1 citations · 1 across the 9 of their papers we have counts for
10 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…
PracRepair: LLM-Empowered Automated Program Repair Inspired by Human-Like Debugging Practices
Yu Cheng, Zhongxin Liu, Zhenchang Xing +3
As software systems grow in scale and complexity, debugging and repair remain costly and time-consuming. Large language models (LLMs) have advanced automated program repair (APR),…
CoRE: A Fine-Grained Code Reasoning Benchmark Beyond Output Prediction
Jun Gao, Yun Peng, Qian Qiao +6
Despite strong performance on code generation tasks, it remains unclear whether large language models (LLMs) genuinely reason about code execution. Existing code reasoning benchmar…
An Iterative Test-and-Repair Framework for Competitive Code Generation
Lingxiao Tang, Muyang Ye, Zhaoyang Chu +4
Large language models (LLMs) have made remarkable progress in code generation, but competitive programming remains a challenge. Recent training-based methods have improved code gen…
ExecVerify: White-Box RL with Verifiable Stepwise Rewards for Code Execution Reasoning
Lingxiao Tang, He Ye, Zhaoyang Chu +4
Code LLMs still struggle with code execution reasoning, especially in smaller models. Existing methods rely on supervised fine-tuning (SFT) with teacher-generated explanations, pri…
\texttt{ReMind}: Understanding Deductive Code Reasoning in LLMs
Jun Gao, Yun Peng, Xiaoxue Ren
Large Language Models (LLMs) have achieved remarkable progress in code-related tasks. Despite their advancement, empirical evidence reveals that they still struggle with \emph{dedu…
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…
The Bidirectional Process Reward Model
Lingyin Zhang, Jun Gao, Xiaoxue Ren +1
Process Reward Models (PRMs), which assign fine-grained scores to intermediate reasoning steps within a solution trajectory, have emerged as a promising approach to enhance the rea…
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…