Assisting the continuous improvement of Scrum projects using metrics and Bayesian networks. Issue 6 (13th November 2016)

• Record Type: Journal Article
• Authors: Perkusich, Mirko; Gorgônio, Kyller Costa; Almeida, Hyggo; Perkusich, Angelo
• Other Names: Mishra Alok guestEditor.; Garbajosa Juan guestEditor.; Wang Xiaofeng guestEditor.; Bosch Jan guestEditor.; Abrahamsson Pekka guestEditor.
• Is Part Of: Journal of software. Volume 29:Issue 6(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-11-13
• Subjects: agile methods -- Bayesian networks -- continuous improvement -- KaizenScrum; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1835
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Scrum is a simple process to understand, but hard to adopt. Therefore, there is a need for resources to assist on its adoption. In this paper, we present the process followed to build a Bayesian network to assist on the assessment of the quality of the software process in the context of Scrum projects. The model provides data to help Scrum Masters lead the improvement of business value delivery of Scrum teams. The process is divided into 2 phases. In the first phase, we built the Bayesian network based on expert knowledge extracted from the literature and experts. We used a top‐down approach and reasoning to define the key metrics necessary to build the models and their relationships. In the second phase, we updated the Bayesian network based on limitations of the first version. We validated the Bayesian network inferences with 10 simulated scenarios. Comparing both versions, for all scenarios, we improved the accuracy of the inferences. Therefore, we concluded that the Bayesian networks adequately represent Scrum projects from the viewpoint of the Scrum Master. Finally, the model built is in conformance with agile methods tailoring and can be adapted to any Scrum team.