Reduzindo Erros Comuns em Modelos de Requisitos Baseados na Linguagem i*: Uma Abordagem Ontológica / Mitigating Common Errors in istar-based Requirement Models: An Ontological Approach

A linguagem de modelagem i* representa os objetivos do sistema e da organização e traz diversas vantagens. Entretanto, o uso indevido de construtores da i*, ambiguidades na interpretação desses construtores e a complexidade dos modelos i* têm sido frequentemente relatados. Este artigo descreve uma abordagem baseada em ontologias que reduz em aproximadamente 70% esses problemas na construção de modelos de requisitos i*

An ontology-basead solution for prevention of common mistakes in models requirements written in the language i*

Submitted by Marlene Santos ([email protected]) on 2016-08-09T17:19:49Z No. of bitstreams: 2 Dissertação - Heyde Francielle do Carmo França - 2016.pdf: 7287432 bytes, checksum: 9138c675f605c1734af600ab0faf3141 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Approved for entry into archive by Luciana Ferreira ([email protected]) on 2016-08-10T11:33:59Z (GMT) No. of bitstreams: 2 Dissertação - Heyde Francielle do Carmo França - 2016.pdf: 7287432 bytes, checksum: 9138c675f605c1734af600ab0faf3141 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2016-08-10T11:33:59Z (GMT). No. of bitstreams: 2 Dissertação - Heyde Francielle do Carmo França - 2016.pdf: 7287432 bytes, checksum: 9138c675f605c1734af600ab0faf3141 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-03-29The 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.A abordagem de Engenharia de Requisitos Orientada a Metas (do Inglês, GORE) representa as necessidades dos usuários através de metas e intenções, focando em capturar a real intenção dos stakeholders. Baseada na técnica GORE, a linguagem de modelagem i* representa metas do sistema e da organização e traz diversas vantagens. Apesar disso, a linguagem i* apresenta problemas relacionados à qualidade dos modelos, que incluem erros típicos de mau uso dos construtores, à presença de ambiguidades na interpretação dos construtores e à complexidade dos modelos resultantes. Assim, o objetivo desta dissertação é apresentar uma solução baseada em ontologia visando a redução de erros comuns em modelos de requisitos construídos na linguagem i*. Para atingir tal objetivo foi realizada inicialmente uma pesquisa bibliográfica, seguida de uma pesquisa experimental para produzir a solução proposta. Esta solução foi implementada realizando a extensão de um ontologia chamada OntoiStar+, na qual foram inseridas restrições na linguagem OWL para garantir que os erros frequentes de modelos i* não sejam reproduzidos. Foi realizada também a extensão da ferramenta TAGOOn+ para validação de modelos i* escritos em iStarML e conversão para modelos em OWL. Para realização dos testes foram modelados dois domínios diferentes, o Media Shop e um sobre universidades, usando estes domínios foram reproduzidos estudos de casos e mensurados os resultados. Os testes realizados em ambas soluções geraram resultados satisfatórios. Os resultados demonstraram uma cobertura de mais de 70% dos erros mais comuns com a extensão da OntoiStar+ e mais de 80% com a extensão da ferramenta TAGOOn+

Convolutional Neural Networks in the Inspection of Serrasalmids (Characiformes) Fingerlings

Aquaculture produces more than 122 million tons of fish globally. Among the several economically important species are the Serrasalmidae, which are valued for their nutritional and sensory characteristics. To meet the growing demand, there is a need for automation and accuracy of processes, at a lower cost. Convolutional neural networks (CNNs) are a viable alternative for automation, reducing human intervention, work time, errors, and production costs. Therefore, the objective of this work is to evaluate the efficacy of convolutional neural networks (CNNs) in counting round fish fingerlings (Serrasalmidae) at different densities using 390 color photographs in an illuminated environment. The photographs were submitted to two convolutional neural networks for object detection: one model was adapted from a pre-trained CNN and the other was an online platform based on AutoML. The metrics used for performance evaluation were precision (P), recall (R), accuracy (A), and F1-Score. In conclusion, convolutional neural networks (CNNs) are effective tools for detecting and counting fish. The pre-trained CNN demonstrated outstanding performance in identifying fish fingerlings, achieving accuracy, precision, and recall rates of 99% or higher, regardless of fish density. On the other hand, the AutoML exhibited reduced accuracy and recall rates as the number of fish increased