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Título : Tailoring agile practices in distributed large-scale contexts : TARGET framework
Autor : FIGUEREDO, Rafael da Camara
Palabras clave : Engenharia de software; Desenvolvimento de software; Agilidade
Fecha de publicación : 19-jun-2023
Editorial : Universidade Federal de Pernambuco
Citación : FIGUEREDO, Rafael da Camara. Tailoring agile practices in distributed large-scale contexts: TARGET framework. 2023. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de Pernambuco, Recife, 2023.
Resumen : With the increasing adoption of agile methodologies in distributed software development teams, there is a need to adapt these practices for large-scale environments. However, the lack of specific guidance can make this process difficult. This study evaluates how large-scale agile distributed teams are tailoring their practices to accommodate the needs of their context. A Systematic Literature Review Systematic Literature Review (SLR) was conducted on the adaptation of agile in large-scale DSD projects, which identified 95 adapted practices from five agile frameworks used by various case studies between 2007 and 2021. Most studies adapted Scrum with 32 customized practices for their distributed realities in a large-scale context, followed by Scaled Agile Framework Scaled Agile Framework (SAFe) with 24 practices, Large Scale Scrum Large Scale Scrum (LeSS) with 17, the Spotify model with 13, and Disciplined Agile Delivery Disciplined Agile Delivery (DAD) with nine practices. Based on the content of the SLR, it was possible to generate the Tailoring lARge-scale aGilE pracTices Framework (TARGET) framework, which serves as a guide specializing in adapting agile practices to the specific context of various organizations from 17 business domains across different scale dimensions and using different agile and scaling agile frameworks. Finally, a case study was conducted on a distributed agile team from a software consultancy in Brazil to analyze the adherence of the TARGET framework to the reality of a real market case, with positive results.
URI : https://repositorio.ufpe.br/handle/123456789/52437
Aparece en las colecciones: Dissertações de Mestrado - Ciência da Computação

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