DIP-AI: A Discovery Framework for AI Innovation Projects
Resumo
Despite the increasing development of Artificial Intelligence (AI) systems, Requirements Engineering (RE) activities face challenges in this new data-intensive paradigm.We identified a lack of support for problem discovery within AI innovation projects. To address this, we propose and evaluate DIP-AI, a discovery framework tailored to guide early-stage exploration in such initiatives. Based on a literature review, our solution proposal combines elements of ISO 12207, 5338, and Design Thinking to support the discovery of AI innovation projects, aiming at promoting higher quality deliveries and stakeholder satisfaction. We evaluated DIP-AI in an industry-academia collaboration (IAC) case study of an AI innovation project, in which participants applied DIP-AI to the discovery phase in practice and provided their perceptions about the approach’s problem discovery capability, acceptance, and suggestions. The results indicate that DIP-AI is relevant and useful, particularly in facilitating problem discovery in AI projects. This research contributes to academia by sharing DIP-AI as a framework for AI problem discovery. For industry, we discuss the use of this framework in a real IAC program that develops AI innovation projects.
Palavras-chave:
Requirements Engineering, Discovery, AI Systems, Innovation Projects
Referências
Khlood Ahmad, Mohamed Abdelrazek, Chetan Arora, Muneera Bano, and John Grundy. 2022. Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study. arXiv:2212.10693 [cs.SE]
Bakhtiyar Ahmed, Thomas Dannhauser, and Nada Philip. 2018. A Lean Design Thinking Methodology (LDTM) for Machine Learning and Modern Data Projects. In 2018 10th Computer Science and Electronic Engineering (CEEC). 11–14. DOI: 10.1109/CEEC.2018.8674234
Alhejab Alhazmi and Shihong Huang. 2020. Integrating Design Thinking into Scrum Framework in the Context of Requirements Engineering Management. In Proceedings of the 3rd International Conference on Computer Science and Software Engineering. 33–45. DOI: 10.1145/3403746.3403902
Silvio Alonso, Antonio Pedro Santos Alves, Lucas Romao, Hélio Lopes, and Marcos Kalinowski. 2025. Define-ML: An Approach to Ideate Machine Learning-Enabled Systems. In 2025 51st Euromicro Conference on Software Engineering and Advanced Applications (SEAA). Springer.
Antonio Pedro Santos Alves, Marcos Kalinowski, Görkem Giray, Daniel Mendez, Niklas Lavesson, Kelly Azevedo, Hugo Villamizar, Tatiana Escovedo, Helio Lopes, Stefan Biffl, Jürgen Musil, Michael Felderer, Stefan Wagner, Teresa Baldassarre, and Tony Gorschek. 2024. Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey. In Product-Focused Software Process Improvement, Regine Kadgien, Andreas Jedlitschka, Andrea Janes, Valentina Lenarduzzi, and Xiaozhou Li (Eds.). Springer Nature Switzerland, Cham, 159–174.
S. S. Anand, A. R. Patrick, J. G. Hughes, and D. A. Bell. 1998. A data mining methodology for cross-sales. Know.-Based Syst. 10, 7 (May 1998), 449–461. DOI: 10.1016/S0950-7051(98)00035-5
Atlas.TI 2024. [link].
V.R. Basili and H.D. Rombach. 1988. The TAME project: towards improvementoriented software environments. IEEE Transactions on Software Engineering 14, 6 (June 1988), 758–773. DOI: 10.1109/32.6156
Peter Cabena, Pablo Hadjinian, Rolf Stadler, Jaap Verhees, and Alessandro Zanasi. 1998. Discovering data mining: from concept to implementation. Prentice-Hall, Inc., USA.
J. Corbin and A. Strauss. 2014. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (4th ed.). SAGE Publications. [link]
CRISP-DM - version 2.0 2008. CRISP-DM - CRoss-Industry Standard Process for DM. (version 1.0 of 1999).
