CodeV: Issue Resolving with Visual Data

• URL: http://arxiv.org/abs/2412.17315v1
• Date: Mon, 23 Dec 2024 06:17:11 GMT
• Title: CodeV: Issue Resolving with Visual Data
• Authors: Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Pengjie Huang, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang,
• Abstract summary: We propose CodeV, the first approach to leveraging visual data to enhance the issue-resolving capabilities of Large Language Models (LLMs) CodeV resolves each issue by following a two-phase process: data processing and patch generation. We demonstrate the effectiveness of CodeV, as well as provide valuable insights into leveraging visual data to resolve GitHub issues.
• Score: 32.05873957588477
• License:
• 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.

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