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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.01304 |
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| _version_ | 1866913384806481920 |
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| author | Chen, Dong Lin, Shaoxin Zeng, Muhan Zan, Daoguang Wang, Jian-Gang Cheshkov, Anton Sun, Jun Yu, Hao Dong, Guoliang Aliev, Artem Wang, Jie Cheng, Xiao Liang, Guangtai Ma, Yuchi Bian, Pan Xie, Tao Wang, Qianxiang |
| author_facet | Chen, Dong Lin, Shaoxin Zeng, Muhan Zan, Daoguang Wang, Jian-Gang Cheshkov, Anton Sun, Jun Yu, Hao Dong, Guoliang Aliev, Artem Wang, Jie Cheng, Xiao Liang, Guangtai Ma, Yuchi Bian, Pan Xie, Tao Wang, Qianxiang |
| contents | GitHub issue resolving recently has attracted significant attention from academia and industry. SWE-bench is proposed to measure the performance in resolving issues. In this paper, we propose CodeR, which adopts a multi-agent framework and pre-defined task graphs to Repair & Resolve reported bugs and add new features within code Repository. On SWE-bench lite, CodeR is able to solve 28.33% of issues, when submitting only once for each issue. We examine the performance impact of each design of CodeR and offer insights to advance this research direction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01304 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CodeR: Issue Resolving with Multi-Agent and Task Graphs Chen, Dong Lin, Shaoxin Zeng, Muhan Zan, Daoguang Wang, Jian-Gang Cheshkov, Anton Sun, Jun Yu, Hao Dong, Guoliang Aliev, Artem Wang, Jie Cheng, Xiao Liang, Guangtai Ma, Yuchi Bian, Pan Xie, Tao Wang, Qianxiang Computation and Language Artificial Intelligence Software Engineering GitHub issue resolving recently has attracted significant attention from academia and industry. SWE-bench is proposed to measure the performance in resolving issues. In this paper, we propose CodeR, which adopts a multi-agent framework and pre-defined task graphs to Repair & Resolve reported bugs and add new features within code Repository. On SWE-bench lite, CodeR is able to solve 28.33% of issues, when submitting only once for each issue. We examine the performance impact of each design of CodeR and offer insights to advance this research direction. |
| title | CodeR: Issue Resolving with Multi-Agent and Task Graphs |
| topic | Computation and Language Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2406.01304 |