Towards LLM-augmented multiagent systems for agile software engineering

A cognitive multi-agent ecosystem designed for efficient software engineering using Agile methodologies can significantly improve software development processes. Key components include the integration of Multi-Agent Systems (MAS) and Large Language Models (LLMs), utilizing Dynamic Context techniques for agent profiling, and Theory of Mind to enhance collaboration. The CogniSim Ecosystem analyzes problems, proposes solutions, constructs and validates plans, and coordinates specialized agents playing roles such as developers, executors, quality checkers, and methodology reviewers. These agents produce documentation, models, and diagrams (e.g., UML) while adhering to predefined quality and performance measures. The ecosystem also simulates the impact of various team configurations on problem-solving effectiveness, helping organizations identify optimal team structures. Case studies and simulations demonstrate its practical applications.

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Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1145/3691620.3695336
Citations
13
Primary Topic
Multi-Agent Systems and Negotiation
Type
article
Field-Weighted Citation Impact
4.52
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article

Towards LLM-augmented multiagent systems for agile software engineering

Jarosław A. Chudziak, Konrad Cinkusz
13 citations
Multi-Agent Systems and Negotiation
4.52
article

Towards LLM-augmented multiagent systems for agile software engineering

Jarosław A. Chudziak, Konrad Cinkusz
article en
13 citations

Abstract

A cognitive multi-agent ecosystem designed for efficient software engineering using Agile methodologies can significantly improve software development processes. Key components include the integration of Multi-Agent Systems (MAS) and Large Language Models (LLMs), utilizing Dynamic Context techniques for agent profiling, and Theory of Mind to enhance collaboration. The CogniSim Ecosystem analyzes problems, proposes solutions, constructs and validates plans, and coordinates specialized agents playing roles such as developers, executors, quality checkers, and methodology reviewers. These agents produce documentation, models, and diagrams (e.g., UML) while adhering to predefined quality and performance measures. The ecosystem also simulates the impact of various team configurations on problem-solving effectiveness, helping organizations identify optimal team structures. Case studies and simulations demonstrate its practical applications.

Warsaw University of Technology (PL)
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
Multi-Agent Systems and Negotiation
4.52
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