Towards a Quantitative Model to Deal with Uncertainty Management in Software Projects
Resumo
The evolution of thinking in project management has raised interest in areas not yet explored by researchers and practitioners of project management, including the management of uncertainties associated with risk management. The correct risk and uncertainty management in software projects can represent a competitive differential for the software development industry. Despite the increasing use of uncertainty management strategies, many projects still fail. Some recent studies show that the current techniques used to manage uncertainties organize the project's known information, but give little or no indication of the unknown information or uncertainties associated with the project. These techniques do not take into account the impact of existing dependency and interdependence relationships between the various sources of uncertainties in the project. This work will apply Action Research to develop a model with a focus on uncertainty quantification techniques. This work aims to present a model with a focus on uncertainty quantification techniques that take into account the relationships of dependencies and interdependence that exist between the sources of risks and uncertainties in software projects and as a result, contribute with the advance of state of the art in the practice of risk and uncertainty management in project software.
Palavras-chave:
Software Engineering Project Management Uncertainty in Project Management Quantification Techniques
Referências
Atkinson, R., Crawford, L., and Ward, S. (2006). Fundamental uncertainties in projects and the scope of project management. International Journal of Project Management, 24(8):687 – 698. Rethinking Project Management.
Browning, T. and Ramasesh, R. (2015). Reducing unwelcome surprises in project management. 56:53–62. Chapman, C. and Ward, S. (2011). How to manage project opportunity and risk.
Dorp, J. V. and Duffey, M. (1999). Statistical dependence in risk analysis for project networks using monte carlo methods. International Journal of Production Economics, 58(1):17 – 29.
Kerzner, H. (2009). Project Management: a systems to aplanning, scheduling, and controling.
Khodakarami, V. and Abdi, A. (2014). Project cost risk analysis: A bayesian networks approach for modeling dependencies between cost items. International Journal of Project Management, 32(7):1233 – 1245.
Khodakarami, V., Fenton, N., and Neil, M. (2007). Project scheduling: Improved approach to incorporate uncertainty using bayesian networks. Project Management Journal, 38:39–49.
Marinho, M., Lima, T., Sampaio, S., and de Moura, H. P. (2015a). Uncertainty management in software projects - an action research. In Proceedings of the XVIII Ibero- American Conference on Software Engineering, CIbSE 2015, Lima, Peru, Apr 22-24, 2015., page 323.
Marinho, M., Sampaio, S., Luna, A., Lima, T., and Moura, H. (2015b). Dealing with uncertainties in software project management. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pages 889–894. IEEE.
Marinho, M. L., Sampaio, S., and Moura, H. (2017). Managing uncertainty in software projects. Innovations in Systems and Software Engineering, 14.
McLain, D. (2009). Quantifying project characteristics related to uncertainty. Project Management Journal, 40(4):60–73.
Merriam, S. and Tisdell, E. (2015). Qualitative Research: A Guide to Design and Implementation. The Jossey-Bass higher and adult education series. Wiley.
Moura, H. (2015). Software project framework. Federal Univerity of Pernambuco, Recife, Pernambuco, Tech. Rep., 2011.
Padalkar, M. and Gopinath, S. (2016). Six decades of project management research: Thematic trends and future opportunities. International Journal of Project Management, 34:1305–1321.
PMI, P.M. I. (2017). A Guide to the ProjectManagement Body of Knowledge (PMBOK Guide) — Sixth Edition and Agile Practice Guide (ENGLISH). Project Management Institute.
Ramasesh, R. V. and Browning, T. R. (2014). A conceptual framework for tackling knowable unknown unknowns in project management. Journal of Operations Management, 32(4):190 – 204.
Staron, M. (2020). Action Research as Research Methodology in Software Engineering, pages 15–36. Springer International Publishing, Cham.
Taipalus, T., Sepp¨anen, V., and Pirhonen, M. (2020). Uncertainty in information system development: Causes, effects, and coping mechanisms. Journal of Systems and Software, 168:110655.
