The Use of Generative AI Tools by Requirements Engineers: An Interview with Industry Professionals
Resumo
This paper investigates how Generative Artificial Intelligence (GenAI) grounded in Large Language Models (LLMs) are being employed to support activities within Requirements Engineering (RE). Based on interviews conducted with software engineering professionals, the findings indicate that GenAI tools are particularly valuable during requirements elicitation and specification, contributing to improved productivity and reduced operational effort, especially when addressing Functional Requirements (FRs). Despite these benefits, participants also reported important limitations, including sensitivity to prompt formulation, generation of overly generic outputs, and limited capability to adequately support Non-Functional Requirements (NFRs).Referências
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Hasso, H., Fischer-Starcke, B., e Geppert, H. (2024). Quest-re question generation and exploration strategy for requirements engineering. In 2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW), pages 1–9. IEEE.
Luitel, D., Hassani, S., e Sabetzadeh, M. (2023). Using language models for enhancing the completeness of natural-language requirements. In International Workshop on Requirements Engineering Foundation for Software Quality (REFSQ), pages 87–104.
Mahbub, T., Dghaym, D., Shankarnarayanan, A., Syed, T., Shapsough, S., e Zualkernan, I. (2024). Can gpt-4 aid in detecting ambiguities, inconsistencies, and incompleteness in requirements analysis? a comprehensive case study. IEEE Access, 12:171972–171992.
Malkawi (2013). The art of software systems development: Reliability, availability, maintainability, performance (ramp). Human-Centric Computing and Information Sciences, 3:1–17.
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Oliveira, A., Correia, J., Assunção, W. K. G., Pereira, J. A., de Mello, R., Coutinho, D., Barbosa, C., Libório, P., e Garcia, A. (2024). Understanding developers’ discussions and perceptions on non-functional requirements: The case of the spring ecosystem. Proceedings of the ACM on Software Engineering, 1(FSE):1–22.
Parra, E., Dimou, C., Llorens, J., Moreno, V., e Fraga, A. (2015). A methodology for the classification of quality of requirements using machine learning techniques. Information and Software Technology, 67:180–195.
Pressman, R. S. e Maxim, B. R. (2021). Software Engineering: A Practitioner’s Approach. McGraw Hill Brasil, 9th edition.
Ramos, F. B. A., Pedro, A., Cesar, M., Costa, A. A. M., Perkusich, M. B., de Almeida, H. O., e Perkusich, A. (2019). Evaluating software developers’ acceptance of a tool for supporting agile non-functional requirement elicitation. In Proceedings of the International Conference on Software Engineering and Knowledge Engineering (SEKE), pages 26–42. KSI Research.
Rego, A., Pina, M., e Jr., V. M. (2018). Quantos participantes são necessários para um estudo qualitativo? linhas práticas de orientação. Revista de Gestão dos Países de Língua Portuguesa, 17(2):43–57.
Ren, S., Nakagawa, H., e Tsuchiya, T. (2024). Combining prompts with examples to enhance llm-based requirement elicitation. In 48th Annual Computers, Software, and Applications Conference (COMPSAC), pages 1376–1381. IEEE.
Reynolds, L. e McDonell, K. (2021). Prompt programming for large language models: Beyond the few-shot paradigm. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (CHI EA ’21). Association for Computing Machinery.
Ronanki, K., Berger, C., e Horkoff, J. (2023). Investigating chatgpt’s potential to assist in requirements elicitation processes. In 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 354–361. IEEE.
Sommerville, I. (2018). Software Engineering. Pearson, 10th edition.
Vijayakumar e Nethravathi, P. S. (2021). Use of natural language processing in software requirements prioritization – a systematic literature review. International Journal of Applied Engineering and Management Letters (IJAEML), 5(2):152–174.
Yu, X., Liu, L., Hu, X., Keung, J., Xia, X., e Lo, D. (2024). Practitioners’ expectations on automated test generation. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2024), pages 1618–1630.
Zhao, L., Alhoshan, W., Ferrari, A., Letsholo, K. J., Ajagbe, M. A., Chioasca, E.-V., e Batista-Navarro, R. T. (2021). Natural language processing for requirements engineering: A systematic mapping study. ACM Computing Surveys (CSUR), 54(3):1–41.