Kevin Dewalt. 2017. The AI Canvas - The strategic framework for enterprise deep learning. [link]
Louis Dorard. 2015. Machine Learning Canvas. [link]
Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. 1996. Knowledge discovery and data mining: towards a unifying framework. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (Portland, Oregon) (KDD’96). AAAI Press, 82–88.
Raphael Augusto Parreiras Gallegos. 2023. Ferramentas de Gestão Voltadas para Melhoria da Qualidade nas Empresas. Freitas Bastos Editora.
Görkem Giray. 2021. A software engineering perspective on engineering machine learning systems: State of the art and challenges. Journal of Systems and Software 180 (2021), 111031.
John W Goodell, Satish Kumar, Weng Marc Lim, and Debidutta Pattnaik. 2021. Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance 32 (2021), 100577.
Daniel Gustafsson. 2019. Analysing the Double diamond design process through research & implementation. [link]
Khan Mohammad Habibullah, Gregory Gay, and Jennifer Horkoff. 2023. Nonfunctional requirements for machine learning: understanding current use and challenges among practitioners. Requirements Engineering 28, 2 (Jan. 2023), 283–316. DOI: 10.1007/s00766-022-00395-3
Jennifer Hehn et al. 2020. On Integrating Design Thinking for Human-Centered Requirements Engineering. IEEE Software 37, 2 (2020), 25–31. DOI: 10.1109/MS.2019.2957715
Jennifer Hehn and Falk Uebernickel. 2018. The Use of Design Thinking for Requirements Engineering: An Ongoing Case Study in the Field of Innovative Software-Intensive Systems. In 2018 IEEE 26th International Requirements Engineering Conference (RE). 400–405. DOI: 10.1109/RE.2018.00-18
Jennifer Hehn, Falk Uebernickel, and Daniel Mendez Fernandez. 2018. DT4RE: Design Thinking for Requirements Engineering: A Tutorial on Human-Centered and Structured Requirements Elicitation. In 2018 IEEE 26th International Requirements Engineering Conference (RE). 504–505. DOI: 10.1109/RE.2018.00074
Hans-Martin Heyn, Eric Knauss, Amna Pir Muhammad, Olof Eriksson, Jennifer Linder, Padmini Subbiah, Shameer Kumar Pradhan, and Sagar Tungal. 2021. Requirement Engineering Challenges for AI-intense Systems Development. arXiv:2103.10270 [cs.LG]
Boyue Caroline Hu, Rick Salay, Krzysztof Czarnecki, Mona Rahimi, Gehan Selim, and Marsha Chechik. 2020. Towards requirements specification for machinelearned perception based on human performance. In 2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). IEEE, 48–51.
Fuyuki Ishikawa and Nobukazu Yoshioka. 2019. How Do Engineers Perceive Difficulties in Engineering of Machine-Learning Systems? - Questionnaire Survey. In 2019 IEEE/ACM Joint 7th International Workshop on Conducting Empirical Studies in Industry (CESI) and 6th International Workshop on Software Engineering Research and Industrial Practice (SERIP). 2–9. DOI: 10.1109/CESSERIP.2019.00009
ISO. 2017. ISO/IEC/IEEE International Standard - Systems and Software Engineering – Software Life Cycle Processes. , 157 pages. DOI: 10.1109/IEEESTD.2017.8100771
Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, and Yongjun Wang. 2017. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology 2, 4 (2017).
Danijel Kučak, Vedran Juričić, and Goran Ðambić. 2018. MACHINE LEARNING IN EDUCATION-A SURVEY OF CURRENT RESEARCH TRENDS. Annals of DAAAM & Proceedings 29 (2018).
