Bacharelado em Sistemas de Informação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/12
Siglas das Coleções:
APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso
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Resultados da Pesquisa
Item Técnicas de comitês para a estimação de esforço na correção de software(2019-12-10) Guimarães, Ariana Lima; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/2605671850587343A well-defined planning of a software project, since the early stages, is indispensable to its success, whether the development refers to product’s creation or maintenance. Accordingly to the software life cycle, maintenance is continuously executed after the product’s building and delivery, in parallel to the tests execution by engineers and/or users. In this stage, User Stories and Issue Reports are the first documents to be presented. These documents describe, in natural language, business requirements, error scenarios found, expected corrections and enhancements for the system. Its objectives are, among other things, ranking the activities needed to be accomplish during the project. Therefore, in line with the available resources – human, financial and temporal -, it is possible to estimate the effort that will be necessary in the activities development and generate essential information for an effective and efficient planning. As these documents are written in natural texts, it raises the opportunity to use Natural Language Processing and Machine Learning (ML) to predict software effort. In practice, in the daily life of software factories, it is common to use experts’ and project staff’s opinion to judge the effort required by an activity during Planning Poker sessions. Usually, in this technique, the effort is measured in Story Points, which follow Fibonacci sequence. But this planning model requires the scaling of more resources to be executed. The application of ML causes in a system, after the learning phase, the ability to seize the team experience and replicate it quickly and automatically to estimate the activities effort. Thus, this work covers the ML field, proposing a PV-DM Ensemble approach to extract features of Issue Reports to estimate Story Points, the effort indicator. Compared to the two other approaches of BoW and simple PV-DM, the proposed technique has presented good results, about 80% of f-measure, in a supervised learning SVM classifier. The experiments results proved to be a starting point for further study of PV-DM Ensemble approach and its improvement.Item Uma abordagem baseada em aprendizado de máquina para dimensionamento de requisitos de software(2016-12-13) Fernandes Neto, Eça da Rocha; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/6325583065151828This work proposes to perform the automatic sizing of software requirements using a machine learning approach. The database used is real and was obtained from a company that works with Scrum-based development process and Planning Poker es- timation. During the studies, data pre-processing, classification and selection of best attributes were used along with the term frequency–inverse document frequency algo- rithm (tf-idf) and principal component analysis (PCA). Machine learning and automatic sorting occurred with the use of Support Vector Machines (SVM) based on available data history. The final tests were performed with and without attribute selection by PCA. It is demonstrated that the assertiveness is greater when the best attributes are selected. The final tool can estimate the size of user stories with a generalization of up to 91 %. The results were considered likely to be used in the production environment without any problems to the development team.Item Programinó: um jogo para auxílio ao aprendizado do assunto de tipos de dados na programação(2019-12-13) Nascimento, Gabriele Pessoa do; Falcão, Taciana Pontual da Rocha; Sampaio, Pablo Azevedo; http://lattes.cnpq.br/8865836949700771; http://lattes.cnpq.br/5706959249737319; http://lattes.cnpq.br/9978319013197863The digital age we live in means that we are always immersed in ever more ubiquitous technologies. For this contact with technology to remain healthy, it is necessary to learn to consume it consciously, and to learn to develop it in different contexts; therefore, we will have increasingly inclusive solutions. Regarding solution development, as much as we have several facilitating artifacts, the process of programming teaching and learning is still a challenge, especially for beginning students. Dealing with so many competing and constant stimuli and still having the ability to abstract and assimilate programming concepts that is not trivial and has not been worked on since childhood, so playful artifacts such as digital games are essential to facilitate first contacts with the schedule. In this context, this work brings to society a digital educational game that deals with the subject of data types in programming, Programinó, so that beginning students can practice and consolidate the content through a playful tool. The game was developed with three levels of difficulty, one easy, one medium and one hard. The hard one applies the adapted minimax algorithm, while the easy one uses the same minimax adapted in an inverted way. The middle level uses a random algorithm. As a way to validate the difficulty levels, comparative experiments were performed that showed that the minimax lost only 5.6% of the time; winning at 49.7% or drawing in the remaining matches.Item Comparação de algoritmos de reconhecimento de gestos aplicados à sinais estáticos de Libras(2019-07-12) Cruz, Lisandra Sousa da; Cordeiro, Filipe Rolim; Macário Filho, Valmir; http://lattes.cnpq.br/4346898674852080; http://lattes.cnpq.br/4807739914511076; http://lattes.cnpq.br/2111589326272463Brazilian Sign Language (BSL) has been created in order to cope with a necessity of a non-verbal communication for the deafs, which during a long time were indoctrinated to learn the Brazilian Portuguese as their first language. Nowadays, the BSL is the Brazil’s second official language and first deaf’s language, as well as the Portuguese for the listener. Nevertheless, even with large recognition, the Brazil’s second official language is not known by the majority of the Brazilian population. The inclusion process aims to allow equality for the impaired, such that the deficiency does not become an impediment factor for living together in society. With the technology arrival and the Artificial Inteligence (AI) advances, it was created technologic artifices to allow inclusion. In the AI, the pattern recognition is one of more approached subthemes in the present, and it is widely applied for the gesture classification of many sign languages in literature. This research has, as key task, the identification of the hands that form a certain BSL gesture and, thus, the recognition of the class it belongs to. Based on American Sign Language (ASL) classification, the Feature Fusion-based Convolutional Neural Network (FFCNN), an extended network from Convolutional Neural Network (CNN), obtained the best accuracy in comparison to other networks, such as Visual Geometry Group (VGG). Therefore, based on this scenario, this work applies the FFCNN to BSL static gestures to verify whether the FFCNN obtain the best accuracy as well as obtained in ASL or not. In order to achieve the goal, this work compares three classifiers: the Visual Geometry Group (VGG), a CNN with variation of 13 and 16 layers, the FFCNN, and a Multi Layer Perceptron network used in recognition of BSL static gestures in literature. The algorithms were applied in a BSL dataset with 9,600 images of 40 signals. The results demonstrate that VGG with 16 layers obtained the best accuracy regarding the described models in this work, corresponding to 99,45%.