Intelligent Platform for Industry-Academia Collaboration in Research Project and Researcher Prospecting
Resumo
One of the main factors influencing the adoption of new technologies in industry and government is the collaboration between Industry, Academia, and Government. Through Research and Development, it is possible to drive digital transformations and create new applied technologies. This paper proposes conducting Systematic Literature Reviews (SLR) and developing an intelligent platform that uses Natural Language Processing techniques and Large Language Models to enhance these collaborations. So far, we have completed two comprehensive SLR, developed two versions of a project prototype, and the goal is to enable information sharing and interconnection with institutions and researchers.Referências
Barbosa, A., Galindo, G., Lencastre, M., Fagundes, R., and Santos, W. (2020). Fostering industry-academia collaboration in software engineering using action research: A case study. In Anais do XIX Simpósio Brasileiro de Qualidade de Software, pages 411–419, Porto Alegre, RS, Brasil. SBC.
Carver, J. and Prikladnicki, R. (2018). Industry–academia collaboration in software engineering. IEEE Software, 35:120–124.
Dallegrave, T. and Santos, W. B. (2023). Action research for industry academia collaboration : A replication study. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6.
Garousi, V., Petersen, K., and Ozkan, B. (2016). Challenges and best practices in industry-academia collaborations in software engineering: A systematic literature review. Information and Software Technology, 79:106–127.
Garousi, V., Pfahl, D., Fernandes, J., Felderer, M., Mäntylä, M., Shepherd, D., Arcuri, A., Coşkunçay, A., and Tekinerdogan, B. (2019). Characterizing industry-academia collaborations in software engineering: evidence from 101 projects. Empirical Software Engineering, 24.
Kitchenham, B. A., Budgen, D., and Brereton, P. (2015). Evidence-Based Software Engineering and Systematic Reviews. Chapman & Hall/CRC.
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., and McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. In Bontcheva, K. and Zhu, J., editors, Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 55–60, Baltimore, Maryland. Association for Computational Linguistics.
Marijan, D. and Gotlieb, A. (2021). Industry-academia research collaboration in software engineering: The certus model. Information and Software Technology, 132:106473.
Marques, D., Rocha, R., Santos, B., Pacheco, F., Rodrigues, C., and Santos, W. (2024). Industry academia and government collaboration to reduce gaps in software engineering: Applications for students and professionals in career transition. In Anais do XXIII Simpósio Brasileiro de Qualidade de Software, page 657–664, Porto Alegre, RS, Brasil. SBC.
Marques, D. d. G., Dallegrave, T. L. D. d. A., Barbosa, L. E. L., Rodrigues, C. M. d. O., and Santos, W. B. (2022). Industry-academy collaboration in agile methodology: a systematic literature review. In 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6.
Marques, D. d. G., Dallegrave, T. L. D. d. A., Rodrigues, C. M. d. O., and Santos, W. B. (2023). Successful practices in industry-academy collaboration in the context of software agility: A systematic literature review. In Filipe, J., Śmiałek, M., Brodsky, A., and Hammoudi, S., editors, Enterprise Information Systems, pages 292–310, Cham. Springer Nature Switzerland.
Mcshane, M. and Nirenburg, S. (2021). Linguistics for the Age of AI.
Petersen, K., Gencel, C., Asghari, N., Baca, D., and Betz, S. (2014). Action research as a model for industry-academia collaboration in the software engineering context. WISE ’14, page 55–62, New York, NY, USA. Association for Computing Machinery.
Sandberg, A. B. and Crnkovic, I. (2017). Meeting industry-academia research collaboration challenges with agile methodologies. In 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP), pages 73–82.
Wohlin, C., Aurum, A., Angelis, L., Phillips, L., Dittrich, Y., Gorschek, T., Grahn, H., Henningsson, K., Kågström, S., Low, G., Rovegard, P., Tomaszewski, P., Van Toorn, C., and Winter, J. (2012). The success factors powering industry-academia collaboration. IEEE Software, 29:67–73.
Carver, J. and Prikladnicki, R. (2018). Industry–academia collaboration in software engineering. IEEE Software, 35:120–124.
Dallegrave, T. and Santos, W. B. (2023). Action research for industry academia collaboration : A replication study. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6.
Garousi, V., Petersen, K., and Ozkan, B. (2016). Challenges and best practices in industry-academia collaborations in software engineering: A systematic literature review. Information and Software Technology, 79:106–127.
Garousi, V., Pfahl, D., Fernandes, J., Felderer, M., Mäntylä, M., Shepherd, D., Arcuri, A., Coşkunçay, A., and Tekinerdogan, B. (2019). Characterizing industry-academia collaborations in software engineering: evidence from 101 projects. Empirical Software Engineering, 24.
Kitchenham, B. A., Budgen, D., and Brereton, P. (2015). Evidence-Based Software Engineering and Systematic Reviews. Chapman & Hall/CRC.
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., and McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. In Bontcheva, K. and Zhu, J., editors, Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 55–60, Baltimore, Maryland. Association for Computational Linguistics.
Marijan, D. and Gotlieb, A. (2021). Industry-academia research collaboration in software engineering: The certus model. Information and Software Technology, 132:106473.
Marques, D., Rocha, R., Santos, B., Pacheco, F., Rodrigues, C., and Santos, W. (2024). Industry academia and government collaboration to reduce gaps in software engineering: Applications for students and professionals in career transition. In Anais do XXIII Simpósio Brasileiro de Qualidade de Software, page 657–664, Porto Alegre, RS, Brasil. SBC.
Marques, D. d. G., Dallegrave, T. L. D. d. A., Barbosa, L. E. L., Rodrigues, C. M. d. O., and Santos, W. B. (2022). Industry-academy collaboration in agile methodology: a systematic literature review. In 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6.
Marques, D. d. G., Dallegrave, T. L. D. d. A., Rodrigues, C. M. d. O., and Santos, W. B. (2023). Successful practices in industry-academy collaboration in the context of software agility: A systematic literature review. In Filipe, J., Śmiałek, M., Brodsky, A., and Hammoudi, S., editors, Enterprise Information Systems, pages 292–310, Cham. Springer Nature Switzerland.
Mcshane, M. and Nirenburg, S. (2021). Linguistics for the Age of AI.
Petersen, K., Gencel, C., Asghari, N., Baca, D., and Betz, S. (2014). Action research as a model for industry-academia collaboration in the software engineering context. WISE ’14, page 55–62, New York, NY, USA. Association for Computing Machinery.
Sandberg, A. B. and Crnkovic, I. (2017). Meeting industry-academia research collaboration challenges with agile methodologies. In 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP), pages 73–82.
Wohlin, C., Aurum, A., Angelis, L., Phillips, L., Dittrich, Y., Gorschek, T., Grahn, H., Henningsson, K., Kågström, S., Low, G., Rovegard, P., Tomaszewski, P., Van Toorn, C., and Winter, J. (2012). The success factors powering industry-academia collaboration. IEEE Software, 29:67–73.
Publicado
19/05/2025
Como Citar
MARQUES, Denis de Gois; RODRIGUES, Cleyton Mario de Oliveira; SANTOS, Wylliams Barbosa.
Intelligent Platform for Industry-Academia Collaboration in Research Project and Researcher Prospecting. In: TRILHA DE TEMAS, IDEIAS E RESULTADOS EMERGENTES EM SISTEMAS DE INFORMAÇÃO - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 21. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 324-330.
DOI: https://doi.org/10.5753/sbsi_estendido.2025.246884.
