Despite the fact that the test phase is described in the literature as one of the most relevant for quality assurance in software projects, this test phase is not usually developed, among others, with enough resources, time or suitable techniques.To offer solutions which supply the test phase, with appropriate tools for the automation of tests generation, or even, for their self-execution, could become a suitable way to improve this phase and reduce the cost constraints in real projects.This paper focuses on answering a concrete research question: is it possible to generate test cases from functional requirements described in an informal way? For this aim, it presents an overview of a set of relevant approaches that works in this field and offers a set of comparative analysis to determine which the state of the art is.
Technology and the Internet have changed how travel is booked, the relationship between travelers and the tourism industry, and how tourists share their travel experiences. As a result of this multiplicity of options, mass tourism markets have been dispersing. But the global demand has not fallen; quite the contrary, it has increased. Another important factor, the digital transformation, is taking hold to reach new client profiles, especially the so-called third generation of tourism consumers, digital natives who only understand the world through their online presence and who make the most of every one of its advantages. In this context, the digital platforms where users publish their impressions of tourism experiences are starting to carry more weight than the corporate content created by companies and brands. In this paper, we propose using different deep-learning techniques and architectures to solve the problem of classifying the comments that tourists publish online and that new tourists use to decide how best to plan their trip. Specifically, in this paper, we propose a classifier to determine the sentiments reflected on the http://booking.com and http://tripadvisor.com platforms for the service received in hotels. We develop and compare various classifiers based on convolutional neural networks (CNN) and long short-term memory networks (LSTM). These classifiers were trained and validated with data from hotels located on the island of Tenerife. An analysis of our findings shows that the most accurate and robust estimators are those based on LSTM recurrent neural networks.
The goal of this study was to compare the processing of social information in deaf and hearing adolescents. A task was developed to assess social information processing (SIP) skills of deaf adolescents based on Crick and Dodge's (1994; A review and reformulation of social information-processing mechanisms in children's social adjustment. Psychological Bulletin, 115, 74-101) reformulated six-stage model. It consisted of a structured interview after watching 18 scenes of situations depicting participation in a peer group or provocations by peers. Participants included 32 deaf and 20 hearing adolescents and young adults aged between 13 and 21 years. Deaf adolescents and adults had lower scores than hearing participants in all the steps of the SIP model (coding, interpretation, goal formulation, response generation, response decision, and representation). However, deaf girls and women had better scores on social adjustment and on some SIP skills than deaf male participants.
The system testing allows to verify the behaviour of the system under test and to guarantee the satisfaction of its requirements. This work describes a complete process to generate test cases from use cases for web applications. This process also resolves the lacks detected in existing approaches.
One of today's greatest technological challenges is adding renewable energies to an electric grid, with the goal being to achieve sustainable and environmentally friendly electricity generation that is also affordable. In order for the incorporation of renewables to be successful, however, predictive tools are required which can be used to determine sufficiently far in advance how much renewable energy will be available to be injected into the grid so that the remaining generation sources, including those based on fossil fuels, can be adjusted in order to fill the demand. This would limit the environmental impact and the dependence on this type of fuel in a foreseeable shortfall scenario. This paper seeks to advance in the creation of these predictive generation models for wind farms using deep learning. We present a predictive model based on a deep, multi-layered neural network that based on a forecast for atmospheric conditions is capable of estimating the generation produced by a wind farm 24 h in advance. These models were trained and validated with data from a wind farm located on the island of Tenerife and show that the best of these predictors is more precise than the reference estimator and the prediction model currently used at the farm. We also note that the problem does not require models based on truly deep neural networks. However, the workflow for correctly developing, training, validating, and tuning these models is greatly enhanced by the advantages that deep learning techniques and tools can offer.
Making every component of an electrical system work in unison is being made more challenging by the increasing number of renewable energies used, the electrical output of which is difficult to determine beforehand. In Spain, the daily electricity market opens with a 12-hour lead time, where the supply and demand expected for the following 24 hours are presented. When estimating the generation, energy sources like nuclear are highly stable, while peaking power plants can be run as necessary. Renewable energies, however, which should eventually replace peakers insofar as possible, are reliant on meteorological conditions. In this paper we propose using different deep-learning techniques and architectures to solve the problem of predicting wind generation in order to participate in the daily market, by making predictions 12 and 36 hours in advance. We develop and compare various estimators based on feedforward, convolutional, and recurrent neural networks. These estimators were trained and validated with data from a wind farm located on the island of Tenerife. We show that the best candidates for each type are more precise than the reference estimator and the polynomial regression currently used at the wind farm. We also conduct a sensitivity analysis to determine which estimator type is most robust to perturbations. An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.
e-Health Systems quality management is an expensive and hard process that entails performing several tasks such as analysis, evaluation, and quality control. Furthermore, the development of an e-Health System involves great responsibility since people's health and quality of life depend on the system and services offered. The focus of the following study is to identify the gap in Quality Characteristics for e-Health Systems, by detecting not only which are the most studied, but also which are the most used Quality Characteristics these Systems include. A strategic study is driven in this paper by a Systematic Literature Review so as to identify Quality Characteristics in e-Health. Such study makes information and communication technology organizations reflect and act strategically to manage quality in e-Health Systems efficiently and effectively. As a result, this paper proposes the bases of a Quality Model and focuses on a set of Quality Characteristics to enable e-Health Systems quality management. Thus, we can conclude that this paper contributes to implementing knowledge with regard to the mission and view of e-Health (Systems) quality management and helps understand how current researches evaluate quality in e-Health Systems.
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