9 papers
CodeMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generation
Peiding Wang, Li Zhang, Fang Liu +2
Large language models (LLMs) substantially enhance developer productivity in repository-level code generation through interactive collaboration. However, as interactions progress,…
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…
AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length
Junhang Cheng, Fang Liu, Chengru Wu +1
While Large Language Models (LLMs) have significantly advanced code generation efficiency, they face inherent challenges in balancing performance and inference costs across diverse…
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…