Trentim, M. H. (2011). Gerenciamento de Projetos:guia para as certificac¸ ˜oes CAPM E PMP. Atlas, New York, NY, USA, 1 edition.
Yin, R. (2013). Case Study Research: Design and Methods. SAGE Publications.
Browning, T. and Ramasesh, R. (2015). Reducing unwelcome surprises in project management. 56:53–62. Chapman, C. and Ward, S. (2011). How to manage project opportunity and risk.
Dorp, J. V. and Duffey, M. (1999). Statistical dependence in risk analysis for project networks using monte carlo methods. International Journal of Production Economics, 58(1):17 – 29.
Kerzner, H. (2009). Project Management: a systems to aplanning, scheduling, and controling.
Khodakarami, V. and Abdi, A. (2014). Project cost risk analysis: A bayesian networks approach for modeling dependencies between cost items. International Journal of Project Management, 32(7):1233 – 1245.
Khodakarami, V., Fenton, N., and Neil, M. (2007). Project scheduling: Improved approach to incorporate uncertainty using bayesian networks. Project Management Journal, 38:39–49.
Marinho, M., Lima, T., Sampaio, S., and de Moura, H. P. (2015a). Uncertainty management in software projects - an action research. In Proceedings of the XVIII Ibero- American Conference on Software Engineering, CIbSE 2015, Lima, Peru, Apr 22-24, 2015., page 323.
Marinho, M., Sampaio, S., Luna, A., Lima, T., and Moura, H. (2015b). Dealing with uncertainties in software project management. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pages 889–894. IEEE.
Marinho, M. L., Sampaio, S., and Moura, H. (2017). Managing uncertainty in software projects. Innovations in Systems and Software Engineering, 14.
McLain, D. (2009). Quantifying project characteristics related to uncertainty. Project Management Journal, 40(4):60–73.
Merriam, S. and Tisdell, E. (2015). Qualitative Research: A Guide to Design and Implementation. The Jossey-Bass higher and adult education series. Wiley.
Moura, H. (2015). Software project framework. Federal Univerity of Pernambuco, Recife, Pernambuco, Tech. Rep., 2011.
Padalkar, M. and Gopinath, S. (2016). Six decades of project management research: Thematic trends and future opportunities. International Journal of Project Management, 34:1305–1321.
PMI, P.M. I. (2017). A Guide to the ProjectManagement Body of Knowledge (PMBOK Guide) — Sixth Edition and Agile Practice Guide (ENGLISH). Project Management Institute.
Ramasesh, R. V. and Browning, T. R. (2014). A conceptual framework for tackling knowable unknown unknowns in project management. Journal of Operations Management, 32(4):190 – 204.
Staron, M. (2020). Action Research as Research Methodology in Software Engineering, pages 15–36. Springer International Publishing, Cham.
Taipalus, T., Sepp¨anen, V., and Pirhonen, M. (2020). Uncertainty in information system development: Causes, effects, and coping mechanisms. Journal of Systems and Software, 168:110655.
Trentim, M. H. (2011). Gerenciamento de Projetos:guia para as certificac¸ ˜oes CAPM E PMP. Atlas, New York, NY, USA, 1 edition.
Yin, R. (2013). Case Study Research: Design and Methods. SAGE Publications.
Publicado
19/10/2020
Como Citar
BARBOSA, Jefferson Ferreira; DE MOURA, Hermano Perrelli; MARINHO, Marcelo Luiz Monteiro.
Towards a Quantitative Model to Deal with Uncertainty Management in Software Projects. In: WORKSHOP DE TESES E DISSERTAÇÕES (WTDSOFT) - CONGRESSO BRASILEIRO DE SOFTWARE: TEORIA E PRÁTICA (CBSOFT), 11. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 91-99.
DOI: https://doi.org/10.5753/cbsoft_estendido.2020.14614.