Impact and Insights of MBE Adoption in Knowledge Capture and Organization: A Survey of Brazilian Software Companies
DOI:
https://doi.org/10.5753/jserd.2025.5757Keywords:
Models, software development company, Knowledge Management, roadmap, MDE, MBEAbstract
Software development is a complex and knowledge-intensive process that involves multiple participants across various stages of the development lifecycle. Managing knowledge effectively in this context is challenging, especially when it comes to capturing, organizing, and reusing information in software engineering projects. Software development artifacts and techniques play a crucial role in addressing these challenges by facilitating the storage and sharing of knowledge. This study examines how model-based engineering facilitates various aspects of knowledge management. A survey of 62 Brazilian companies was conducted, providing a comprehensive roadmap for the future. Using qualitative and quantitative analyses, the findings were compared with those of other studies. The results indicate that different artifacts effectively support various knowledge management concepts across different phases of software development. Furthermore, while companies predominantly adopt artifact models in the early stages of development and recognize their benefits, they do not fully utilize their potential.
Downloads
References
ABES, S. (2024). Brazilian Software Market: Scenario and Trends. Associação Brasileira das Empresas de Software.
Agner, L. T. W., Soares, I. W., Stadzisz, P. C., and Simão, J. M. (2013). A brazilian survey on uml and model-driven practices for embedded software development. Journal of Systems and Software, 86(4):997–1005.
Akdur, D., Garousi, V., and Demirörs, O. (2018). A survey on modeling and model-driven engineering practices in the embedded software industry. Journal of Systems Architecture, 91:62–82.
Ameller, D., Franch, X., Gómez, C., Martínez-Fernández, S., Araújo, J., Biffl, S., Cabot, J., Cortellessa, V., Fernández, D. M., Moreira, A., et al. (2019). Dealing with nonfunctional requirements in model-driven development: A survey. IEEE Transactions on Software Engineering, 47(4):818–835.
American Productivity and Quality Center (2025). Levels of knowledge management maturity. [link]. Accessed: May 2025.
Bardin, L. (1977). L’analyse de contenu. Presses universitaires de France.
Bennett, J., Cooper, K., and Dai, L. (2010). Aspect-oriented model-driven skeleton code generation: A graph-based transformation approach. Science of Computer Programming, 75(8):689–725.
Bjørnson, F. O. and Dingsøyr, T. (2008). Knowledge management in software engineering: A systematic review of studied concepts, findings and research methods used. Inf. Softw. Technol., 50(11):1055–1068.
Brambilla, M., Cabot, J., and Wimmer, M. (2017). Model-Driven Software Engineering in Practice. Synthesis Lectures on Software Engineering. Morgan Claypool Publishers.
Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2):77–101.
Braun, V., Clarke, V., and Rance, N. (2014). How to use thematic analysis with interview data, pages 183–197.
Chen, P. P.-S. (1977). The entity-relationship model: a basis for the enterprise view of data. In Proceedings of the June 13-16, 1977, national computer conference, pages 77–84.
Dalkir, K. (2005). Knowledge Management in Theory and Practice. Elsevier Butterworth-Heinemann, Boston, MA.
Darimont, R., Delor, E., Massonet, P., and van Lamsweerde, A. (1997). Grail/kaos: an environment for goal-driven requirements engineering. In Proceedings of the 19th international conference on Software engineering, pages 612–613.
Davenport, T. and Prusak, L. (1998). Working Knowledge: How Organizations Manage What they Know. Harvard Business School.
Fernández-Sáez, A. M., Caivano, D., Genero, M., and Chaudron, M. R. (2015). On the use of uml documentation in software maintenance: Results from a survey in industry. In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS), pages 292–301. IEEE.
Fernández-Sáez, A. M., Chaudron, M. R., and Genero, M. (2018). An industrial case study on the use of uml in software maintenance and its perceived benefits and hurdles. Empirical Software Engineering, 23(6):3281–3345.
Flora, H. (2014). A systematic study on agile software development methodologies and practices. International Journal of Computer Science and Information Technologies, 5:3626–3637.
Forward, A. and Lethbridge, T. C. (2008). Problems and opportunities for model-centric versus code-centric software development: A survey of software professionals. In Proceedings of the 2008 International Workshop on Models in Software Engineering, page 27–32. Association for Computing Machinery.
France, R. and Rumpe, B. (2007). Model-driven development of complex software: A research roadmap. In 2007 Future of Software Engineering, page 37–54, USA. IEEE Computer Society.
Garvin, D. A. (1988). Managing Quality: The Strategic and Competitive Edge. Free Press.
Ghazi, A. N., Petersen, K., Reddy, S. S. V. R., and Nekkanti, H. (2019). Survey research in software engineering: Problems and mitigation strategies. IEEE Access, 7:24703–24718.
