AgileQube: an approach for specification and detection of agile smells

• Ano de defesa: 2020
• Autor(a) principal: Telemaco Neto, Ulisses
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal do Rio de Janeiro; Brasil; Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia; Programa de Pós-Graduação em Engenharia de Sistemas e Computação; UFRJ
• Palavras-chave em Português: Avaliação de agilidade; Avaliação ágil; Agility Assessment; Agile Assessment; Agile Smell; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO::ENGENHARIA DE SOFTWARE
• Link de acesso: http://hdl.handle.net/11422/25741

In this research, we aimed to contribute to the area of agility assessment in the following way: first, we investigated existing agility assessment approaches and identified the following problems: 1. Unclear assessment criteria selection; 2. Unclear assessment criteria representation; 3. Lack of support for adding new assessment criterion; 4. Manual data collection and input; 5. Lack of real-time assessment feedback ; and 6. Limited Scalability. To address these problems, we extended the code smell term to the context of agility assessment, introduced the agile smell metaphor to denote a situation that may harm the adoption of an agile practice and proposed an agility assessment approach that automatically (or semi-automatically) detects agile smells in agile projects. This research produced the following contributions: (a) the Catalogue of Agile Smells, a catalogue that acts as the baseline for the proposed approach and has 20 agile smells; (b) the Agile Project Metamodel, a metamodel that contains the elements necessary to represent an agile project; (c) the Agile Smell Schema, a schema that enables the systematic specification of the agile smells; and (d) the AgileQube App, a computational supporting infrastructure composed of 4 elements (Specification Module, ETL Module, Detection Engine, and Validation Module) that together support the specification and detection of agile smells in agile projects. The resulting contributions addressed the problems identified in the existing agility assessment approaches and the reports generated in the case studies confirmed that the proposed approach, along with the other contributions, was able to automatically detect agile smells in the assessed agile projects.