Caracterização de redes de desenvolvimento colaborativo de software inspirada em modelos biológicos

• Ano de defesa: 2019
• Autor(a) principal: Gabriel Lage Calegari
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO; Programa de Pós-Graduação em Ciência da Computação; UFMG
• Palavras-chave em Português: redes de colaboração; github; modelos biológicos; redes complexas; Computação – Teses; Sistemas colaborativos – Teses; Modelos biológicos – Simulação (computadores) – Teses; GitHub – Teses. I
• Link de acesso: http://hdl.handle.net/1843/42343; http://orcid.org/0000-0002-7549-7184

Since the emergence of the theory of natural selection, collaboration systems have come to light because they have created a difficult dilemma: collaborative behaviors could reduce a collaborator’s relative fitness and act against him in natural selection. Besides this, collaboration is everywhere, at all biological level - from genes cooperating to genomes to constituted beings collaborating with each other. Human collaborative systems have been expanded with the advent of globalization and the Internet. One such system is the collaborative software development networks, with web portals devoted to the theme. The most popular of these is GitHub. Launched in 2008, GitHub has over 36 million users, and it is organized around repositories where multiple users come together to collaboratively develop software. The main objective of this dissertation is to analyze from an eminently biologically inspired perspective the collaborative software development networks built on GitHub. This dissertation built a model to reproduce the collaboration in GitHub as an ecosystem, characterizing it under different dimensions. These ecosystems have been modeled as complex networks and also characterized topologically over time. Finally, some of the key biological models for explaining collaboration have been adapted for these networks to understand whether they can also be used to explain collaborative software development.