3 papers
cs.SE2025
Prompt Alchemy: Automatic Prompt Refinement for Enhancing Code Generation
Sixiang Ye, Zeyu Sun, Guoqing Wang +4
Code generation has emerged as a key task to automate software development by converting high-level descriptions into executable code. Large language models (LLMs) excel at this bu…
cs.SE2025
LLMs are Bug Replicators: An Empirical Study on LLMs' Capability in Completing Bug-prone Code
Liwei Guo, Sixiang Ye, Zeyu Sun +6
Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, the training data used to develop these models often contain a significant amount…
cs.DB2024
The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving
Pai Zeng, Zhenyu Ning, Jieru Zhao +5
We survey the large language model (LLM) serving area to understand the intricate dynamics between cost-efficiency and accuracy, which is magnified by the growing need for longer c…