Large Language Model-Based Agents for Software Engineering: A Survey
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.
Code (1)
Tasks
AI AgentLanguage ModelingLanguage ModellingLarge Language ModelSurveySimilar Papers 제목 키워드 기반
From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future
With the rise of large language models (LLMs), researchers are increasingly exploring their applications in var ious vertical domains, such as software engineering. LLMs have achieved remarkable success in areas includin…
BenchmarkingCode GenerationDecision MakingVulnerability DetectionAgents in Software Engineering: Survey, Landscape, and Vision
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 stud…
SurveySoftware Development Life Cycle Perspective: A Survey of Benchmarks for Code Large Language Models and Agents
Code large language models (CodeLLMs) and agents have shown great promise in tackling complex software engineering tasks.Compared to traditional software engineering methods, CodeLLMs and agents offer stronger abilities,…
BenchmarkingSelf-Evolving Coding Agents
Large language models are increasingly embedded in software engineering workflows as coding agents that can inspect repositories, invoke tools, execute tests, debug failures, and generate patches. Yet most existing agent…
Dialogue Systems Engineering: A Survey and Future Directions
This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With th…