Saved in:
| Main Authors: | Zhang, Binquan, Zhang, Li, Zhang, Haoyuan, Liu, Fang, Wang, Song, Shen, Bo, Fu, An, Shi, Lin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.10493 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
An Empirical Study of Interaction Smells in Multi-Turn Human-LLM Collaborative Code Generation
by: Zhang, Binquan, et al.
Published: (2026)
by: Zhang, Binquan, et al.
Published: (2026)
Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation
by: Zhang, Binquan, et al.
Published: (2025)
by: Zhang, Binquan, et al.
Published: (2025)
EfficientEdit: Accelerating Code Editing via Edit-Oriented Speculative Decoding
by: Wang, Peiding, et al.
Published: (2025)
by: Wang, Peiding, et al.
Published: (2025)
CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation
by: Wang, Peiding, et al.
Published: (2025)
by: Wang, Peiding, et al.
Published: (2025)
An Empirical Study of Bugs in Modern LLM Agent Frameworks
by: Zhu, Xinxue, et al.
Published: (2026)
by: Zhu, Xinxue, et al.
Published: (2026)
Copilot Arena: A Platform for Code LLM Evaluation in the Wild
by: Chi, Wayne, et al.
Published: (2025)
by: Chi, Wayne, et al.
Published: (2025)
LLMs are Bug Replicators: An Empirical Study on LLMs' Capability in Completing Bug-prone Code
by: Guo, Liwei, et al.
Published: (2025)
by: Guo, Liwei, et al.
Published: (2025)
Towards Realistic Project-Level Code Generation via Multi-Agent Collaboration and Semantic Architecture Modeling
by: Zhao, Qianhui, et al.
Published: (2025)
by: Zhao, Qianhui, et al.
Published: (2025)
RepoScope: Leveraging Call Chain-Aware Multi-View Context for Repository-Level Code Generation
by: Liu, Yang, et al.
Published: (2025)
by: Liu, Yang, et al.
Published: (2025)
An Empirical Study on Challenges for LLM Application Developers
by: Chen, Xiang, et al.
Published: (2024)
by: Chen, Xiang, et al.
Published: (2024)
Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild
by: Liu, Yue, et al.
Published: (2026)
by: Liu, Yue, et al.
Published: (2026)
Code Review Automation Via Multi-task Federated LLM -- An Empirical Study
by: Kumar, Jahnavi, et al.
Published: (2024)
by: Kumar, Jahnavi, et al.
Published: (2024)
Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code
by: Liu, Fang, et al.
Published: (2024)
by: Liu, Fang, et al.
Published: (2024)
An Empirical Study on the Potential of LLMs in Automated Software Refactoring
by: Liu, Bo, et al.
Published: (2024)
by: Liu, Bo, et al.
Published: (2024)
AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length
by: Cheng, Junhang, et al.
Published: (2025)
by: Cheng, Junhang, et al.
Published: (2025)
Demystifying Errors in LLM Reasoning Traces: An Empirical Study of Code Execution Simulation
by: Abdollahi, Mohammad, et al.
Published: (2025)
by: Abdollahi, Mohammad, et al.
Published: (2025)
Engineering Pitfalls in AI Coding Tools: An Empirical Study of Bugs in Claude Code, Codex, and Gemini CLI
by: Zhang, Ruixin, et al.
Published: (2026)
by: Zhang, Ruixin, et al.
Published: (2026)
An Empirical Study on Influence-Based Pretraining Data Selection for Code Large Language Models
by: Xing, Chengli, et al.
Published: (2026)
by: Xing, Chengli, et al.
Published: (2026)
Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation
by: Yan, Hao, et al.
Published: (2025)
by: Yan, Hao, et al.
Published: (2025)
An Empirical Study of LLM-Based Code Clone Detection
by: Zhu, Wenqing, et al.
Published: (2025)
by: Zhu, Wenqing, et al.
Published: (2025)
Failure-Aware Enhancements for Large Language Model (LLM) Code Generation: An Empirical Study on Decision Framework
by: Shen, Jianru, et al.
Published: (2026)
by: Shen, Jianru, et al.
