An Empirical Evaluation of a Model for dealing with Epistemic Uncertainty in Agile Software Project Management


Context: The current trend of employing agility in software development indicates the need to manage uncertainty through its cycles of inspection and adaptation to changes. Problem: Despite the increasing agile methods and uncertainty management approaches, many agile software projects still fail. Some studies show that existing approaches to managing uncertainty do not consider the quantitative aspect of managing uncertainty in agile projects. The construction of approaches that fill the identified gap involves research methods that can generate results artifacts, methods, frameworks, or models. These approaches need to be evaluated before they are made available to practitioners of uncertainty management in the industry. Solution: This article describes an empirical evaluation process of a model called Euler (version 1.0) built to deal with epistemic uncertainty in agile software project management. IS Theory: This work was conceived under the aegis of Structured process modeling theory (SPMT), particularly concerning constructing process models as more effective and efficient. Method: This study used the framework known as Proof of Concept Research (PoCR). Summary of Results: As a result of applying the PoCR, four recommendations emerged. These recommendations resulted in version 2.0 of the model. Contributions and Impact in the IS area: The industry can use it to improve the performance of organizations and the processes of managing uncertainties in agile projects.
Palavras-chave: Empirical Evaluation, Proof of Concept Research, Epistemic Uncertainty, Uncertainty Quantification, Agile Project Management


Roger Atkinson, Lynn Crawford, and Stephen Ward. 2006. Fundamental uncertainties in projects and the scope of project management. International Journal of Project Management 24, 8 (2006), 687 – 698. Rethinking Project Management.

J. F. Barbosa, M. L. M. Marinho, and H. P. de Moura. 2021. Em Direção a um Modelo para Quantificação da Incerteza Epistêmica em Projetos de Software: uma Pesquisa-Ação. Revista Ibérica de Sistemas e Tecnologias de Informação 44 (2021), 67–83.

S Basu. 2017. Evaluation of hazard and risk analysis.

Chris Chapman and Stephen Ward. 2011. How to manage project opportunity and risk.

Ali Chenarani and EA Druzhinin. 2017. A quantitative measure for evaluating project uncertainty under variation and risk effects. Engineering, Technology & Applied Science Research 7, 5 (2017), 2083–2088.

Torgeir Dingsøyr, Sridhar Nerur, VenuGopal Balijepally, and Nils Brede Moe. 2012. A decade of agile methodologies: Towards explaining agile software development. Journal of Systems and Software 85, 6 (2012), 1213–1221. Special Issue: Agile Development.

Ademilton dos Santos and Moacyr Cardoso-Junior. 2018. Uso da teoria da evidência de Dempster-Shafer na avaliação da incerteza de prazos em projeto de P&D. 5 (12 2018), 19–29.

Steve Elliott. 2021. Proof of Concept Research. Philosophy of Science 88, 2 (2021), 258–280.

Rafaela Mantovani Fontana, Victor Meyer Jr, Sheila Reinehr, and Andreia Malucelli. 2015. Progressive Outcomes: A framework for maturing in agile software development. Journal of Systems and Software 102 (2015), 88–108.

Patrick Hester. 2012. Epistemic uncertainty analysis: an approach using expert judgment and evidential credibility. Journal of Quality and Reliability Engineering 2012 (2012).

David Howell, Charlotta Windahl, and Rainer Seidel. 2010. A project contingency framework based on uncertainty and its consequences. International Journal of Project Management 28, 3 (2010), 256–264.

John Jakeman, Michael Eldred, and Dongbin Xiu. 2010. Numerical approach for quantification of epistemic uncertainty. J. Comput. Phys. 229, 12 (2010), 4648–4663.

Catherine Elizabeth Kendig. 2016. What is proof of concept research and how does it generate epistemic and ethical categories for future scientific practice?Science and Engineering Ethics 22, 3 (2016), 735–753.

Vahid Khodakarami, Norman Fenton, and Martin Neil. 2007. Project Scheduling: Improved Approach to Incorporate Uncertainty Using Bayesian Networks. Project Management Journal 38 (06 2007), 39–49.

Frank H. Knight. 1921. Risk, Uncertainty and Profit. Houghton Mifflin Co, Boston, MA.

Thomas Kühne. 2004. What is a Model? (2004).

M. Marinho, S. Sampaio, A. Luna, T. Lima, and H. Moura. 2015. 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. 889–894.

Marcelo Luiz Marinho, Suzana Sampaio, and Hermano Moura. 2017. Managing uncertainty in software projects. Innovations in Systems and Software Engineering 14 (08 2017).

Matthew B Miles, A Michael Huberman, and Johnny Saldaña. 2018. Qualitative data analysis: A methods sourcebook. Sage publications.

Colin Robson and Kieran McCartan. 2016. Real world research. John Wiley & Sons.

Per Runeson and Martin Höst. 2009. Guidelines for conducting and reporting case study research in software engineering. Empirical software engineering 14, 2 (2009), 131–164.

Miroslaw Staron. 2020. Action Research as Research Methodology in Software Engineering. Springer International Publishing, Cham, 15–36.

Priya Krishnan Sundarararajan, Ole J Mengshoel, and Ted Selker. 2013. Multi-focus and multi-window techniques for interactive network exploration. In Visualization and Data Analysis 2013, Vol. 8654. International Society for Optics and Photonics, 86540O.

Edward Tufte. 2001. The visual display of quantitative information.
Como Citar

Selecione um Formato
BARBOSA, Jefferson Ferreira; MARINHO, Marcelo Luiz Monteiro; MOURA, Hermano Perrelli. An Empirical Evaluation of a Model for dealing with Epistemic Uncertainty in Agile Software Project Management. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 19. , 2023, Maceió/AL. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 .