Oscar Marban, Gonzalo Mariscal, and Javier Segovia. 2009. A Data Mining Knowledge Discovery Process Model. In Data Mining and Knowledge Discovery in Real Life Applications, Julio Ponce and Adem Karahoca (Eds.). IntechOpen, Rijeka, Chapter 1. DOI: 10.5772/6438
Mariana Martins, Taciana Kudo, and Renato Bulcão-Neto. 2024. A Qualitative Study on Requirements Engineering Practices in an Artificial Intelligence Unit of the Brazilian Industrial Research and Innovation Company. In Anais do XXVII Congresso Ibero-Americano em Engenharia de Software (Curitiba/PR). SBC, Porto Alegre, RS, Brasil, 46–60. [link]
Mariana Martins, Taciana Kudo, and Renato Bulcão-Neto. 2024. A Requirements Engineering Process for Machine Learning Innovation Projects. In Anais do XXVII Workshop de Engenharia de Requisitos (Buenos Aires/ARG). Even3, Brazil, Buenos Aires, Argentina. [link]
Mariana Crisostomo Martins, Lívia Mancine C. Campos, João Lucas R. Soares, Taciana Novo Kudo, and Renato F. Bulcão-Neto. 2025. Requirements Engineering for Machine Learning-Based AI Systems: A Tertiary Study. Journal of Software Engineering Research and Development 13, 2 (Sep. 2025), 13:129 – 13:142. DOI: 10.5753/jserd.2025.4892
Elizamary Nascimento, Anh Nguyen-Duc, Ingrid Sundbø, and Tayana Conte. 2020. Software engineering for artificial intelligence and machine learning software: A systematic literature review. arXiv:2011.03751 [cs.SE] [link]
Lauriane Pereira et al. 2021. Towards an understanding of benefits and challenges in the use of design thinking in requirements engineering. 1338–1345. DOI: 10.1145/3412841.3442008
Carlos E. Perez. 2017. The Deep Learning Ai Playbook: Strategy for Disruptive Artificial Intelligence. Intuition Machine.
S.L Pfleeger. [n. d.]. Understanding and improving technology transfer in software engineering. Journal of Systems and Software 47, 2 ( [n. d.]), 111–124. DOI: 10.1016/S0164-1212(99)00031-X
Lucas Romao, Marcos Kalinowski, Clarissa Barbosa, Allysson Allex Araújo, Simone D. J. Barbosa, and Helio Lopes. 2024. Agile Minds, Innovative Solutions, and Industry-Academia Collaboration: Lean RD Meets Problem-Based Learning in Software Engineering Education. arXiv:2407.15982 [cs.SE] [link]
Per Runeson and Martin Höst. 2008. Guidelines for conducting and reporting case study research in software engineering. Empirical Software Engineering 14, 2 (Dec. 2008), 131–164. DOI: 10.1007/s10664-008-9102-8
Viswanath Venkatesh and Hillol Bala. 2008. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences 39, 2 (May 2008), 273–315. DOI: 10.1111/j.1540-5915.2008.00192.x
Hugo Villamizar, Tatiana Escovedo, and Marcos Kalinowski. 2021. Requirements engineering for machine learning: A systematic mapping study. In 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 29–36.
Hugo Villamizar, Marcos Kalinowski, and Helio Lopes. 2022. Towards Perspective-Based Specification of Machine Learning-Enabled Systems. arXiv:2206.09760 [cs.SE]
Hugo Villamizar, Marcos Kalinowski, Hélio Lopes, and Daniel Mendez. 2024. Identifying concerns when specifying machine learning-enabled systems: A perspective-based approach. Journal of Systems and Software 213 (2024), 112053. DOI: 10.1016/j.jss.2024.112053
Hugo Villamizar, Marcos Kalinowski, Marx Viana, and Daniel Méndez Fernández. 2018. A Systematic Mapping Study on Security in Agile Requirements Engineering. In 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). 454–461. DOI: 10.1109/SEAA.2018.00080
Andreas Vogelsang and Markus Borg. 2019. Requirements Engineering for Machine Learning: Perspectives from Data Scientists. arXiv:1908.04674 [cs.LG]
Zhiyuan Wan, Xin Xia, David Lo, and Gail C. Murphy. 2020. How does Machine Learning Change Software Development Practices? IEEE Transactions on Software Engineering (2020), 1–1. DOI: 10.1109/tse.2019.2937083
Matei Zaharia, Andrew Chen, Aaron Davidson, Ali Ghodsi, Sue Ann Hong, Andy Konwinski, Siddharth Murching, Tomas Nykodym, Paul Ogilvie, Mani Parkhe, et al. 2018. Accelerating the machine learning lifecycle with MLflow. IEEE Data Eng. Bull. 41, 4 (2018), 39–45.
