Bacharelado em Ciência da Computação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/6
Siglas das Coleções:
APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso
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4 resultados
Resultados da Pesquisa
Item Desenvolvimento de no-code no FlowUp: construção de campos personalizáveis(2024-10-04) Santos, João Victor Vieira Silva dos; Monteiro, Cleviton Vinicius Fonsêca; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/5366897410770757This study focuses on the implementation of No-Code features in the FlowUp ERP system to provide greater flexibility and customization for users. Using the Design Thinking methodology, customizable fields were developed, allowing for better adaptation of the system to the specific needs of each company. Interviews with stakeholders confirmed the usefulness of the feature, suggesting improvements for its expansion. The study concludes that the introduction of No-Code in FlowUp is an efficient solution for customization, offering new possibilities for system evolution and data integration.Item Racismo algorítmico no desenvolvimento de software: um estudo de caso sobre letramento racial no ensino superior(2024-08-05) Silva, Jamelly Nascimento; Falcão, Taciana Pontual da Rocha; Santos, George Augusto Valença; http://lattes.cnpq.br/8525564952779211; http://lattes.cnpq.br/5706959249737319This article presents a case study on the topic of algorithmic racism in software development, focusing on racial literacy in higher education. The study involved students and instructors at the Federal Rural University of Pernambuco (Brazil) to investigate their level of knowledge about algorithmic racism. The main objective was to understand whether this knowledge is present in any way in courses related to information technology. The results reveal important insights into awareness and understanding of algorithmic racism within academia, highlighting areas of opportunity to promote more inclusive and comprehensive teaching on these crucial issues in the technology industry.Item Desenvolvimento de um sistema com dispositivo portátil para auxílio no diagnóstico de coarctação da artéria aorta em neonatos(2018-08-16) Amorim, Jéssyca Karolinny da Silva; Barros, Abner Corrêa; http://lattes.cnpq.br/3825998752196688; http://lattes.cnpq.br/4499057406814333In recent years, advances in technology have brought unquestionable benefits to human life. Especially in the medical field, where some techniques and tests that were considered technically and economically unviable, have become usual. However, in some cases, the actual technology has not been sufficient to provide an early diagnosis of congenital diseases. it happens with Congenital Heart Disease (CC), with the most common CC being the Coarctation of the Aorta (CoA). Currently, available diagnostic methods are ineffective, leading to late diagnosis, which can lead to cardiac collapse, neurological damage, and death. In this way, the present work aims to propose an alternative to the development of a computational system that captures, by a low cost and mobile embedded device, and evaluate the photoplethysmographic (PPG) tags of neonates in order to aid in the identification and in diagnosis of CoA.Item Impacto da latência na verificação e do atraso acentuado na rotulação para detecção de falhas de software(2023-04-25) Sgotti, Lucas Cordeiro; Cabral, George Gomes; http://lattes.cnpq.br/8227256452129177; http://lattes.cnpq.br/6243434551046357Software Defect Prediction is an activity inherent to software development and it requires a high amount of human effort. This activity is often not prioritized in order toreduce the project's expenses. Just-in-Time Software Defect Prediction (JIT-SDP) is one of the approaches used for predicting software defects in order to automatically identify, through machine learning methods, software artifacts likely to contain defects based on historical data. However, most approaches assume that the characteristics of the problem remain the same over time, but the imbalance between classes is a problem that evolves over time as new training examples arrive, for example. Analyzing the problem in an online environment means that, among other things, there is an intrinsic chronological aspect to be considered which, in turn, brings with it some issues, among them verification latency, which refers to the fact that training example labels can arrive much later than their characteristics. This work aims to investigate the impact of verification latency on the problem of detecting defects in software, as well as the performance of these methods according to the degree of verification latency in examples of the defect inducing class.