Felfernig, A., Stettinger, M., Atas, M., Samer, R., Nerlich, J., Scholz, S., e Raatikainen, M. (2018). Towards utility-based prioritization of requirements in open source environments. In 2018 IEEE 26th International Requirements Engineering Conference (RE), pages 406–411. IEEE.
Hasso, H., Fischer-Starcke, B., e Geppert, H. (2024). Quest-re question generation and exploration strategy for requirements engineering. In 2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW), pages 1–9. IEEE.
Luitel, D., Hassani, S., e Sabetzadeh, M. (2023). Using language models for enhancing the completeness of natural-language requirements. In International Workshop on Requirements Engineering Foundation for Software Quality (REFSQ), pages 87–104.
Mahbub, T., Dghaym, D., Shankarnarayanan, A., Syed, T., Shapsough, S., e Zualkernan, I. (2024). Can gpt-4 aid in detecting ambiguities, inconsistencies, and incompleteness in requirements analysis? a comprehensive case study. IEEE Access, 12:171972–171992.
Malkawi (2013). The art of software systems development: Reliability, availability, maintainability, performance (ramp). Human-Centric Computing and Information Sciences, 3:1–17.
Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heintz, I., e Roth, D. (2023). Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys (CSUR), 56(2).
Oliveira, A., Correia, J., Assunção, W. K. G., Pereira, J. A., de Mello, R., Coutinho, D., Barbosa, C., Libório, P., e Garcia, A. (2024). Understanding developers’ discussions and perceptions on non-functional requirements: The case of the spring ecosystem. Proceedings of the ACM on Software Engineering, 1(FSE):1–22.
Parra, E., Dimou, C., Llorens, J., Moreno, V., e Fraga, A. (2015). A methodology for the classification of quality of requirements using machine learning techniques. Information and Software Technology, 67:180–195.
Pressman, R. S. e Maxim, B. R. (2021). Software Engineering: A Practitioner’s Approach. McGraw Hill Brasil, 9th edition.
Ramos, F. B. A., Pedro, A., Cesar, M., Costa, A. A. M., Perkusich, M. B., de Almeida, H. O., e Perkusich, A. (2019). Evaluating software developers’ acceptance of a tool for supporting agile non-functional requirement elicitation. In Proceedings of the International Conference on Software Engineering and Knowledge Engineering (SEKE), pages 26–42. KSI Research.
Rego, A., Pina, M., e Jr., V. M. (2018). Quantos participantes são necessários para um estudo qualitativo? linhas práticas de orientação. Revista de Gestão dos Países de Língua Portuguesa, 17(2):43–57.
Ren, S., Nakagawa, H., e Tsuchiya, T. (2024). Combining prompts with examples to enhance llm-based requirement elicitation. In 48th Annual Computers, Software, and Applications Conference (COMPSAC), pages 1376–1381. IEEE.
Reynolds, L. e McDonell, K. (2021). Prompt programming for large language models: Beyond the few-shot paradigm. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (CHI EA ’21). Association for Computing Machinery.
Ronanki, K., Berger, C., e Horkoff, J. (2023). Investigating chatgpt’s potential to assist in requirements elicitation processes. In 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 354–361. IEEE.
Sommerville, I. (2018). Software Engineering. Pearson, 10th edition.
Vijayakumar e Nethravathi, P. S. (2021). Use of natural language processing in software requirements prioritization – a systematic literature review. International Journal of Applied Engineering and Management Letters (IJAEML), 5(2):152–174.
Yu, X., Liu, L., Hu, X., Keung, J., Xia, X., e Lo, D. (2024). Practitioners’ expectations on automated test generation. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2024), pages 1618–1630.
Zhao, L., Alhoshan, W., Ferrari, A., Letsholo, K. J., Ajagbe, M. A., Chioasca, E.-V., e Batista-Navarro, R. T. (2021). Natural language processing for requirements engineering: A systematic mapping study. ACM Computing Surveys (CSUR), 54(3):1–41.
Publicado
19/07/2026
Como Citar
FERREIRA, Josué V.; PORTELA, Carlos S.; OLIVEIRA, Sandro R. B..
The Use of Generative AI Tools by Requirements Engineers: An Interview with Industry Professionals. In: WORKSHOP SOBRE ASPECTOS SOCIAIS, HUMANOS E ECONÔMICOS DE SOFTWARE (WASHES), 11. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 152-163.
ISSN 2763-874X.
DOI: https://doi.org/10.5753/washes.2026.21003.