Bakhtiyar Ahmed, Thomas Dannhauser, and Nada Philip. 2018. A Lean Design Thinking Methodology (LDTM) for Machine Learning and Modern Data Projects. In 2018 10th Computer Science and Electronic Engineering (CEEC). 11–14. DOI: 10.1109/CEEC.2018.8674234
Alhejab Alhazmi and Shihong Huang. 2020. Integrating Design Thinking into Scrum Framework in the Context of Requirements Engineering Management. In Proceedings of the 3rd International Conference on Computer Science and Software Engineering. 33–45. DOI: 10.1145/3403746.3403902
Silvio Alonso, Antonio Pedro Santos Alves, Lucas Romao, Hélio Lopes, and Marcos Kalinowski. 2025. Define-ML: An Approach to Ideate Machine Learning-Enabled Systems. In 2025 51st Euromicro Conference on Software Engineering and Advanced Applications (SEAA). Springer.
Antonio Pedro Santos Alves, Marcos Kalinowski, Görkem Giray, Daniel Mendez, Niklas Lavesson, Kelly Azevedo, Hugo Villamizar, Tatiana Escovedo, Helio Lopes, Stefan Biffl, Jürgen Musil, Michael Felderer, Stefan Wagner, Teresa Baldassarre, and Tony Gorschek. 2024. Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey. In Product-Focused Software Process Improvement, Regine Kadgien, Andreas Jedlitschka, Andrea Janes, Valentina Lenarduzzi, and Xiaozhou Li (Eds.). Springer Nature Switzerland, Cham, 159–174.
S. S. Anand, A. R. Patrick, J. G. Hughes, and D. A. Bell. 1998. A data mining methodology for cross-sales. Know.-Based Syst. 10, 7 (May 1998), 449–461. DOI: 10.1016/S0950-7051(98)00035-5
Atlas.TI 2024. [link].
V.R. Basili and H.D. Rombach. 1988. The TAME project: towards improvementoriented software environments. IEEE Transactions on Software Engineering 14, 6 (June 1988), 758–773. DOI: 10.1109/32.6156
Peter Cabena, Pablo Hadjinian, Rolf Stadler, Jaap Verhees, and Alessandro Zanasi. 1998. Discovering data mining: from concept to implementation. Prentice-Hall, Inc., USA.
J. Corbin and A. Strauss. 2014. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (4th ed.). SAGE Publications. [link]
CRISP-DM - version 2.0 2008. CRISP-DM - CRoss-Industry Standard Process for DM. (version 1.0 of 1999).
Kevin Dewalt. 2017. The AI Canvas - The strategic framework for enterprise deep learning. [link]
Louis Dorard. 2015. Machine Learning Canvas. [link]
Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. 1996. Knowledge discovery and data mining: towards a unifying framework. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (Portland, Oregon) (KDD’96). AAAI Press, 82–88.
Raphael Augusto Parreiras Gallegos. 2023. Ferramentas de Gestão Voltadas para Melhoria da Qualidade nas Empresas. Freitas Bastos Editora.
Görkem Giray. 2021. A software engineering perspective on engineering machine learning systems: State of the art and challenges. Journal of Systems and Software 180 (2021), 111031.
John W Goodell, Satish Kumar, Weng Marc Lim, and Debidutta Pattnaik. 2021. Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance 32 (2021), 100577.
