@inproceedings{zhang-etal-2025-codev,
    title = "{C}ode{V}: Issue Resolving with Visual Data",
    author = "Zhang, Linhao  and
      Zan, Daoguang  and
      Yang, Quanshun  and
      Huang, Zhirong  and
      Chen, Dong  and
      Shen, Bo  and
      Liu, Tianyu  and
      Gong, Yongshun  and
      Pengjie, Huang  and
      Lu, Xudong  and
      Liang, Guangtai  and
      Cui, Lizhen  and
      Wang, Qianxiang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.384/",
    pages = "7350--7361",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) have advanced rapidly in recent years, with their applications in software engineering expanding to more complex repository-level tasks. GitHub issue resolving is a key challenge among these tasks. While recent approaches have made progress on this task, they focus on textual data within issues, neglecting visual data. However, this visual data is crucial for resolving issues as it conveys additional knowledge that text alone cannot. We propose CodeV, the first approach to leveraging visual data to enhance the issue-resolving capabilities of LLMs. CodeV resolves each issue by following a two-phase process: data processing and patch generation. To evaluate CodeV, we construct a benchmark for visual issue resolving, namely Visual SWE-bench. Through extensive experiments, we demonstrate the effectiveness of CodeV, as well as provide valuable insights into leveraging visual data to resolve GitHub issues."
}