1 citations · 1 across the 4 of their papers we have counts for
5 papers
MultiFixer: A Coordinator-Proposer Based Multi-Agent Framework For Fixing Multi-Hunk Bugs
Haichuan Hu, Chunrong Fang, Ye Shang +5
Automated Program Repair (APR) has benefited greatly from Large Language Models (LLMs), but existing LLM-based APR methods still struggle with multi-hunk bugs that require coordina…
Learning from Repair Experience: Self-Evolving Agents for Automated Vulnerability Repair
Haichuan Hu, Guoqing Xie, Quanjun Zhang +5
Large Language Models (LLMs) have shown promise for automated vulnerability repair (AVR), but they still face several limitations, including the lack of intra-vulnerability experie…
CL4SE: Benchmarking Context Learning on Software Engineering
Haichuan Hu, Quanjun Zhang, Ye Shang +4
Context engineering has emerged as a pivotal paradigm for unlocking the potential of Large Language Models (LLMs) in Software Engineering (SE) tasks, enabling performance gains at…
Efficient Prompt Compression with Evaluator Heads for Long-Context Transformer Inference
Weizhi Fei, Xueyan Niu, Guoqing Xie +3
Although applications involving long-context inputs are crucial for the effective utilization of large language models (LLMs), they also result in increased computational costs and…
Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding
Weizhi Fei, Xueyan Niu, Guoqing Xie +4
Current Large Language Models (LLMs) face inherent limitations due to their pre-defined context lengths, which impede their capacity for multi-hop reasoning within extensive textua…