Gorschek, T., Tempero, E., and Angelis, L. (2014). On the use of software design models in software development practice: An empirical investigation. J. Syst. Softw., 95:176–193.
Greenacre, M. J. (2010). Correspondence analysis. WIREs Computational Statistics, 2(5):613–619.
Grossman, M., Aronson, J. E., and McCarthy, R. V. (2005). Does uml make the grade? insights from the software development community. Information and Software Technology, 47(6):383–397.
Haynes, S. N., Richard, D. C. S., and Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7:238–247.
Hutchinson, J., Whittle, J., and Rouncefield, M. (2014). Model-driven engineering practices in industry: Social, organizational and managerial factors that lead to success or failure. Science of Computer Programming, 89:144–161.
Hutchinson, J., Whittle, J., Rouncefield, M., and Kristoffersen, S. (2011). Empirical assessment of mde in industry. In Proceedings of the 33rd International Conference on Software Engineering, page 471–480. Association for Computing Machinery.
Iandoli, L. and Zollo, G. (2008). Organizational cognition and learning. Hershey: Information Science Pub.
Jolak, R., Savary-Leblanc, M., Dalibor, M., Vincur, J., Hebig, R., Pallec, X. L., Chaudron, M., Gérard, S., Polasek, I., and Wortmann, A. (2022). The influence of software design representation on the design communication of teams with diverse personalities. In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems, pages 255–265.
Jolak, R., Savary-Leblanc, M., Dalibor, M., Wortmann, A., Hebig, R., Vincur, J., Polasek, I., Le Pallec, X., Gérard, S., and Chaudron, M. R. (2020). Software engineering whispers: The effect of textual vs. graphical software design descriptions on software design communication. Empirical software engineering, 25:4427–4471.
Josse, J., Husson, F., and Pages, J. (2010). Principal component methods - hierarchical clustering - partitional clustering: why would we need to choose for visualizing data? pages 1–17.
Kavitha, R. and Irfan Ahmed, M. (2011). A knowledge management framework for agile software development teams. In 2011 International Conference on Process Automation, Control and Computing, pages 1–5.
Khalil, C. and Khalil, S. (2020). Exploring knowledge management in agile software development organizations. International Entrepreneurship and Management Journal, 16(2):555––569.
Kitchenham, B. A., Budgen, D., and Brereton, P. (2015). Evidence-Based Software Engineering and Systematic Reviews. Chapman amp; Hall/CRC.
Kleppe, A. G., Warmer, J. B., and Bast, W. (2003). MDA explained: the model driven architecture: practice and promise. Addison-Wesley Professional.
Kobayashi, O., Kawabata, M., Sakai, M., and Parkinson, E. (2006). Analysis of the interaction between practices for introducing xp effectively. In Proceedings of the 28th International Conference on Software Engineering, page 544–550. Association for Computing Machinery.
Kolovos, D. S., Paige, R. F., and Polack, F. A. C. (2006). On-demand merging of traceability links with models. In ECMDA 06 Traceability Workshop, pages 1–9.
Kotonya, G. and Sommerville, I. (1996). Requirements engineering with viewpoints. Software Engineering Journal, 11(1):5–18.
KPMG Consulting (2000). Knowledge management research report 2000: Knowledge management maturity model (kmmm). Internal report, widely cited but not publicly available.
Kulkarni, U. R., Ravindran, S., and Freeze, R. (2006). A knowledge management success model: Theoretical development and empirical validation. Journal of management information systems, 23(3):309–347.
Liebel, G., Marko, N., Tichy, M., Leitner, A., and Hansson, J. (2018). Model-based engineering in the embedded systems domain: an industrial survey on the state-of-practice. Software & Systems Modeling, 17(1):91–113.
Mellor, S. J., Clark, A. N., and Futagami, T. (2003). Guest editors’ introduction: Model-driven development. IEEE Software, 20(05):14–18.
Menolli, A., Cunha, M. A., Reinehr, S., and Malucelli, A. (2015). “old” theories, “new” technologies: Understanding knowledge sharing and learning in brazilian software development companies. Information and Software Technology, 58:289–303.
Menolli, A., Reinehr, S., and Malucelli, A. (2013). Organizational learning applied to software engineering: a systematic review. International Journal of Software Engineering and Knowledge Engineering, 23(08):1153–1175.
Molléri, J. S., Petersen, K., and Mendes, E. (2020). An empirically evaluated checklist for surveys in software engineering. Information and Software Technology, 119:106240.
Moreira, A., Mussbacher, G., Araújo, J., and Sánchez, P. (2022). Theme section on model-driven requirements engineering. Software and Systems Modeling, 21(6):2109–2112.
