PERKUSICH, M. B.; http://lattes.cnpq.br/3250186213608951; PERKUSICH, Mirko Barbosa.
Resumen:
There is a high rate of software development projects that fail. Whenever problems can
be detected ahead of time, projects may have better chances of success, and therefore save
money and time. Recently, the usage of agile methodologies and frameworks have been
increasing due to its lightweight practices, rules and principles that allow frequent changes
during project execution. Scrum is the most popular agile framework. Even though it is
composed of simple practices, rules and principles, professionals find it hard to apply it in the
industry, specially, in teams used to traditional project management. To help ScrumMasters
to fulfill their duty of facilitating the usage of Scrum, this dissertation presents a method
to detect problems regarding its application in software development projects. The method
is cyclic and uses a probabilistic model to present project data to the ScrumMaster. Given
its capacity to handle uncertainties and flexibility to modifications, Bayesian Networks were
used to implement the model. The model was validated using scenarios to test it and the
method through a case study in two projects in a company. The results show that using
the method helps to detect problem in software development projects using Scrum with a
positive cost-benefit and useful to guide the team to achieve excellence.