Peng
,
Dexin Liu
,
Ningyuan Sun
,
Bo Jiang
,
Wenchao Li
,
Jie Liu
,
Hang Zhu
PDF
Cite
DOI
An Empirical Study on LLM-based Agents for Automated Bug Fixing
Large language models (LLMs) and LLM-based Agents have been applied to fix bugs automatically, demonstrating the capability in …
Xiangxin Meng
,
Zexiong Ma
,
Pengfei Gao
,
Chao Peng
PDF
Cite
DOI
MarsCode Agent: AI-native Automated Bug Fixing
Recent advances in large language models (LLMs) have shown significant potential to automate various software development tasks, …
Yizhou Liu
,
Pengfei Gao
,
Xinchen Wang
,
Jie Liu
,
Yexuan Shi
,
Zhao Zhang
,
Chao Peng
PDF
Cite
DOI
Cite
×