Nevis, E. C., Dibella, A. J., and Gould, J. M. (1995). Understanding organizations as learning systems. Sloan Management Review, 36:342–367.
Nonaka, I., Toyama, R., and Konno, N. (2000). Seci, ba and leadership: a unified model of dynamic knowledge creation. Long Range Planning, 33(1):5–34.
Nugroho, A. and Chaudron, M. R. (2008). A survey into the rigor of uml use and its perceived impact on quality and productivity. In Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, page 90–99. Association for Computing Machinery.
OMG (2003). Mda guide version 1.0.1. online. [link].
Ouriques, R., Wnuk, K., Gorschek, T., and Berntsson Svensson, R. (2018). Knowledge management strategies and processes in agile software development: A systematic literature review. International Journal of Software Engineering And Knowledge Engineering, 29(3):345–380.
Ras, E., Memmel, M., and Weibelzahl, S. (2005). Integration of e-learning and knowledge management – barriers, solutions and future issues. In Proceedings of the Third Biennial Conference on Professional Knowledge Management, page 155–164, Berlin, Heidelberg. Springer-Verlag.
Rodríguez-Sánchez, J.-L., González-Torres, T., Montero-Navarro, A., and Gallego-Losada, R. (2020). Investing time and resources for work–life balance: The effect on talent retention. International journal of environmental research and public health, 17(6):1920.
Rus, I. and Lindvall, M. (2002). Knowledge management in software engineering. IEEE Software, 19(3):26–38.
Santos, V., Shinoda, A. C. M., Goldman, A., and Fishcher, A. (2011). A view towards Organizational Learning: An empirical study on Scrum implementation. 23rd International Conference on Software Engineering and Knowledge Engineering (SEKE 2011), pages 583–589.
Schmidt, D. (2006). Guest editor’s introduction: Model-driven engineering. Computer, 39(2):25–31.
Senge, S., Kleiner, A., Roberts, C., Ross, R., and Smith, B. J. (1994). The fifth discipline field book. Doubleday.
Sommerville, I. (2011). Software Engineering. Addison-Wesley Publishing Company.
Stahl, T. and Vöelter, M. (2006). Model-Driven Software Development: Technology, Engineering, Management. John Wiley Sons Ltd.
Störrle, H. (2017). How are conceptual models used in industrial software development? a descriptive survey. In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering, EASE’17, page 160–169, New York, NY, USA. Association for Computing Machinery.
Tiwana, A. (2002). The knowledge management toolkit. Prentice Hall PTR.
Tomassetti, F., Torchiano, M., Tiso, A., Ricca, F., and Reggio, G. (2012). Maturity of software modelling and model driven engineering: A survey in the italian industry. In 16th International Conference on Evaluation & Assessment in Software Engineering (EASE 2012), pages 91–100. IET.
Torchiano, M., Tomassetti, F., Ricca, F., Tiso, A., and Reggio, G. (2011). Preliminary findings from a survey on the md state of the practice. In 2011 International Symposium on Empirical Software Engineering and Measurement, pages 372–375.
Torchiano, M., Tomassetti, F., Ricca, F., Tiso, A., and Reggio, G. (2013). Relevance, benefits, and problems of software modelling and model driven techniques—a survey in the italian industry. Journal of Systems and Software, 86(8):2110–2126.
Vasanthapriyan, S., Tian, J., and Xiang, J. (2015). A survey on knowledge management in software engineering. In 2015 IEEE International Conference on Software Quality, Reliability and Security-Companion, pages 237–244. IEEE.
Wang, X. and Cheng, Z. (2020). Cross-sectional studies: strengths, weaknesses, and recommendations. Chest, 158(1):S65–S71.
Whittle, J., Hutchinson, J., and Rouncefield, M. (2014). The state of practice in model-driven engineering. IEEE Software, 31(3):79–85.
Whittle, J., Hutchinson, J., Rouncefield, M., Burden, H., and Heldal, R. (2017). A taxonomy of tool-related issues affecting the adoption of model-driven engineering. Software & Systems Modeling, 16:313–331.
Wohlin, C. and Aurum, A. (2015). Towards a decision-making structure for selecting a research design in empirical software engineering. Empirical Softw. Engg., 20(6):1427–1455.
Wohlin, C., Runeson, P., Hst, M., Ohlsson, M. C., Regnell, B., and Wessln, A. (2012). Experimentation in Software Engineering. Springer Publishing Company, Incorporated.
Yanzer Cabral, A. R., Ribeiro, M. B., and Noll, R. P. (2014). Knowledge management in agile software projects: A systematic review. Journal of Information & Knowledge Management, 13(01).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 André Menolli, Thiago Coleti, Edson OliveiraJr
This work is licensed under a Creative Commons Attribution 4.0 International License.
