Related papers: Large Language Model-Based Agents for Software Engineering: A Survey

Large Language Model-Based Agents for Software Engineering: A Survey

URL: http://arxiv.org/abs/2409.02977v1
Date: Wed, 4 Sep 2024 15:59:41 GMT
Title: Large Language Model-Based Agents for Software Engineering: A Survey
Authors: Junwei Liu, Kaixin Wang, Yixuan Chen, Xin Peng, Zhenpeng Chen, Lingming Zhang, Yiling Lou,
Abstract summary: The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. We collect 106 papers and categorize them from two perspectives, i.e., the SE and agent perspectives. In addition, we discuss open challenges and future directions in this critical domain.
Score: 20.258244647363544
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the capabilities of perceiving and utilizing external resources and tools. To date, LLM-based agents have been applied and shown remarkable effectiveness in Software Engineering (SE). The synergy between multiple agents and human interaction brings further promise in tackling complex real-world SE problems. In this work, we present a comprehensive and systematic survey on LLM-based agents for SE. We collect 106 papers and categorize them from two perspectives, i.e., the SE and agent perspectives. In addition, we discuss open challenges and future directions in this critical domain. The repository of this survey is at https://github.com/FudanSELab/Agent4SE-Paper-List.

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