Uso do ChatGPT na Priorização de Requisitos: Uma Experiência Educacional em Engenharia de Software
Resumo
O ensino de Engenharia de Software (ES) tem sido transformado pela Inteligência Artificial Generativa (IA-Generativa), ampliando possibilidades no processo ensino-aprendizagem de computação. Na ES, as IAs apoiam várias tarefas de desenvolvimento de software. Este trabalho relata uma experiência com 16 alunos utilizando o ChatGPT para priorizar requisitos de software, visando o MVP (Produto Mínimo Viável). Primeiramente, os alunos elicitaram e priorizaram os requisitos de software implementados no MVP. Em seguida, usaram o ChatGPT para revisar a priorização e propor novos requisitos. O ChatGPT corroborou 86,02% das escolhas dos alunos. Apesar de reconhecerem agilidade proporcionada, os alunos relataram falta de confiança nos resultados da IA.
Referências
Alonso, S., Kalinowski, M., Ferreira, B., Barbosa, S. D., and Lopes, H. (2023). A systematic mapping study and practitioner insights on the use of software engineering practices to develop mvps. Information and Software Technology, 156:107144.
Arora, C., Grundy, J., and Abdelrazek, M. (2024). Advancing requirements engineering through generative ai: Assessing the role of llms. In Generative AI for Effective Software Development, pages 129–148. Springer.
Beganovic, A., Jaber, M. A., and Abd Almisreb, A. (2023). Methods and applications of chatgpt in software development: a literature review. Southeast Europe Journal of Soft Computing, 12(1):08–12.
Daun, M. and Brings, J. (2023). How chatgpt will change software engineering education. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1, pages 110–116.
Marques, N., Silva, R. R., and Bernardino, J. (2024). Using chatgpt in software requirements engineering: A comprehensive review. Future Internet, 16(6):180.
Melegati, J., Chanin, R., Sales, A., Prikladnicki, R., and Wang, X. (2020). Mvp and experimentation in software startups: a qualitative survey. In 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 322–325. IEEE.
Mendonça, G. D., de Souza Filho, I. P., and Guedes, G. T. A. (2021). A systematic review about requirements engineering processes for multi-agent systems. ICAART (1), pages 69–79.
Mittal, U., Sai, S., Chamola, V., et al. (2024). A comprehensive review on generative ai for education. IEEE Access.
Petrovska, O., Clift, L., Moller, F., and Pearsall, R. (2024). Incorporating generative ai into software development education. In Proceedings of the 8th Conference on Computing Education Practice, pages 37–40.
Ries, E. (2011). The lean startup: How today’s entrepreneurs use continuous innovation to create radically successful businesses. Crown Currency.
Sampaio, S., Lima, M., Rodrigues, E., Meireles, M. A., Pessoa, M., and Conte, T. (2024a). Exploring the use of large language models in requirements engineering education: An experience report with chatgpt 3.5. SBQS 2024.
Sampaio, S., Lima, M., Rodrigues, E., Meireles, M. A., Pessoa, M., and Conte, T. (2024b). Exploring the use of large language models in requirements engineering education: An experience report with chatgpt 3.5.
Sun, J., Liao, Q. V., Muller, M., Agarwal, M., Houde, S., Talamadupula, K., and Weisz, J. D. (2022). Investigating explainability of generative ai for code through scenario-based design. In Proceedings of the 27th International Conference on Intelligent User Interfaces, pages 212–228.
White, J., Hays, S., Fu, Q., Spencer-Smith, J., and Schmidt, D. C. (2024). Chatgpt prompt patterns for improving code quality, refactoring, requirements elicitation, and software design. In Generative AI for Effective Software Development, pages 71–108. Springer.
Arora, C., Grundy, J., and Abdelrazek, M. (2024). Advancing requirements engineering through generative ai: Assessing the role of llms. In Generative AI for Effective Software Development, pages 129–148. Springer.
Beganovic, A., Jaber, M. A., and Abd Almisreb, A. (2023). Methods and applications of chatgpt in software development: a literature review. Southeast Europe Journal of Soft Computing, 12(1):08–12.
Daun, M. and Brings, J. (2023). How chatgpt will change software engineering education. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1, pages 110–116.
Marques, N., Silva, R. R., and Bernardino, J. (2024). Using chatgpt in software requirements engineering: A comprehensive review. Future Internet, 16(6):180.
Melegati, J., Chanin, R., Sales, A., Prikladnicki, R., and Wang, X. (2020). Mvp and experimentation in software startups: a qualitative survey. In 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 322–325. IEEE.
Mendonça, G. D., de Souza Filho, I. P., and Guedes, G. T. A. (2021). A systematic review about requirements engineering processes for multi-agent systems. ICAART (1), pages 69–79.
Mittal, U., Sai, S., Chamola, V., et al. (2024). A comprehensive review on generative ai for education. IEEE Access.
Petrovska, O., Clift, L., Moller, F., and Pearsall, R. (2024). Incorporating generative ai into software development education. In Proceedings of the 8th Conference on Computing Education Practice, pages 37–40.
Ries, E. (2011). The lean startup: How today’s entrepreneurs use continuous innovation to create radically successful businesses. Crown Currency.
Sampaio, S., Lima, M., Rodrigues, E., Meireles, M. A., Pessoa, M., and Conte, T. (2024a). Exploring the use of large language models in requirements engineering education: An experience report with chatgpt 3.5. SBQS 2024.
Sampaio, S., Lima, M., Rodrigues, E., Meireles, M. A., Pessoa, M., and Conte, T. (2024b). Exploring the use of large language models in requirements engineering education: An experience report with chatgpt 3.5.
Sun, J., Liao, Q. V., Muller, M., Agarwal, M., Houde, S., Talamadupula, K., and Weisz, J. D. (2022). Investigating explainability of generative ai for code through scenario-based design. In Proceedings of the 27th International Conference on Intelligent User Interfaces, pages 212–228.
White, J., Hays, S., Fu, Q., Spencer-Smith, J., and Schmidt, D. C. (2024). Chatgpt prompt patterns for improving code quality, refactoring, requirements elicitation, and software design. In Generative AI for Effective Software Development, pages 71–108. Springer.
Publicado
07/04/2025
Como Citar
QUEIROZ, Francisca Karolina; LIMA, Márcia Sampaio.
Uso do ChatGPT na Priorização de Requisitos: Uma Experiência Educacional em Engenharia de Software. In: SIMPÓSIO BRASILEIRO DE EDUCAÇÃO EM COMPUTAÇÃO (EDUCOMP), 5. , 2025, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 491-501.
DOI: https://doi.org/10.5753/educomp.2025.5370.
