Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report | Paper Citation Record

Observation c8c13e6f-db8e-47ef-981a-7539608900b1 · outbound

This paper cites https://github.com/Significant-Gravitas/Auto-GPT, gitHub repository, last accessed 11-02-2025.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/Significant-Gravitas/Auto-GPT, gitHub repository, last accessed 11-02-2025

Reference 1

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/run-llama/llama_index, gitHub repository, last accessed 11-02-2025

Reference 2

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This paper cites https://github.com/microsoft/autogen, gitHub repository, last accessed 11-02-2025.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/microsoft/autogen, gitHub repository, last accessed 11-02-2025

Reference 3

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This paper cites https://github.com/microsoft/semantic-kernel, gitHub repository, last accessed 11-02-2025.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/microsoft/semantic-kernel, gitHub repository, last accessed 11-02-2025

Reference 4

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This paper cites https://github.com/xlang-ai/OpenAgents, gitHub repository, last accessed 11-02-2025.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/xlang-ai/OpenAgents, gitHub repository, last accessed 11-02-2025

Reference 5

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Reference 12

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This paper cites https://github.com/camel-ai/camel, gitHub repository, last accessed 11-02-2025.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/camel-ai/camel, gitHub repository, last accessed 11-02-2025

Reference 17

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This paper cites https://github.com/langgenius/dify, gitHub repository, last accessed 11-02-2025.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/langgenius/dify, gitHub repository, last accessed 11-02-2025

Reference 18

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This paper cites https://github.com/FlowiseAI/Flowise, gitHub repository, last accessed 11-02-2025.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/FlowiseAI/Flowise, gitHub repository, last accessed 11-02-2025

Reference 19

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This paper cites https://github.com/kreneskyp/ix, gitHub repository, last accessed 11-02-2025.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/kreneskyp/ix, gitHub repository, last accessed 11-02-2025

Reference 20

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Houghton Mifflin Boston

Reference 21

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This paper cites In: Bertolino A, Pascoal Faria J, Lago P, Semini L (eds) Quality of Information and Communications Technology, Springer Nature Switzerland, Cham, pp 161--176.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report In: Bertolino A, Pascoal Faria J, Lago P, Semini L (eds) Quality of Information and Communications Technology, Springer Nature Switzerland, Cham, pp 161--176

Reference 22

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This paper cites Information and Software Technology 181:107678, doi:https://doi.org/10.1016/j.infsof.2025.107678, ://www.sciencedirect.com/science/article/pii/S0950584925000175.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Information and Software Technology 181:107678, doi:https://doi.org/10.1016/j.infsof.2025.107678, ://www.sciencedirect.com/science/article/pii/S0950584925000175

Reference 23

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report ://github.com/MDEGroup/LLMs-based-MAS-ReplicationPackage

Reference 24

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This paper cites https://github.com/deepset-ai/haystack, gitHub repository, last accessed 11-02-2025.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/deepset-ai/haystack, gitHub repository, last accessed 11-02-2025

Reference 25

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This paper cites https://github.com/agno-agi/agno, gitHub repository, last accessed 11-02-2025.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/agno-agi/agno, gitHub repository, last accessed 11-02-2025

Reference 29

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This paper cites In: The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024, OpenReview.net, ://openreview.net/forum?id=2Rwq6c3tvr.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report In: The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024, OpenReview.net, ://openreview.net/forum?id=2Rwq6c3tvr

Reference 30

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Lawrence Erlbaum Associates, ://books.google.it/books?id=4C49CGkNxLAC

Reference 31

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This paper cites In: Proceedings of the 40th International Conference on Machine Learning, JMLR.org, ICML'23.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report In: Proceedings of the 40th International Conference on Machine Learning, JMLR.org, ICML'23

Reference 40

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This paper cites IEEE Software 12(6):42--50, doi:10.1109/52.469759.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report IEEE Software 12(6):42--50, doi:10.1109/52.469759

Reference 43

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This paper cites CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

Reference 45

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Journal of Systems and Software 212:112002, doi:https://doi.org/10.1016/j.jss.2024.112002, ://www.sciencedirect.com/science/article/pii/S0164121224000451

Reference 47

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This paper cites In: Text Summarization Branches Out, Association for Computational Linguistics, Barcelona, Spain, pp 74--81, ://aclanthology.org/W04-1013/.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report In: Text Summarization Branches Out, Association for Computational Linguistics, Barcelona, Spain, pp 74--81, ://aclanthology.org/W04-1013/

Reference 48

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report doi:http://dx.doi.org/10.1016/j.redeen.2016.05.001

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report ://docs.softwareheritage.org/devel/swh-dataset/graph/dataset.html

Reference 57

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Reference 59

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This paper cites In: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, JMLR.org, ICML'15, p 2152–2161.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report In: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, JMLR.org, ICML'15, p 2152–2161

Reference 60

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Biometrika 52(3/4):591--611, ://www.jstor.org/stable/2333709

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://botpress.com, last accessed: Mar 19, 2025

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/crewAIInc/crewAI, gitHub repository, last accessed 11-02-2025

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/langchain-ai/langchain, gitHub repository, last accessed 11-02-2025

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/geekan/MetaGPT, gitHub repository, last accessed 11-02-2025

Reference 70

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report https://github.com/huggingface/smolagents, gitHub repository, last accessed 11-02-2025

Reference 71

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Reference 72

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Biometrics Bulletin 1(6):80--83, ://www.jstor.org/stable/3001968

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Science China Information Sciences 68(2):121101, doi:10.1007/s11432-024-4222-0, ://doi.org/10.1007/s11432-024-4222-0, read\_Status: New Read\_Status\_Date: 2025-05-12T08:57:00.797Z

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Chain-of-Thought in Neural Code Generation: From and For Lightweight Language Models

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report The Twelfth International Conference on Learning Representations,

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Select, Prompt, Filter: Distilling Large Language Models for Summarizing Conversations

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report C hat D ev: Communicative Agents for Software Development

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Many hands make light work: An LLM-based multi-agent system for detecting malicious PyPI packages , journal =

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report ACM Trans

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Guidelines for Empirical Studies in Software Engineering involving Large Language Models

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Privacy issues in Large Language Models: A survey , journal =

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Security and privacy in LLMs: A comprehensive survey of threats and mitigation strategies , journal =

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Nguyen , keywords =

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective , journal =

Reference 95

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Dataset —

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Understanding the

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Reference 114

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report and Sethi, Rohan and Lu, Yung-Hsiang and Thiruvathukal, George K

Reference 115

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Reference 116

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Reference 117

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Reference 118

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Reference 119

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report IEEE Transactions on Software Engineering , volume = 49, number = 4, pages =

Reference 120

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report A Survey on Machine Learning Techniques for Source Code Analysis

Reference 123

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Reference 124

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Reference 125

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report RouterBench: A Benchmark for Multi-LLM Routing System

Reference 126

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Reference 128

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report A survey on

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Proceedings of the 37th

Reference 131

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Information and Software Technology , volume = 106, pages =

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Reference 133

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Impromptu: a framework for model-driven prompt engineering , shorttitle =

Reference 134

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Observation 201ee65d-6f83-40d0-acd6-83bafc304b7b · outbound

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report ACM Trans

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report and Santos, Wylliams B

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report and Lo, David , year = 2024, month =

Reference 138

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Survey of

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology

Reference 140

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report and Baxter, Daniel P

Reference 141

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Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report Introducing

Reference 144

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