Authors: Yanxian Huang, Wanjun Zhong, Ensheng Shi, Min Yang, Jiachi Chen, Hui Li, Yuchi Ma, Qianxiang Wang, Zibin Zheng, Yanlin Wang
Abstract: 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.
Source: http://arxiv.org/abs/2409.09030v1
