Uma solução baseada em ontologia para a prevenção de erros comuns em modelos de requisitos escriitos na linguagem i*

• Ano de defesa: 2016
• Autor(a) principal: França, Heyde Francielle do Carmo
• Orientador(a): Bulcão Neto, Renato de Freitas
• Banca de defesa: Bulcão Neto, Renato de Freitas, Silva, Marcel Ferrante, Zinader, Juliana Pereira de Souza
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Engenharia de requisitos orientada a metas; Linguagem i*; Istar; Ontologia; Ontoistar; Experimentação; Goal oriented requirements engineering; Language i*; Ontology; Experimentation
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/5894

The Goal Oriented Requirements Engineering (GORE) approach represents users’ needs through goals with focus on capturing the real intentions of stakeholders. Based on the GORE technique, the i* modeling language represents system’s and organization’s goals and brings several advantages. Despite that, the i* language faces problems regarding the quality of models, which include typical mistakes of misuse of i* constructs, the presence of ambiguities on the interpretation of those constructs, and the complexity of the resulting i* models. The aim of this work is to present an ontology-based solution for i* models in order to reduce the most well-known errors while constructing such models. To achieve this goal was accomplished initially a literature search, followed by an experimental research to produce the proposed solution This solution includes the extension of an ontology called OntoiStar+ with OWL restrictions to ensure that frequent mistakes in i* models are not found. Besides, the TAGOOn+ tool was also extended to validate i* models in the iStarML language and convert those to an OWL representation.To perform the tests were modeled two different domains, Media Shop and on universities, using these domains case studies have been reproduced and measured results. Results demonstrate an approximate coverage of 70% of those common errors with extension of OntoiStar+ and more than 80% with extension of TAGOOn+ tool.