Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2025

Abstract

Integrating Large Language Models (LLMs) into autonomous agents marks a significant shift in the research landscape by offering cognitive abilities that are competitive with human planning and reasoning. This paper explores the transformative potential of integrating Large Language Models into Multi-Agent (LMA) systems for addressing complex challenges in software engineering (SE). By leveraging the collaborative and specialized abilities of multiple agents, LMA systems enable autonomous problem-solving, improve robustness, and provide scalable solutions for managing the complexity of real-world software projects. In this paper, we conduct a systematic review of recent primary studies to map the current landscape of LMA applications across various stages of the software development lifecycle (SDLC). To illustrate current capabilities and limitations, we perform two case studies to demonstrate the effectiveness of state-of-the-art LMA frameworks. Additionally, we identify critical research gaps and propose a comprehensive research agenda focused on enhancing individual agent capabilities and optimizing agent synergy. Our work outlines a forward-looking vision for developing fully autonomous, scalable, and trustworthy LMA systems, laying the foundation for the evolution of Software Engineering 2.0.

Keywords

Large Language Models, Autonomous Agents, Multi-Agent Systems, Software Engineering

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Areas of Excellence

Digital transformation

Publication

ACM Transactions on Software Engineering and Methodology

Volume

34

Issue

5

First Page

1

Last Page

30

ISSN

1049-331X

Identifier

10.1145/3712003

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3712003

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