Daniel Gustafsson. 2019. Analysing the Double diamond design process through research & implementation. [link]
Khan Mohammad Habibullah, Gregory Gay, and Jennifer Horkoff. 2023. Nonfunctional requirements for machine learning: understanding current use and challenges among practitioners. Requirements Engineering 28, 2 (Jan. 2023), 283–316. DOI: 10.1007/s00766-022-00395-3
Jennifer Hehn et al. 2020. On Integrating Design Thinking for Human-Centered Requirements Engineering. IEEE Software 37, 2 (2020), 25–31. DOI: 10.1109/MS.2019.2957715
Jennifer Hehn and Falk Uebernickel. 2018. The Use of Design Thinking for Requirements Engineering: An Ongoing Case Study in the Field of Innovative Software-Intensive Systems. In 2018 IEEE 26th International Requirements Engineering Conference (RE). 400–405. DOI: 10.1109/RE.2018.00-18
Jennifer Hehn, Falk Uebernickel, and Daniel Mendez Fernandez. 2018. DT4RE: Design Thinking for Requirements Engineering: A Tutorial on Human-Centered and Structured Requirements Elicitation. In 2018 IEEE 26th International Requirements Engineering Conference (RE). 504–505. DOI: 10.1109/RE.2018.00074
Hans-Martin Heyn, Eric Knauss, Amna Pir Muhammad, Olof Eriksson, Jennifer Linder, Padmini Subbiah, Shameer Kumar Pradhan, and Sagar Tungal. 2021. Requirement Engineering Challenges for AI-intense Systems Development. arXiv:2103.10270 [cs.LG]
Boyue Caroline Hu, Rick Salay, Krzysztof Czarnecki, Mona Rahimi, Gehan Selim, and Marsha Chechik. 2020. Towards requirements specification for machinelearned perception based on human performance. In 2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). IEEE, 48–51.
Fuyuki Ishikawa and Nobukazu Yoshioka. 2019. How Do Engineers Perceive Difficulties in Engineering of Machine-Learning Systems? - Questionnaire Survey. In 2019 IEEE/ACM Joint 7th International Workshop on Conducting Empirical Studies in Industry (CESI) and 6th International Workshop on Software Engineering Research and Industrial Practice (SERIP). 2–9. DOI: 10.1109/CESSERIP.2019.00009
ISO. 2017. ISO/IEC/IEEE International Standard - Systems and Software Engineering – Software Life Cycle Processes. , 157 pages. DOI: 10.1109/IEEESTD.2017.8100771
Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, and Yongjun Wang. 2017. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology 2, 4 (2017).
Danijel Kučak, Vedran Juričić, and Goran Ðambić. 2018. MACHINE LEARNING IN EDUCATION-A SURVEY OF CURRENT RESEARCH TRENDS. Annals of DAAAM & Proceedings 29 (2018).
Oscar Marban, Gonzalo Mariscal, and Javier Segovia. 2009. A Data Mining Knowledge Discovery Process Model. In Data Mining and Knowledge Discovery in Real Life Applications, Julio Ponce and Adem Karahoca (Eds.). IntechOpen, Rijeka, Chapter 1. DOI: 10.5772/6438
Mariana Martins, Taciana Kudo, and Renato Bulcão-Neto. 2024. A Qualitative Study on Requirements Engineering Practices in an Artificial Intelligence Unit of the Brazilian Industrial Research and Innovation Company. In Anais do XXVII Congresso Ibero-Americano em Engenharia de Software (Curitiba/PR). SBC, Porto Alegre, RS, Brasil, 46–60. [link]
Mariana Martins, Taciana Kudo, and Renato Bulcão-Neto. 2024. A Requirements Engineering Process for Machine Learning Innovation Projects. In Anais do XXVII Workshop de Engenharia de Requisitos (Buenos Aires/ARG). Even3, Brazil, Buenos Aires, Argentina. [link]
Mariana Crisostomo Martins, Lívia Mancine C. Campos, João Lucas R. Soares, Taciana Novo Kudo, and Renato F. Bulcão-Neto. 2025. Requirements Engineering for Machine Learning-Based AI Systems: A Tertiary Study. Journal of Software Engineering Research and Development 13, 2 (Sep. 2025), 13:129 – 13:142. DOI: 10.5753/jserd.2025.4892
Elizamary Nascimento, Anh Nguyen-Duc, Ingrid Sundbø, and Tayana Conte. 2020. Software engineering for artificial intelligence and machine learning software: A systematic literature review. arXiv:2011.03751 [cs.SE] [link]
Lauriane Pereira et al. 2021. Towards an understanding of benefits and challenges in the use of design thinking in requirements engineering. 1338–1345. DOI: 10.1145/3412841.3442008
Carlos E. Perez. 2017. The Deep Learning Ai Playbook: Strategy for Disruptive Artificial Intelligence. Intuition Machine.
