LLM-Based Multi-Agent Systems for Software Engineering: Vision and the Road Ahead

• URL: http://arxiv.org/abs/2404.04834v2
• Date: Mon, 07 Oct 2024 10:28:25 GMT
• Title: LLM-Based Multi-Agent Systems for Software Engineering: Vision and the Road Ahead
• Authors: Junda He, Christoph Treude, David Lo,
• Abstract summary: This paper envisions the evolution of Multi-Agent (LMA) systems in addressing complex and multi-faceted software engineering challenges. By examining the role of LMA systems in future software engineering practices, this vision paper highlights the potential applications and emerging challenges.
• Score: 14.834072370183106
• License:
• Abstract: Integrating Large Language Models(LLMs) into autonomous agents marks a significant shift in the research landscape by offering cognitive abilities competitive to human planning and reasoning. This paper envisions the evolution of LLM-based Multi-Agent (LMA) systems in addressing complex and multi-faceted software engineering challenges. LMA systems introduce numerous benefits, including enhanced robustness through collaborative cross-examination, autonomous problem-solving, and scalable solutions to complex software projects. By examining the role of LMA systems in future software engineering practices, this vision paper highlights the potential applications and emerging challenges. We further point to specific opportunities for research and conclude with a research agenda with a set of research questions to guide future research directions.

Related papers

• Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents [67.07177243654485]
  This survey collects and analyzes the different threats faced by large language models-based agents. We identify six key features of LLM-based agents, based on which we summarize the current research progress. We select four representative agents as case studies to analyze the risks they may face in practical use.
  arXiv  Detail & Related papers  (2024-11-14T15:40:04Z)
• The Systems Engineering Approach in Times of Large Language Models [6.333694023236363]
  Large Language Models (LLMs) to address critical societal problems requires adopting this technology into socio-technical systems. This paper introduces the challenges LLMs generate and surveys systems research efforts for engineering AI-based systems.
  arXiv  Detail & Related papers  (2024-11-13T22:10:07Z)
• A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks [74.52259252807191]
  Multimodal Large Language Models (MLLMs) address the complexities of real-world applications far beyond the capabilities of single-modality systems. This paper systematically sorts out the applications of MLLM in multimodal tasks such as natural language, vision, and audio.
  arXiv  Detail & Related papers  (2024-08-02T15:14:53Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• Large Multimodal Agents: A Survey [78.81459893884737]
  Large language models (LLMs) have achieved superior performance in powering text-based AI agents. There is an emerging research trend focused on extending these LLM-powered AI agents into the multimodal domain. This review aims to provide valuable insights and guidelines for future research in this rapidly evolving field.
  arXiv  Detail & Related papers  (2024-02-23T06:04:23Z)
• LLM Multi-Agent Systems: Challenges and Open Problems [14.174833743880244]
  This paper explores existing works of multi-agent systems and identifies challenges that remain inadequately addressed. By leveraging the diverse capabilities and roles of individual agents within a multi-agent system, these systems can tackle complex tasks through collaboration. We discuss optimizing task allocation, fostering robust reasoning through iterative debates, managing complex and layered context information, and enhancing memory management to support the intricate interactions within multi-agent systems.
  arXiv  Detail & Related papers  (2024-02-05T23:06:42Z)
• Building Guardrails for Large Language Models [19.96292920696796]
  Guardrails, which filter the inputs or outputs of LLMs, have emerged as a core safeguarding technology. This position paper takes a deep look at current open-source solutions (Llama Guard, Nvidia NeMo, Guardrails AI) and discusses the challenges and the road towards building more complete solutions.
  arXiv  Detail & Related papers  (2024-02-02T16:35:00Z)
• A Survey on Large Language Model based Autonomous Agents [105.2509166861984]
  Large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This paper delivers a systematic review of the field of LLM-based autonomous agents from a holistic perspective. We present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering.
  arXiv  Detail & Related papers  (2023-08-22T13:30:37Z)
• Towards an Understanding of Large Language Models in Software Engineering Tasks [29.30433406449331]
  Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in text generation and reasoning tasks. The evaluation and optimization of LLMs in software engineering tasks, such as code generation, have become a research focus. This paper comprehensively investigate and collate the research and products combining LLMs with software engineering.
  arXiv  Detail & Related papers  (2023-08-22T12:37:29Z)
• A Survey on Multimodal Large Language Models [71.63375558033364]
  Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot. This paper aims to trace and summarize the recent progress of MLLMs.
  arXiv  Detail & Related papers  (2023-06-23T15:21:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.