Software Development Using a Multi-agent Approach
Resumo
Software development involves complexities in requirements analysis, code generation, and continuous validation. This work presents a multi-agent system (MAS) designed to enhance the software engineering process by integrating Large Language Model (LLM) capabilities with human oversight. The MAS comprises five specialized agents that communicate through a shared message broker and maintain context in a persistent knowledge base. These agents autonomously handle tasks such as architectural planning, artifact generation, code review, and operational management, while human experts intervene to ensure accuracy and strategic alignment. The findings indicate that LLMs can impact software engineering, offering tools and techniques that automate tasks, enhance code generation, and potentially improve the development process.
Palavras-chave:
Gemini AI Chatbot, LLM, multi-agent system, software engineer
Referências
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Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Zhang, C., Wang, J., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., Xiao, L., Wu, C., and Schmidhuber, J. (2024). MetaGPT: Meta programming for a multi-agent collaborative framework. arXiv:2308.00352, [link].
Islam, M. A., Ali, M. E., and Parvez, M. R. (2024). MapCoder: Multi-agent code generation for competitive problem solving. arXiv:2405.11403, DOI: 10.48550/arXiv.2405.11403.
Jin, H., Huang, L., Cai, H., Yan, J., Li, B., and Chen, H. (2024). From LLMs to LLM-based agents for software engineering: A survey of current, challenges and future. arXiv:2408.02479, [link].
Jorgensen, S., Nadizar, G., Pietropolli, G., Manzoni, L., Medvet, E., O’Reilly, U.-M., and Hemberg, E. (2024). Large language model-based test case generation for gp agents. In Genetic and Evolutionary Computation Conference (GECCO’24), page 914–923, New York, NY, USA. ACM.
Lin, F., Kim, D. J., et al. (2024). When LLM-based code generation meets the software development process. arXiv:2403.15852, [link].
Liu, J., Wang, K., Chen, Y., Peng, X., Chen, Z., Zhang, L., and Lou, Y. (2024). Large language model-based agents for software engineering: A survey. arXiv:2409.02977, [link].
Nguyen, M. H., Chau, T. P., Nguyen, P. X., and Bui, N. D. Q. (2024). AgileCoder: Dynamic collaborative agents for software development based on agile methodology. arXiv:2406.11912, DOI: 10.48550/arXiv.2406.11912.
Pimentel, J. and Castro, J. (2018). piStar Tool – a pluggable online tool for goal modeling. In IEEE 26th Int. Requirements Engineering Conference (RE), pages 498–499.
Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X., Xu, J., Li, D., Liu, Z., and Sun, M. (2024a). ChatDev: Communicative agents for software development. In the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15174–15186, Bangkok, Thailand.
Qian, C., Xie, Z., Wang, Y., Liu, W., Zhu, K., Xia, H., Dang, Y., Du, Z., Chen, W., Yang, C., Liu, Z., and Sun, M. (2024b). Scaling large language model-based multi-agent collaboration. arXiv:2406.07155, DOI: 10.48550/arXiv.2406.07155. Accepted to ICLR 2025.
Rao, H., Zhao, Y., Hou, X., Wang, S., and Wang, H. (2025). Software engineering for large language models: Research status, challenges and the road ahead. arXiv:2506.23762v1, [link].
Sami, M. A., Waseem, M., Rasheed, Z., Saari, M., Systä, K., and Abrahamsson, P. (2024). Experimenting with multi-agent software development: Towards a unified platform. arXiv:2406.05381, DOI: 10.48550/arXiv.2406.05381.
Wang, Z., Wang, W., Li, Z., Wang, L., Yi, C., Xu, X., Cao, L., Su, H., Chen, S., and Zhou, J. (2024). Xuat-copilot: Multi-agent collaborative system for automated user acceptance testing with large language model. arXiv:2401.02705, DOI: 10.48550/arXiv.2401.02705.
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., Awadallah, A. H., White, R. W., Burger, D., and Wang, C. (2023). Autogen: Enabling next-gen llm applications via multi-agent conversation. arXiv:2308.08155, DOI: 10.48550/arXiv.2308.08155.
