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arXiv 2409.09030cs.SEcs.AIcs.CL
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软件工程中的智能体:综述、全景图与愿景

Agents in Software Engineering: Survey, Landscape, and Vision

Sun Yat-sen University(中山大学);Xi’an Jiaotong University(西安交通大学);Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences(中国科学院深圳先进技术研究院);Xiamen University(厦门大学);Huawei Cloud Computing Technologies Co., Ltd.(华为云计算技术有限公司)

Yanlin Wang, Wanjun Zhong, Yanxian Huang, Ensheng Shi, Min Yang, Jiachi Chen, Hui Li, Yuchi Ma, Qianxiang Wang, Zibin Zheng

AI总结:

本文首次综述了基于LLM的智能体在软件工程中的应用,提出了包含感知、记忆和动作三个模块的智能体框架,并总结了当前挑战与未来机遇。

摘要:

近年来,大型语言模型(LLMs)取得了显著成功,并广泛应用于各种下游任务中,特别是在软件工程(SE)领域的任务中。我们发现,许多结合LLMs与SE的研究已经明确或隐含地采用了智能体的概念。然而,目前缺乏深入的综述来梳理现有工作的发展脉络,分析现有工作如何结合基于LLM的智能体技术来优化各种任务,并阐明SE中基于LLM的智能体框架。在本文中,我们首次对结合基于LLM的智能体与SE的研究进行了综述,并提出了一个SE中基于LLM的智能体框架,该框架包含三个关键模块:感知、记忆和动作。我们还总结了结合这两个领域当前面临的挑战,并针对现有挑战提出了未来的机遇。我们在以下GitHub仓库中维护了相关论文:https://github.com/DeepSoftwareAnalytics/Awesome-Agent4SE。

英文摘要:

In recent years, Large Language Models (LLMs) have achieved remarkable success and have been widely used in various downstream tasks, especially in the tasks of the software engineering (SE) field. We find that many studies combining LLMs with SE have employed the concept of agents either explicitly or implicitly. However, there is a lack of an in-depth survey to sort out the development context of existing works, analyze how existing works combine the LLM-based agent technologies to optimize various tasks, and clarify the framework of LLM-based agents in SE. In this paper, we conduct the first survey of the studies on combining LLM-based agents with SE and present a framework of LLM-based agents in SE which includes three key modules: perception, memory, and action. We also summarize the current challenges in combining the two fields and propose future opportunities in response to existing challenges. We maintain a GitHub repository of the related papers at: https://github.com/DeepSoftwareAnalytics/Awesome-Agent4SE.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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