Papers by Quanshun Yang
CodeV: Issue Resolving with Visual Data (2025.findings-acl)
Copied to clipboard
Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Huang Pengjie, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang
| Challenge: | Large Language Models (LLMs) have expanded to more complex repository-level tasks. |
| Approach: | They propose a first approach to leveraging visual data to enhance the issue-resolving capabilities of Large Language Models (LLMs) they demonstrate the effectiveness of CodeV and provide valuable insights into leveraging visualization to resolve GitHub issues. |
| Outcome: | The proposed approach improves the issue-resolving capabilities of Large Language Models (LLMs) by using visual data. |