Software testing activities account for a considerable portion of systems development cost and, for this reason, many studies have sought to automate these activities. Test data generation has a high cost reduction potential (especially for complex domain systems), since it can decrease human effort. Although several studies have been published about this subject, articles of reviews covering this topic usually focus only on specific domains. This article presents a systematic mapping aiming at providing a broad, albeit critical, overview of the literature in the topic of test data generation using genetic algorithms. The selected studies were categorized by software testing technique (structural, functional, or mutation testing) for which test data were generated and according to the most significantly adapted genetic algorithms aspects. The most used evaluation metrics and software testing techniques were identified. The results showed that genetic algorithms have been successfully applied to simple test data generation, but are rarely used to generate complex test data such as images, videos, sounds, and 3D (three-dimensional) models. From these results, we discuss some challenges and opportunities for research in this area.

Background COVID-19 caused more than 622 thousand deaths in Brazil. The infection can be asymptomatic and cause mild symptoms, but it also can evolve into a severe disease and lead to death. It is difficult to predict which patients will develop severe disease. There are, in the literature, machine learning models capable of assisting diagnose and predicting outcomes for several diseases, but usually these models require laboratory tests and/or imaging. Methods We conducted a observational cohort study that evaluated vital signs and measurements from patients who were admitted to Hospital das Clínicas (São Paulo, Brazil) between March 2020 and October 2021 due to COVID-19. The data was then represented as univariate and multivariate time series, that were used to train and test machine learning models capable of predicting a patient’s outcome. Results Time series-based machine learning models are capable of predicting a COVID-19 patient’s outcome with up to 96% general accuracy and 81% accuracy considering only the first hospitalization day. The models can reach up to 99% sensitivity (discharge prediction) and up to 91% specificity (death prediction). Conclusions Results indicate that time series-based machine learning models combined with easily obtainable data can predict COVID-19 outcomes and support clinical decisions. With further research, these models can potentially help doctors diagnose other diseases.

Background Optimal COVID-19 management is still undefined. In this complicated scenario, the construction of a computational model capable of extracting information from electronic medical records, correlating signs, symptoms and medical prescriptions, could improve patient management/prognosis. Methods The aim of this study is to investigate the correlation between drug prescriptions and outcome in patients with COVID-19. We extracted data from 3674 medical records of hospitalized patients: drug prescriptions, outcome, and demographics. The outcome evaluated was hospital outcome. We applied correlation analysis using a Logistic Regression algorithm for machine learning with Lasso and Matthews correlation coefficient. Results We found correlations between drugs and patient outcomes (death/discharged alive). Anticoagulants, used very frequently during all phases of the disease, were associated with good prognosis only after the first week of symptoms. Antibiotics very frequently prescribed, especially early, were not correlated with outcome, suggesting that bacterial infections may not be important in determining prognosis. There were no differences between age groups. Conclusions In conclusion, we achieved an important result in the area of Artificial Intelligence, as we were able to establish a correlation between concrete variables in a real and extremely complex environment of clinical data from COVID-19. Our results are an initial and promising contribution in decision-making and real-time environments to support resource management and forecasting prognosis of patients with COVID-19.

A Visualização de Informação se propõe a representar dados abstratos graficamente, de modo a melhorar a compreensão dos mesmos. Sistemas de recuperação de imagens baseada em conteúdo (Content-Based Image Retrieval - CBIR) produzem grandes volumes de dados, que, muitas vezes, são exibidos de modo pouco compreensível. Tendo em vista este cenário, este artigo tem como objetivo propor e avaliar técnicas de visualização de informação que otimizem a exibição de resultados de sistemas de CBIR. Foram desenvolvidas duas técnicas bidimensionais e duas tridimensionais. Por meio de avaliação com usuários, constatou-se que as técnicas bidimensionais propostas foram as mais eficientes em melhorar a compreensão dos resultados no contexto analisado.

Agradeço a Deus. Agradeço aos meus pais eà minha irmã por me apoiarem constantemente, apesar de todas as incertezas. Agradeçoà minha orientadora, professora Fátima, pela companhia, pelos conselhos, pela compreensão e por sempre me incentivar a ir além. Agradeço ao professor Delamaro por sua grande contribuição no desenvolvimento deste trabalho. Resumo RODRIGUES, Davi Silva. TAIGA: uma abordagem para geração de dados de teste por meio de algoritmo genético para programas de processamento de imagens. 2018. 127 f.