A Genetic Algorithm-Based Approach to Support Forming Multiple Scrum Project Teams

• Published in: IEEE access Vol. 10; pp. 68981 - 68994
• Main Authors: Costa, Alexandre, Ramos, Felipe, Perkusich, Mirko, Neto, Ademar De Sousa, Silva, Luiz, Cunha, Felipe, Rique, Thiago, Almeida, Hyggo, Perkusich, Angelo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Costs; genetic algorithm; Genetic algorithms; Intelligent software engineering; Managers; multiple team formation problem; Resource management; Search problems; search-based software engineering; Software algorithms; Software engineering; Task analysis; Teams
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3186347

Forming effective teams is an essential but challenging task, especially for organizations that carry out multiple projects simultaneously, a problem known as the Multiple Team Formation (MTF) problem. The literature presents several solutions for the MTF problem, mostly modeling it as a search problem. However, the existing solutions are not suitable for Scrum projects. We addressed this gap by developing an approach composed of two main steps. First, we designed a Structured Task Model to support creating developers' profiles given their performance on past Scrum projects. Then, given a set of target projects' technology requirements and the available developers' profiles, we developed a Genetic Algorithm to form the teams for a set of target projects. We evaluated the proposed approach by comparing the teams formed by our approach with the ones formed by project managers from one organization. Our approach achieved 85% of precision when compared with the teams provided by the project managers who worked on the same target projects. We also recorded an acceptance rate of up to 75%. The significant value of precision achieved suggests that our approach can provide teams close to the project managers' expectations. In addition, our Structured Task Model offers a promising way to build technical profiles semi-automatically for Scrum developers. In future work, we intend to investigate how to complement the developers' profiles by using other types of attributes and knowledge sources.