1 citations · 1 across the 9 of their papers we have counts for
10 papers
Token Sugar: Making Source Code Sweeter for LLMs through Token-Efficient Shorthand
Zhensu Sun, Chengran Yang, Xiaoning Du +3
Large language models (LLMs) have shown exceptional performance in code generation and understanding tasks, yet their high computational costs hinder broader adoption. One importan…
Extending Fill-In-the-Middle with Instructions for Steerable Code Completion
Zhensu Sun, Chengran Yang, Chao Peng +4
Code completion models often fail when the developer's intent is under-specified in the code context. To mitigate this, developers frequently use natural language comments to clari…
The Hidden Cost of Readability: How Code Formatting Silently Consumes Your LLM Budget
Dangfeng Pan, Zhensu Sun, Cenyuan Zhang +2
Source code is usually formatted with elements like indentation and newlines to improve readability for human developers. However, these visual aids do not seem to be beneficial fo…
From Code to Courtroom: LLMs as the New Software Judges
Junda He, Jieke Shi, Terry Yue Zhuo +5
Recently, Large Language Models (LLMs) have been increasingly used to automate SE tasks such as code generation and summarization. However, evaluating the quality of LLM-generated…
Identifying and Mitigating API Misuse in Large Language Models
Terry Yue Zhuo, Junda He, Jiamou Sun +4
API misuse in code generated by large language models (LLMs) presents a serious and growing challenge in software development, as although LLMs demonstrate impressive code generati…
FDI: Attack Neural Code Generation Systems through User Feedback Channel
Zhensu Sun, Xiaoning Du, Xiapu Luo +3
Neural code generation systems have recently attracted increasing attention to improve developer productivity and speed up software development. Typically, these systems maintain a…
Towards Agentic Runtime Healing
Zhensu Sun, Haotian Zhu, Bowen Xu +3
Self-healing systems have long been a focus of research, aiming to enable software to recover from unexpected runtime errors without human intervention. Traditional approaches rely…
AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation
Zhensu Sun, Xiaoning Du, Zhou Yang +2
Artificial Intelligence (AI) models have emerged as another important audience for programming languages alongside humans and machines, as we enter the era of large language models…
When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference
Zhensu Sun, Xiaoning Du, Fu Song +2
Leveraging recent advancements in large language models, modern neural code completion models have demonstrated the capability to generate highly accurate code suggestions. However…
Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?
Terry Yue Zhuo, Xiaoning Du, Zhenchang Xing +4
Recent breakthroughs in pre-trained code models, such as CodeBERT and Codex, have shown their superior performance in various downstream tasks. The correctness and unambiguity of A…