89 citations · 138 across the 9 of their papers we have counts for
10 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…
FastCoder: Accelerating Repository-level Code Generation via Efficient Retrieval and Verification
Qianhui Zhao, Li Zhang, Fang Liu +6
Code generation is a latency-sensitive task that demands high timeliness. However, with the growing interest and inherent difficulty in repository-level code generation, most exist…
Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities
Xiangping Chen, Xing Hu, Yuan Huang +23
Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language p…
Uncovering Weaknesses in Neural Code Generation
Xiaoli Lian, Shuaisong Wang, Jieping Ma +5
Code generation, the task of producing source code from prompts, has seen significant advancements with the advent of pre-trained large language models (PLMs). Despite these achiev…
Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code
Fang Liu, Yang Liu, Lin Shi +5
The rise of Large Language Models (LLMs) has significantly advanced various applications on software engineering tasks, particularly in code generation. Despite the promising perfo…