Towards Requirements Specification for Machine Learning-Based Software Systems
Resumo
Machine Learning-based software systems (ML-based systems) have been increasingly developed for various domains, such as medicine, smart cities, and the automotive sector. These systems have posed significant challenges for Requirements Engineering (RE), including for requirements specification, while ensuring critical qualities such as reliability. The main problem addressed in this work is the difficulty practitioners have faced in specifying the requirements for these systems. Additionally, effective approaches (e.g., methods, techniques, processes, or guidelines) for this still lack consensus. The main objective of this work is to present and discuss current trends in approaches to requirements specification for ML-based s ystems. We observe this research field is very new, with weak collaboration with industrial environments. Novel approaches or adaptations of existing ones were proposed, but several possibilities for future research still exist; hence, we also discuss some of the main ones.Referências
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Horkoff, J. (2019). Non-functional requirements for machine learning: Challenges and new directions. In 2019 IEEE 27th International Requirements Engineering Conference (RE), pages 386–391.
Hu, B. C., Salay, R., Czarnecki, K., Rahimi, M., Selim, G., and Chechik, M. (2020). Towards requirements specification for machine-learned perception based on human performance. In 2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), pages 48–51.
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Nalchigar, S., Yu, E., and Keshavjee, K. (2021). Modeling machine learning requirements from three perspectives: a case report from the healthcare domain. Requirements Engineering, 26:237–254.
Nascimento, E. d. S., Ahmed, I., Oliveira, E., Palheta, M. P., Steinmacher, I., and Conte, T. (2019). Understanding development process of machine learning systems: Challenges and solutions. In 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), pages 1–6.
Peng, Y., Heyn, H.-M., and Horkoff, J. (2026). From machine learning documentation to requirements: Bridging processes with requirements languages. Lecture Notes in Computer Science, 16361:119–136.
Rahimi, M., Guo, J. L., Kokaly, S., and Chechik, M. (2019). Toward requirements specification for machine-learned components. In 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW), pages 241–244.
Ries, B., Guelfi, N., and Jahić, B. (2021). An mde method for improving deep learning dataset requirements engineering using alloy and uml. page 41 – 52.
Saeeda, H., Rohacova, Z., Jakobsson, O., Heyn, H.-M., Knauss, E., Knauss, A., and Horkoff, J. (2025). Requirements representations in machine learning-based automotive perception systems development for multi-party collaboration. Lecture Notes in Computer Science, 15588:197–213.
Shao, W. and Wang, X. (2022). A data modeling method for machine learning systems. page 1 – 5.
Sothilingam, R. and Yu, E. (2023). Toward a goal-oriented argumentation approach for fair ml measures using i*. volume 3533, page 4 – 9.
Uysal, M. P. (2023). Requirements modeling: A use case approach to machine learning.
Villamizar, H., Escovedo, T., and Kalinowski, M. (2021). Requirements engineering for machine learning: A systematic mapping study. In 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 29–36. IEEE.
Villamizar, H., Kalinowski, M., Lopes, H., and Mendez, D. (2024). Identifying concerns when specifying machine learning-enabled systems: A perspective-based approach. Journal of Systems and Software, 213.
Villamizar, H., Mendez, D., and Kalinowski, M. (2025). Towards a framework for operationalizing the specification of trustworthy ai requirements. In IEEE 33rd International Requirements Engineering Conference Workshops (REW), page 533–537.
Vogelsang, A. and Borg, M. (2019). Requirements engineering for machine learning: Perspectives from data scientists. In 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW), pages 245–251.
Wieringa, R., Maiden, N., Mead, N., and Rolland, C. (2006). Requirements engineering paper classification and evaluation criteria: a proposal and a discussion. Requirements Engineering, 11:102–107.
Yang, Y., Zeng, B., and Gao, J. (2025). Rm4ml: requirements model for machine learning-enabled software systems. Requirements Engineering, 30(1):1 – 33.
Yu, E. (2021). Requirements engineering for actors-with-learning: Encompassing the two kinds of modeling for full cognitive cycle re. volume 2857.
Barrera, J. M., Reina-Reina, A., García-Ponsoda, S., and Trujillo, J. (2022). Use of a i*extension for machine learning: a real case study. volume 3231, page 14 – 20.
Barrera, J. M., Reina-Reina, A., Lavalle, A., Maté, A., and Trujillo, J. (2024). An extension of istar for machine learning requirements by following the prise methodology. Computer Standards and Interfaces, 88.
Chuprina, T., Mendez, D., and Wnuk, K. (2021). Towards artefact-based requirements engineering for data-centric systems. volume 2857.
Cunha, C., Oliveira, R., and Duarte, R. (2024). Agile-based requirements engineering for machine learning: A case study on personalized nutrition. International Journal of Intelligent Systems and Applications in Engineering, 12(2):319 – 328.
