Buchumschlag

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Bibliographische Detailangaben
Hauptverfasser: 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
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Computation and Language
Artificial Intelligence
Software Engineering
Online-Zugang:https://arxiv.org/abs/2406.01304
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Inhaltsangabe:
  • 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.

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