Estudo exploratório sobre o perfil de contribuição dos desenvolvedores da plataforma Topcoder

• Ano de defesa: 2018
• Autor(a) principal: Neira, Anderson Bergamini de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual de Maringá; Brasil; Departamento de Informática; Programa de Pós-Graduação em Ciência da Computação; Maringá, PR; Centro de Tecnologia
• Palavras-chave em Português: Desenvolvimento de software; Crowdsourcing; Plataforma Topcoder; Ciências Exatas e da Terra; Ciência da Computação
• Link de acesso: http://repositorio.uem.br:8080/jspui/handle/1/4688

Crowdsourcing is a movement that has attracted renowned companies and participants from around the world. Currently, there are several crowdsourcing platforms focused on software development. The Topcoder platform is one of the popular platforms on the market. Given the growth of this development model, researchers have been studying ways to improve the interaction between requester, user and platform, with the goal of ensuring a pleasant experience for all. One factor that directly affects the platform and the requesting companies is the contribution profile of the participants, since they are the ones who solve the demands created by the clients. Thus, the objective of this work is to investigate the contribution profile of users who participate in software development in the Topcoder platform. In order to analyze the contribution profile of the users, this study was divided in two phases: hyperspecialization analysis; and contribution profile analysis. In the first phase, we investigated this phenomenon by considering the hyperspecialization as something related to the challenges that developers participated (development, design or data science); and to the technologies that are required to accomplish the challenges. The analysis of hyperspecialization was quantitative and was conducted in two steps: in the first step we made use of data from all types of challenges from an 18-month time window; the second focuses only on development challenges and encompasses all the contributions of the most participatory users. As a result of the first phase, it was possible to verify the existence of hyperespecialistas users, although this profile is not the most victorious. In the second phase (contribution profile analysis), the objective was to identify different contribution profiles based on users' participation. We collected different attributes related to user participation and we grouped users using the Model-based Clustering algorithm. We observed five distinct groups of users: experts, newcomers, dropouts, late users and adventurous users. Among the groups it is possible to check users highly engaged with the platform, in addition to users who are less time on the platform and have difficulty in evolving. We believe that, in the possession of these results, it is possible to evolve in understanding the actions of crowdsourcing participants, contributing to the maintenance of the ecosystem