01.1 - Graduação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/2
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Resultados da Pesquisa
Item Análise de dados coletados para a melhoria de uma suite de testes em um site de e-commerce(2024-03-08) Lubambo, Manoela Timossi; Monteiro, Cleviton Vinicius Fonsêca; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/5743969633597802Quality is important in Software Engineering so that systems maintain and meet specified requirements, are reliable, efficient and free from defects. This is guaranteed through a series of standards, practices and processes. As an essential part of the quality assurance process, software testing aims to verify the software’s compliance with established functional and non-functional requirements, such as performance, security, usability, reliability, among others. They are conducted by running the software under controlled conditions, using specific techniques and strategies to detect problems and ensure their correction. One of its diverse approaches is through test automation. In this work, a detailed report is made on the process of automating a test suite, highlighting the challenges faced throughout this process. And, a thorough analysis of the data collected regarding automation is carried out and, through this analysis, we seek identify the root of the problems related to the lack of effectiveness of the automation presented, where possible improvements are identified based on the results obtained, aiming to optimize the effectiveness of the test automation process.Item Métodos computacionais para a análise de dados de expressão gênica provenientes de uma análise de microarray utilizada para teste farmacológico(2023-04-28) Costa, Allan Mesquita da; Melo, Jeane Cecília Bezerra de; Costa, Luciana Amaral de Mascena; http://lattes.cnpq.br/2352032088330896; http://lattes.cnpq.br/8499459630583005; http://lattes.cnpq.br/2703136397519338The advent of the Human Genome Project (HGP), completed in October 2003, propelled the development of techniques for obtaining and analyzing biological data. The need to manage the vast amount of digital genome data was a decisive factor in the growth of a multidisciplinary area of knowledge, Computational Biology. In the two decades following the completion of the HGP, genomes of different organisms were obtained. Regarding mammals, projects such as the 1000 Genomes Project and the Cancer Genome Atlas (TCGA) illustrated the advancement of knowledge in the analysis of complex data. Among the newest techniques, we highlight Microarrays. They provide a significant amount of data in a single experiment, allowing the comparison of complete genomes. The analysis of Microarray data is relatively complex and requires protocols that make this analysis simpler, producing more understandable information. The present study involves the use of computational methods to analyze gene expression data obtained from a Microarray experiment used for pharmacological testing related to breast cancer. To process the raw data, obtained from a spreadsheet containing more than 3216 genes resulting from a Microarray analysis, a script was developed to facilitate the extraction of information from this data and subsequent selection of genes of interest. The program allowed the search for genes involved in the processes of cell death (apoptosis, necrosis, and autophagy), which are determining factors in the success analysis of the tested drug. To categorize the genes involved in the apoptotic, necrotic, and autophagic death cascade, heatmaps were constructed from fold-change values (difference in gene expression for values before and after treatment of cancerous cells with the mesoionic compound), using k-means clustering and hierarchical clustering techniques provided in the Heatmapper program. Results of the study include the development of a script in the R program that resulted in the separation of 20 genes involved in the apoptotic death cascade, six involved in the autophagic death, and seven involved in the necrotic death cascade, as well as the development of three heatmaps, contributing to the biological analysis of data, in addition to making Microarray data processing more accessible.Item Uso de business intelligence como ferramenta de apoio em previsões de apostas de jogos de futebol(2021-12-16) Domingues, Marcela Soares; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/6238090449898774The number of Brazilians that currently make investments on online sports betting, mainly related to football teams, are huge. This practice is allowed in Brazil since 2018, according to the law 3.756/18. With this permission granted, added to the Brazilian passion for football games, this is an investment area that has been growing fast since then, reaching high levels of financial movements, making people to call on this practice as guaranty of extra or main source of incomes. To make an investment in an specific bet, the ideal is that you have some idea of the risks you are taking. Betting involves losses and sometimes the investments are worthless. To know that, is necessary to take into account some variables as historical data from football teams and players. The use of Business Intelligence, that is already used for some teams to study the behaviour of others, is also of great value when it comes to analyze which bets are worth to make an invesment. Through BI concepts, this paper presents the development of a dashboard containing graphs and reports with the intention to help on the bet analysis. Besides that, it also presents a predict model to find out the amount of goals in an specific match and the calculation of winning probabilities of each team in a given match.Item Análise de dados de redes Wi-Fi por meio de redes de correlação(2021-12-17) Ferreira, Anderson dos Santos; Goncalves, Glauco Estacio; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/6157118581200722The growing use of Wi-Fi networks has generated a large volume of data that allow studies to analyze human behavior. One of the ways to study these data is through a complex network created from correlation coefficients. Data from the UFRPE Wi-Fi network were collected during 6 days, the creation of time series allowed the analysis of the occupation graphs of the headquarters buildings, thus enabling the creation of complex networks based on correlation coefficients. The occupancy graphs showed the times and buildings with the highest occupancy for each day, while the complex network metrics showed a strong correlation of buildings with different degree values.Item Uso da ciência de dados para estudo de falhas e fraudes dos abastecimentos de postos de gasolina(2019-12-19) Arruda, Luiz Felipe Ribeiro de; Albuquerque Júnior, Gabriel Alves de; Roullier, Ana; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/1825682578554550