7 citations · 9 across the 4 of their papers we have counts for
4 papers
Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study
Shuo Liu, Jacky Keung, Zhen Yang +3
Compared to Full-Model Fine-Tuning (FMFT), Parameter Efficient Fine-Tuning (PEFT) has demonstrated superior performance and lower computational overhead in several code understandi…
Large Language Model-Aware In-Context Learning for Code Generation
Jia Li, Ge Li, Chongyang Tao +3
Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation. LLMs take a prompt consisting of requirement-code examples and a new requir…
ZC3: Zero-Shot Cross-Language Code Clone Detection
Jia Li, Chongyang Tao, Zhi Jin +2
Developers introduce code clones to improve programming productivity. Many existing studies have achieved impressive performance in monolingual code clone detection. However, durin…
Learning to Recommend Method Names with Global Context
Fang Liu, Ge Li, Zhiyi Fu +3
In programming, the names for the program entities, especially for the methods, are the intuitive characteristic for understanding the functionality of the code. To ensure the read…