Yang, J., Jimenez, C. E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K., and Press, O. (2024). SWE-agent: Agent-computer interfaces enable automated software engineering. arXiv:2405.15793, DOI: 10.48550/arXiv.2405.15793.
Cinkusz, K., Chudziak, J. A., and Niewiadomska-Szynkiewicz, E. (2025). Cognitive agents powered by large language models for agile software project management. Electronics, 14(1).
Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Zhang, C., Wang, J., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., Xiao, L., Wu, C., and Schmidhuber, J. (2024). MetaGPT: Meta programming for a multi-agent collaborative framework. arXiv:2308.00352, [link].
Islam, M. A., Ali, M. E., and Parvez, M. R. (2024). MapCoder: Multi-agent code generation for competitive problem solving. arXiv:2405.11403, DOI: 10.48550/arXiv.2405.11403.
Jin, H., Huang, L., Cai, H., Yan, J., Li, B., and Chen, H. (2024). From LLMs to LLM-based agents for software engineering: A survey of current, challenges and future. arXiv:2408.02479, [link].
Jorgensen, S., Nadizar, G., Pietropolli, G., Manzoni, L., Medvet, E., O’Reilly, U.-M., and Hemberg, E. (2024). Large language model-based test case generation for gp agents. In Genetic and Evolutionary Computation Conference (GECCO’24), page 914–923, New York, NY, USA. ACM.
Lin, F., Kim, D. J., et al. (2024). When LLM-based code generation meets the software development process. arXiv:2403.15852, [link].
Liu, J., Wang, K., Chen, Y., Peng, X., Chen, Z., Zhang, L., and Lou, Y. (2024). Large language model-based agents for software engineering: A survey. arXiv:2409.02977, [link].
Nguyen, M. H., Chau, T. P., Nguyen, P. X., and Bui, N. D. Q. (2024). AgileCoder: Dynamic collaborative agents for software development based on agile methodology. arXiv:2406.11912, DOI: 10.48550/arXiv.2406.11912.
Pimentel, J. and Castro, J. (2018). piStar Tool – a pluggable online tool for goal modeling. In IEEE 26th Int. Requirements Engineering Conference (RE), pages 498–499.
Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X., Xu, J., Li, D., Liu, Z., and Sun, M. (2024a). ChatDev: Communicative agents for software development. In the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15174–15186, Bangkok, Thailand.
Qian, C., Xie, Z., Wang, Y., Liu, W., Zhu, K., Xia, H., Dang, Y., Du, Z., Chen, W., Yang, C., Liu, Z., and Sun, M. (2024b). Scaling large language model-based multi-agent collaboration. arXiv:2406.07155, DOI: 10.48550/arXiv.2406.07155. Accepted to ICLR 2025.
Rao, H., Zhao, Y., Hou, X., Wang, S., and Wang, H. (2025). Software engineering for large language models: Research status, challenges and the road ahead. arXiv:2506.23762v1, [link].
Sami, M. A., Waseem, M., Rasheed, Z., Saari, M., Systä, K., and Abrahamsson, P. (2024). Experimenting with multi-agent software development: Towards a unified platform. arXiv:2406.05381, DOI: 10.48550/arXiv.2406.05381.
Wang, Z., Wang, W., Li, Z., Wang, L., Yi, C., Xu, X., Cao, L., Su, H., Chen, S., and Zhou, J. (2024). Xuat-copilot: Multi-agent collaborative system for automated user acceptance testing with large language model. arXiv:2401.02705, DOI: 10.48550/arXiv.2401.02705.
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., Awadallah, A. H., White, R. W., Burger, D., and Wang, C. (2023). Autogen: Enabling next-gen llm applications via multi-agent conversation. arXiv:2308.08155, DOI: 10.48550/arXiv.2308.08155.
Yang, J., Jimenez, C. E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K., and Press, O. (2024). SWE-agent: Agent-computer interfaces enable automated software engineering. arXiv:2405.15793, DOI: 10.48550/arXiv.2405.15793.
Publicado
29/09/2025
Como Citar
RIBEIRO, Juliana Gomes; PIRES, Joel Machado; PEREIRA, Jonas Oliveira; DURÃO, Frederico Araujo; RALHA, Célia Ghedini.
Software Development Using a Multi-agent Approach. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 19. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 93-100.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2025.37549.
