6 papers
Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning
Tong Ye, Hang Yu, Tengfei Ma +6
Large Language Models have demonstrated remarkable progress in general-purpose capabilities and can achieve strong performance in specific domains through fine-tuning on domain-spe…
Cuckoo Attack: Stealthy and Persistent Attacks Against AI-IDE
Xinpeng Liu, Junming Liu, Peiyu Liu +5
Modern AI-powered Integrated Development Environments (AI-IDEs) are increasingly defined by an Agent-centric architecture, where an LLM-powered Agent is deeply integrated to autono…
LLM4EFFI: Leveraging Large Language Models to Enhance Code Efficiency and Correctness
Tong Ye, Weigang Huang, Xuhong Zhang +4
Large Language Models (LLMs), particularly Code LLMs, have demonstrated impressive performance in code generation. Current research primarily focuses on the correctness of generate…
Beyond Static Pattern Matching? Rethinking Automatic Cryptographic API Misuse Detection in the Era of LLMs
Yifan Xia, Zichen Xie, Peiyu Liu +4
While the automated detection of cryptographic API misuses has progressed significantly, its precision diminishes for intricate targets due to the reliance on manually defined patt…
A Problem-Oriented Perspective and Anchor Verification for Code Optimization
Tong Ye, Tengfei Ma, Xuhong Zhang +3
Large Language Models (LLMs) have shown remarkable capabilities in solving various programming tasks, such as code generation. However, their potential for code optimization, parti…
Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting
Tong Ye, Yangkai Du, Tengfei Ma +4
Large Language Models (LLMs) have demonstrated remarkable proficiency in generating code. However, the misuse of LLM-generated (synthetic) code has raised concerns in both educatio…