Evolving Practices in Distributed R&D&I Projects: Bridging Academia and Industry Through Lightweight Collaboration
Abstract
This presentation reports the best practices and methods adopted by the Alan Turing Lab (ATLab) research group in the past years, running distributed research, development, and innovation (R&D&I) projects with a global industry partner. This work highlights the software engineering processes thatwere iteratively refined through years of collaboration.We also share insights in two domains where our researchers stand out: Visualization and Artificial Intelligence. This report may be useful for research groups and organizations beginning to engage in innovation partnerships.
Keywords:
Academy/Industry Partnership, Research, Development, Innovation, Experience Report
References
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Josu Diaz-De-Arcaya, Ana I Torre-Bastida, Gorka Zárate, Raúl Miñón, and Aitor Almeida. 2023. A joint study of the challenges, opportunities, and roadmap of MLOps and AIOps: A systematic survey. Comput. Surveys 56, 4 (2023), 1–30.
Christof Ebert, Marco Kuhrmann, and Rafael Prikladnicki. 2016. Global software engineering: Evolution and trends. In 2016 IEEE 11th International Conference on Global Software Engineering (ICGSE). IEEE, 144–153.
Dominik Kreuzberger, Niklas Kühl, and Sebastian Hirschl. 2023. Machine learning operations (MLOps): Overview, definition, and architecture. IEEE access 11 (2023), 31866–31879.
Robert Munro Monarch. 2021. Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI. Simon and Schuster.
Andrei Paleyes, Raoul-Gabriel Urma, and Neil D Lawrence. 2022. Challenges in deploying machine learning: a survey of case studies. ACM computing surveys 55, 6 (2022), 1–29.
Paul Parsons. 2021. Understanding data visualization design practice. IEEE Transactions on Visualization and Computer Graphics 28, 1 (2021), 665–675.
Anna Börjesson Sandberg and Ivica Crnkovic. 2017. Meeting industry-academia research collaboration challenges with agile methodologies. In 39th Intl. Conf. on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP). IEEE, 73–82.
Stefan Studer, Thanh Binh Bui, Christian Drescher, Alexander Hanuschkin, Ludwig Winkler, Steven Peters, and Klaus-Robert Müller. 2021. Towards CRISP-ML (Q): a machine learning process model with quality assurance methodology. Machine learning and knowledge extraction 3, 2 (2021), 392–413.
Maria Vieira, Vitor M. de Lima, Windson Viana, Michel Bonfim, and Paulo Rego. 2025. Enhancing Continuous Integration Workflows: End-to-End Testing Automation with Cypress. In 27th Intl. Conf. on Enterprise Information Systems (ICEIS). 160–167. DOI: 10.5220/0013230200003929
Rüdiger Wirth and Jochen Hipp. 2000. CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, Vol. 1. Manchester, 29–39.
Published
2025-09-22
How to Cite
ROCHA, Lincoln et al.
Evolving Practices in Distributed R&D&I Projects: Bridging Academia and Industry Through Lightweight Collaboration. In: BRAZILIAN SYMPOSIUM ON SOFTWARE ENGINEERING (SBES), 39. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1028-1030.
ISSN 2833-0633.
DOI: https://doi.org/10.5753/sbes.2025.11719.
