4 papers
No One-Size-Fits-All: Adaptive Code Editing with Feature-Based Strategy Selection
Jun Wan, Zhongxin Liu, Dajun Chen +3
Large language models have shown strong potential in automating code editing (CE) tasks, yet most existing systems rely on a fixed CE strategy (i.e., one-step, search-replace, and…
Multi-level Code Optimization via Mixture of Prompts
Yun Peng, Jun Wan, Jiakun Liu +3
Runtime efficiency is a critical factor that impacts both software quality and user satisfaction. There are many approaches proposed for code optimization to improve runtime effici…
PEACE: Towards Efficient Project-Level Efficiency Optimization via Hybrid Code Editing
Xiaoxue Ren, Jun Wan, Yun Peng +5
Large Language Models (LLMs) have demonstrated significant capability in code generation, but their potential in code efficiency optimization remains underexplored. Previous LLM-ba…
COFFE: A Code Efficiency Benchmark for Code Generation
Yun Peng, Jun Wan, Yichen Li +1
Code generation has largely improved development efficiency in the era of large language models (LLMs). With the ability to follow instructions, current LLMs can be prompted to gen…