Uma abordagem para seleção e alocação de pessoas baseada em perfis técnicos e de personalidades para projetos de software

• Ano de defesa: 2019
• Autor(a) principal: Lima, Jorcyane Araújo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso embargado
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Informática; Programa de Pós-Graduação em Informática; UFPB
• Palavras-chave em Português: Projetos de software; Seleção e alocação de pessoas; Tipologias de personalidades; Otimização multiobjetiva; Software projects; Selection and allocation of people; Personality typologies; Multiobjective optimization; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/19617

Software development project activities are grouped into functional roles, each of which is performed by a type of software engineer who carries out different activities. Therefore, for each of these roles, various technicals and personalities skills are required. Failure to meet these requirements when selecting team members and allocating roles will put the software development project at serious risk of failure as the selected people may not perform well. However, the process of selecting people and assigning them to roles is a complex process for the project manager to do based on his or her experience, because even with a relatively small number of people and roles, a large number of combinations can be identified. Thus, analyzing each option is not feasible and good options may not be considered. In order to support this decision process, exploring Search Based Software Engineering (SBSE) techniques, this paper proposes a multi-objective approach for selection and allocation of technically qualified and psychologically suitable people for each functional role of the software development project. To evaluate the proposal, besides comparing with the existing literature related to the theme of the proposed approach, the paper presents results from three distinct metaheuristics capable of dealing with the multiple objectives to be optimized. These algorithms are applied to real data collected in a case study that reveal the efficiency and potential applicability of the proposed approach.