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.