4.6 Article

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

Journal

IEEE ACCESS
Volume 10, Issue -, Pages 68981-68994

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3186347

Keywords

Task analysis; Genetic algorithms; Search problems; Resource management; Software engineering; Software algorithms; Costs; Intelligent software engineering; search-based software engineering; genetic algorithm; multiple team formation problem

Funding

  1. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brazil (CAPES) [001]

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Forming effective teams for multiple projects is a challenging task, known as the Multiple Team Formation problem. Existing solutions do not work well for Scrum projects. Therefore, we developed a two-step approach, including a Structured Task Model and a Genetic Algorithm, to form teams for target projects. Our approach achieved high precision and acceptance rate, indicating the potential of providing teams close to project managers' expectations. The Structured Task Model also offers a promising way to build technical profiles for Scrum developers.
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.

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