TCC - Bacharelado em Sistemas da Informação (Sede)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/427
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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 Liferay Portal Upgrade: definição de um processo eficiente para upgrade de clientes em versões legadas(2022-10-11) Ferreira, Nícolas Moura do Canto; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505Item Sistema de aprendizado de máquina para predição do tempo de esforço de tarefas de desenvolvimento de software(2021-12-14) Sitonio, Tiago Pedro da Silva; Monteiro, Cleviton Vinicius Fonsêca; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/0915757895643807Item QA Metrics: integração das métricas de qualidade de software, em ambiente Docker, para exibição de dashboards Grafana alimentado pelo banco de dados temporal InfluxDB via Newman(2022-07-15) Silva, Lucas Ferreira da; Bocanegra, Silvana; Assad, Rodrigo Elia; http://lattes.cnpq.br/3791808485485116; http://lattes.cnpq.br/4596111202208863; http://lattes.cnpq.br/9075508106025707Software Quality metrics and indicators are able to help a software tester, commonly known as QA, to assess what needs to be done to improve performance of a software development project. In addition, it makes it possible to monitor the progress of a project in order to suggest initiatives based on the collected data. However, gathering the metrics obtained from different data sources to present them in real time is not an easy task for some companies. Nevertheless, with the use of APIs, it is possible to collect the data, analyze and present them in dashboards and reduce QA rework. In this paper, it was developed a system capable of collecting data from three services in order to present, in dashboards, the metrics that are essential in the area of Software Quality. Furthermore, the collection and storage of these metrics are performed in an automated and orchestrated manner through a compact, fast, efficient, safe, portable and isolated application.Item Um sistema de apoio à decisão para priorização e estruturação de histórias de usuários: um suporte para equipe ágeis(2018-08) Calado, Alex Rogério da Silva; Garrozi, Cícero; Sampaio, Suzana Cândido de Barros; http://lattes.cnpq.br/0066131495297081; http://lattes.cnpq.br/0488054917286587; http://lattes.cnpq.br/1189807442146524Despite the advances obtained in the Software Engineering with agile methods the market still shows successful rates in unsatisfactory projects. Throughout a software project, changing requirement and priority are unavoidable and recurrent. This is an important factor to the software development businesses mainly small ones with limited resources. The usage of agile techniques such as Scrum and User Stories (US) benefits the businesses and make them more competitive. One of the problems faced with agile requirements is to obtain a prioritization of secure US according to the business value given by the client in agreement with technical specifications. This paper proposes to present an essay that serves as basis to the construction of a decision support tool to the US prioritization decisions. For such was considered a software project as a temporary organization and suggested metrics that best suits the needs of small teams avoiding reworks, the increasing in the deadlines and costs, without disregarding the client satisfaction in interactive deliveries. Based on the Volere requirements template and in the literature review it was proposed the adoption of five metrics to be considered in the prioritization of the User Stories: the client satisfaction in receiving the US and the client dissatisfaction in not receiving it; replacing the traditional value of business; the so usual complexity; the necessity of team learning and; software risks. Using online forms were captured data about these metrics and the prioritization given to each US in software projects that uses the agile Scrum method. Decision Tree was the proposed suggestion for predicting the US prioritization for having a practical visualization and a more intuitive interpretation, facilitating the acceptance as a decision method support for professionals involved in the area. In spite of the low databases volume the results obtained through the Weka tool, like the precision and the ROC curve, were satisfactory without prediction tendencies and with good indexes of correctness, after the algorithms and databases adjustments avoiding overfitting and underfitting.