Dey, S. and Lee, S.-W. (2023). A multi-layered collaborative framework for evidence-driven data requirements engineering for machine learning-based safety-critical systems. page 1404 – 1413.
Dyba, T., Kitchenham, B. A., and Jorgensen, M. (2005). Evidence-based software engineering for practitioners. IEEE Software, 22(1):58–65.
Heyn, H.-M., Knauss, E., Malleswaran, I., and Dinakaran, S. (2024). An empirical investigation of challenges of specifying training data and runtime monitors for critical software with machine learning and their relation to architectural decisions. Requirements Engineering, 29(1):97 – 117.
Heyn, H.-M., Mao, Y., Weiß, R., and Knauss, E. (2026). Causal models for specifying requirements in industrial ml-based software: A case study. Journal of Systems and Software, 232.
Horkoff, J. (2019). Non-functional requirements for machine learning: Challenges and new directions. In 2019 IEEE 27th International Requirements Engineering Conference (RE), pages 386–391.
Hu, B. C., Salay, R., Czarnecki, K., Rahimi, M., Selim, G., and Chechik, M. (2020). Towards requirements specification for machine-learned perception based on human performance. In 2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), pages 48–51.
Kitchenham, B., Budgen, D., and Brereton, P. (2015). Evidence-based software engineering and systematic reviews, volume 4. CRC Press.
Nalchigar, S., Yu, E., and Keshavjee, K. (2021). Modeling machine learning requirements from three perspectives: a case report from the healthcare domain. Requirements Engineering, 26:237–254.
Nascimento, E. d. S., Ahmed, I., Oliveira, E., Palheta, M. P., Steinmacher, I., and Conte, T. (2019). Understanding development process of machine learning systems: Challenges and solutions. In 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), pages 1–6.
Peng, Y., Heyn, H.-M., and Horkoff, J. (2026). From machine learning documentation to requirements: Bridging processes with requirements languages. Lecture Notes in Computer Science, 16361:119–136.
Rahimi, M., Guo, J. L., Kokaly, S., and Chechik, M. (2019). Toward requirements specification for machine-learned components. In 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW), pages 241–244.
Ries, B., Guelfi, N., and Jahić, B. (2021). An mde method for improving deep learning dataset requirements engineering using alloy and uml. page 41 – 52.
Saeeda, H., Rohacova, Z., Jakobsson, O., Heyn, H.-M., Knauss, E., Knauss, A., and Horkoff, J. (2025). Requirements representations in machine learning-based automotive perception systems development for multi-party collaboration. Lecture Notes in Computer Science, 15588:197–213.
Shao, W. and Wang, X. (2022). A data modeling method for machine learning systems. page 1 – 5.
Sothilingam, R. and Yu, E. (2023). Toward a goal-oriented argumentation approach for fair ml measures using i*. volume 3533, page 4 – 9.
Uysal, M. P. (2023). Requirements modeling: A use case approach to machine learning.
Villamizar, H., Escovedo, T., and Kalinowski, M. (2021). Requirements engineering for machine learning: A systematic mapping study. In 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 29–36. IEEE.
Villamizar, H., Kalinowski, M., Lopes, H., and Mendez, D. (2024). Identifying concerns when specifying machine learning-enabled systems: A perspective-based approach. Journal of Systems and Software, 213.
Villamizar, H., Mendez, D., and Kalinowski, M. (2025). Towards a framework for operationalizing the specification of trustworthy ai requirements. In IEEE 33rd International Requirements Engineering Conference Workshops (REW), page 533–537.
Vogelsang, A. and Borg, M. (2019). Requirements engineering for machine learning: Perspectives from data scientists. In 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW), pages 245–251.
Wieringa, R., Maiden, N., Mead, N., and Rolland, C. (2006). Requirements engineering paper classification and evaluation criteria: a proposal and a discussion. Requirements Engineering, 11:102–107.
Yang, Y., Zeng, B., and Gao, J. (2025). Rm4ml: requirements model for machine learning-enabled software systems. Requirements Engineering, 30(1):1 – 33.
Yu, E. (2021). Requirements engineering for actors-with-learning: Encompassing the two kinds of modeling for full cognitive cycle re. volume 2857.
Publicado
11/05/2026
Como Citar
BREDA, Bruno Garcia de Oliveira; VALLE, Pedro Henrique
Dias; TAMBURRI, Damian Andrew; NAKAGAWA, Elisa Yumi.
Towards Requirements Specification for Machine Learning-Based Software Systems. In: CONGRESSO IBERO-AMERICANO EM ENGENHARIA DE SOFTWARE (CIBSE), 29. , 2026, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 151-165.
