7 citations · 7 across the 2 of their papers we have counts for
7 papers
Decoding Human-LLM Collaboration in Coding: An Empirical Study of Multi-Turn Conversations in the Wild
Binquan Zhang, Li Zhang, Haoyuan Zhang +5
Large language models (LLMs) are increasingly acting as dynamic conversational interfaces, supporting multi-turn interactions that mimic human-like conversation and facilitate comp…
Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation
Binquan Zhang, Li Zhang, Zhiwen Luo +4
Large language models (LLMs) have demonstrated impressive performance in code generation, particularly when augmented with chain-of-thought (CoT) prompting techniques. They break d…
RepoScope: Leveraging Call Chain-Aware Multi-View Context for Repository-Level Code Generation
Yang Liu, Li Zhang, Fang Liu +6
Repository-level code generation aims to generate code within the context of a specified repository. Existing approaches typically employ retrieval-augmented generation (RAG) techn…
EfficientEdit: Accelerating Code Editing via Edit-Oriented Speculative Decoding
Peiding Wang, Li Zhang, Fang Liu +7
Large Language Models (LLMs) have demonstrated remarkable capabilities in code editing, substantially enhancing software development productivity. However, the inherent complexity…
Revolutionizing Newcomers' Onboarding Process in OSS Communities: The Future AI Mentor
Xin Tan, Xiao Long, Yinghao Zhu +3
Onboarding newcomers is vital for the sustainability of open-source software (OSS) projects. To lower barriers and increase engagement, OSS projects have dedicated experts who prov…
CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation
Peiding Wang, Li Zhang, Fang Liu +4
Large Language Models (LLMs) have demonstrated exceptional performance in code generation tasks and have become indispensable programming assistants for developers. However, existi…