S.L Pfleeger. [n. d.]. Understanding and improving technology transfer in software engineering. Journal of Systems and Software 47, 2 ( [n. d.]), 111–124. DOI: 10.1016/S0164-1212(99)00031-X
Lucas Romao, Marcos Kalinowski, Clarissa Barbosa, Allysson Allex Araújo, Simone D. J. Barbosa, and Helio Lopes. 2024. Agile Minds, Innovative Solutions, and Industry-Academia Collaboration: Lean RD Meets Problem-Based Learning in Software Engineering Education. arXiv:2407.15982 [cs.SE] [link]
Per Runeson and Martin Höst. 2008. Guidelines for conducting and reporting case study research in software engineering. Empirical Software Engineering 14, 2 (Dec. 2008), 131–164. DOI: 10.1007/s10664-008-9102-8
Viswanath Venkatesh and Hillol Bala. 2008. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences 39, 2 (May 2008), 273–315. DOI: 10.1111/j.1540-5915.2008.00192.x
Hugo Villamizar, Tatiana Escovedo, and Marcos Kalinowski. 2021. Requirements engineering for machine learning: A systematic mapping study. In 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 29–36.
Hugo Villamizar, Marcos Kalinowski, and Helio Lopes. 2022. Towards Perspective-Based Specification of Machine Learning-Enabled Systems. arXiv:2206.09760 [cs.SE]
Hugo Villamizar, Marcos Kalinowski, Hélio Lopes, and Daniel Mendez. 2024. Identifying concerns when specifying machine learning-enabled systems: A perspective-based approach. Journal of Systems and Software 213 (2024), 112053. DOI: 10.1016/j.jss.2024.112053
Hugo Villamizar, Marcos Kalinowski, Marx Viana, and Daniel Méndez Fernández. 2018. A Systematic Mapping Study on Security in Agile Requirements Engineering. In 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). 454–461. DOI: 10.1109/SEAA.2018.00080
Andreas Vogelsang and Markus Borg. 2019. Requirements Engineering for Machine Learning: Perspectives from Data Scientists. arXiv:1908.04674 [cs.LG]
Zhiyuan Wan, Xin Xia, David Lo, and Gail C. Murphy. 2020. How does Machine Learning Change Software Development Practices? IEEE Transactions on Software Engineering (2020), 1–1. DOI: 10.1109/tse.2019.2937083
Matei Zaharia, Andrew Chen, Aaron Davidson, Ali Ghodsi, Sue Ann Hong, Andy Konwinski, Siddharth Murching, Tomas Nykodym, Paul Ogilvie, Mani Parkhe, et al. 2018. Accelerating the machine learning lifecycle with MLflow. IEEE Data Eng. Bull. 41, 4 (2018), 39–45.
Publicado
04/11/2025
Como Citar
MARTINS, Mariana Crisostomo; ROCHA, Lucas Elias Cardoso; ROMÃO, Lucas Cordeiro; KUDO, Taciana Novo; KALINOWSKI, Marcos; BULCÃO-NETO, Renato de Freitas.
DIP-AI: A Discovery Framework for AI Innovation Projects. In: SIMPÓSIO BRASILEIRO DE QUALIDADE DE SOFTWARE (SBQS), 24. , 2025, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 131-141.
DOI: https://doi.org/10.5753/sbqs.2025.13834.
