Agents in Software Engineering: Survey, Landscape, and Vision
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917782669492224 |
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| author | Wang, Yanlin Zhong, Wanjun Huang, Yanxian Shi, Ensheng Yang, Min Chen, Jiachi Li, Hui Ma, Yuchi Wang, Qianxiang Zheng, Zibin |
| author_facet | Wang, Yanlin Zhong, Wanjun Huang, Yanxian Shi, Ensheng Yang, Min Chen, Jiachi Li, Hui Ma, Yuchi Wang, Qianxiang Zheng, Zibin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09030 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Agents in Software Engineering: Survey, Landscape, and Vision Wang, Yanlin Zhong, Wanjun Huang, Yanxian Shi, Ensheng Yang, Min Chen, Jiachi Li, Hui Ma, Yuchi Wang, Qianxiang Zheng, Zibin Software Engineering Artificial Intelligence Computation and Language 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. |
| title | Agents in Software Engineering: Survey, Landscape, and Vision |
| topic | Software Engineering Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2409.09030 |