Dong Chen
2025
CodeV: Issue Resolving with Visual Data
Linhao Zhang
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Daoguang Zan
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Quanshun Yang
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Zhirong Huang
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Dong Chen
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Bo Shen
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Tianyu Liu
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Yongshun Gong
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Huang Pengjie
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Xudong Lu
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Guangtai Liang
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Lizhen Cui
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Qianxiang Wang
Findings of the Association for Computational Linguistics: ACL 2025
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.
2015
TwittDict: Extracting Social Oriented Keyphrase Semantics from Twitter
Suppawong Tuarob
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Wanghuan Chu
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Dong Chen
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Conrad Tucker
Proceedings of the ACL 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction
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Co-authors
- Wanghuan Chu 1
- Lizhen Cui 1
- Yongshun Gong 1
- Zhirong Huang 1
- Guangtai Liang 1
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