activity
20232026
most citedAn Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SE2026

Unseen-Codebases-Domain Data Synthesis and Training Based on Code Graphs

Guangsheng Ou, Qiming Zhang, Sirong Chen +9

In the context of newly release software frameworks, large language models (LLMs) often exhibit poor performance and a high rate of hallucination, as they are not exposed to such e…

cs.AI2025

Data Dependency-Aware Code Generation from Enhanced UML Sequence Diagrams

Wenxin Mao, Zhitao Wang, Long Wang +7

Large language models (LLMs) excel at generating code from natural language (NL) descriptions. However, the plain textual descriptions are inherently ambiguous and often fail to ca…

cs.SE2025★ 1 cited

An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities

Zezhou Yang, Sirong Chen, Cuiyun Gao +4

Code generation aims to automatically generate code snippets of specific programming language according to natural language descriptions. The continuous advancements in deep learni…

cs.SE2023

REEF: A Framework for Collecting Real-World Vulnerabilities and Fixes

Chaozheng Wang, Zongjie Li, Yun Peng +5

Software plays a crucial role in our daily lives, and therefore the quality and security of software systems have become increasingly important. However, vulnerabilities in softwar…

cs.SE2023

Deep Learning Based Code Generation Methods: Literature Review

Zezhou Yang, Sirong Chen, Cuiyun Gao +3

This paper focuses on Code Generation task that aims at generating relevant code fragments according to given natural language descriptions. In the process of software development,…