The new coronavirus (COVID-19), declared by the World Health Organization as a pandemic, has infected more than 1 million people and killed more than 50 thousand. An infection caused by COVID-19 can develop into pneumonia, which can be detected by a chest X-ray exam and should be treated appropriately. In this work, we propose an automatic detection method for COVID-19 infection based on chest X-ray images. The datasets constructed for this study are composed of 194 X-ray images of patients diagnosed with coronavirus and 194 X-ray images of healthy patients. Since few images of patients with COVID-19 are publicly available, we apply the concept of transfer learning for this task. We use different architectures of convolutional neural networks (CNNs) trained on ImageNet, and adapt them to behave as feature extractors for the X-ray images. Then, the CNNs are combined with consolidated machine learning methods, such as k-Nearest Neighbor, Bayes, Random Forest, multilayer perceptron (MLP), and support vector machine (SVM). The results show that, for one of the datasets, the extractor-classifier pair with the best performance is the MobileNet architecture with the SVM classifier using a linear kernel, which achieves an accuracy and an F1-Score of 98.5%. For the other dataset, the best pair is DenseNet201 with MLP, achieving an accuracy and an F1-Score of 95.6%. Thus, the proposed approach demonstrates efficiency in detecting COVID-19 in X-ray images.

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Non-functional requirements (NFR) elicitation is a complex activity, since it requires specific knowledge in many different areas, such as performance, security, portability and usability. An approach for the definition of NFR elicitation guides (ADEG-NFR) was proposed with the objective of supporting the requirement engineers in carrying out this activity and providing mechanisms for customer involvement in this process. An elicitation guide consists of a set of questions, templates and examples of requirements to make the elicitation process easier using an appropriate language to customer. Besides, appropriate language to the customer's understanding is the natural language described in a clear way, avoiding the use of technical terms. This study evaluates the results of the experience of use of ADEG-NFR in a software development organisation. Results have shown that ADEG-NFR fulfills its purpose. In addition, the ADEG-NFR evaluation process has identified opportunities for improvement that can help in the evolution and adoption of ADEG-NFR by different organisations in the industry.

An increasingly common practice in large software development companies is to distribute tasks among geographically dispersed teams. This practice can bring many benefits, such as gains in terms of time and cost, but many are the challenges. One of the major challenges regards the method of assigning tasks to remote teams. This method involves knowing, classifying and ordering the factors that drive the assignment of tasks in a distributed scenario. This is a typical scenario for decision-making based on multiple criteria. Verbal decision analysis (VDA) is a multi-criteria framework to decisionmaking. This study presents a hybrid methodology structured on methods of VDA for classification ORdinal CLASSification (ORCLASS) and ordering (ZAPROS III-i) of factors that drive task assignment to distributed teams in software development projects. Tasks were grouped according to their type, i.e. requirements, architecture, implementation, and testing.

Decision support methods aim at assisting in the decision-making process by simplifying the analysis of the problem and justifying the choice of a particular potential action. Recent researches have shown that the hybridization of methods is able to overcome limitations presented by the methods when applied separately: the classification of alternatives before submitting them to an ordination methodology would be an effective way of filtering the set to be ordered. Specific Practices of Capability Maturity Model Integration were analyzed through a decision making model, assisted by the methods SAC and ZAPROS III-i. The results will be compared to previous studies.

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