Published: (2026)
WildCode: An Empirical Analysis of Code Generated by ChatGPT
by: Khanmohammadi, Kobra, et al.
Published: (2025)
by: Khanmohammadi, Kobra, et al.
Published: (2025)
GraphCodeAgent: Dual Graph-Guided LLM Agent for Retrieval-Augmented Repo-Level Code Generation
by: Li, Jia, et al.
Published: (2025)
by: Li, Jia, et al.
Published: (2025)
Do AI Coding Agents Log Like Humans? An Empirical Study
by: Ouatiti, Youssef Esseddiq, et al.
Published: (2026)
by: Ouatiti, Youssef Esseddiq, et al.
Published: (2026)
Figma2Code: Automating Multimodal Design to Code in the Wild
by: Gui, Yi, et al.
Published: (2026)
by: Gui, Yi, et al.
Published: (2026)
Detect--Repair--Verify for LLM-Generated Code: A Multi-Language, Multi-Granularity Empirical Study
by: Cheng, Cheng
Published: (2026)
by: Cheng, Cheng
Published: (2026)
A Scalable Benchmark for Repository-Oriented Long-Horizon Conversational Context Management
by: Liu, Yang, et al.
Published: (2026)
by: Liu, Yang, et al.
Published: (2026)
Explaining Explanation: An Empirical Study on Explanation in Code Reviews
by: Widyasari, Ratnadira, et al.
Published: (2023)
by: Widyasari, Ratnadira, et al.
Published: (2023)
LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning
by: Hou, Bo, et al.
Published: (2025)
by: Hou, Bo, et al.
Published: (2025)
An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding
by: Shang, Xiuwei, et al.
Published: (2025)
by: Shang, Xiuwei, et al.
Published: (2025)
Unveiling the Landscape of LLM Deployment in the Wild: An Empirical Study
by: Hou, Xinyi, et al.
Published: (2025)
by: Hou, Xinyi, et al.
Published: (2025)
Adaptive Confidence Gating in Multi-Agent Collaboration for Efficient and Optimized Code Generation
by: Zhang, Haoji, et al.
Published: (2026)
by: Zhang, Haoji, et al.
Published: (2026)
Rethinking Code Review Workflows with LLM Assistance: An Empirical Study
by: Aðalsteinsson, Fannar Steinn, et al.
Published: (2025)
by: Aðalsteinsson, Fannar Steinn, et al.
Published: (2025)
CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation
by: Pan, Ruwei, et al.
Published: (2025)
by: Pan, Ruwei, et al.
Published: (2025)
Detect Repair Verify for Securing LLM Generated Code: A Multi-Language Empirical Study
by: Cheng, Cheng
Published: (2026)
by: Cheng, Cheng
Published: (2026)
Benchmarking and Studying the LLM-based Code Review
by: Zeng, Zhengran, et al.
Published: (2025)
by: Zeng, Zhengran, et al.
Published: (2025)
An Empirical Study of the Non-determinism of ChatGPT in Code Generation
by: Ouyang, Shuyin, et al.
Published: (2023)
by: Ouyang, Shuyin, et al.
Published: (2023)
Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale
by: Liu, Yi, et al.
Published: (2026)
by: Liu, Yi, et al.
Published: (2026)
Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study
by: Liu, Shuo, et al.
Published: (2024)
by: Liu, Shuo, et al.
Published: (2024)
Turning the Tide: Repository-based Code Reflection
by: Zhang, Wei, et al.
Published: (2025)
by: Zhang, Wei, et al.
Published: (2025)
Similar Items
-
An Empirical Study of Interaction Smells in Multi-Turn Human-LLM Collaborative Code Generation
by: Zhang, Binquan, et al.
Published: (2026) -
Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation
by: Zhang, Binquan, et al.
Published: (2025) -
EfficientEdit: Accelerating Code Editing via Edit-Oriented Speculative Decoding
by: Wang, Peiding, et al.
Published: (2025) -
CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation
by: Wang, Peiding, et al.
Published: (2025) -
An Empirical Study of Bugs in Modern LLM Agent Frameworks
by: Zhu, Xinxue, et al.
Published: (2026)