Having a full and healthy life is what most people want. This involves a set of domains such as physical and psychological health, social relationships, and environmental issues. Together, these domains can be used to characterize a person's Quality of Life (QoL). This desire for a healthy life becomes even more prominent as the world population ages. Thereby, new socioeconomic challenges arise, putting pressure on governments and industries to adopt technologies to improve QoL. One of the technologies that have been widely used is the Internet of Health Things (IoHT). However, developers still face challenges when proposing IoHT solutions, such as lack of interoperability, high volatility, difficulties in providing runtime adaptations, high development cost when it involves AI, and the absence of a semantic model for QoL data. Thus, this Ph.D. project aims to investigate the development of QoL-based IoHT systems to address these challenges. As a result, an IoT-Health Platform is expected to support this development and bring health professionals closer to this process.

This systematic mapping aims to identify, summarize and analyze tools that act in the measurement and/or improvement of the Quality of Life (QoL) of adolescents. The literature was mapped using SCOPUS, PubMed and Web of Science. A total of 361 articles were identified and only three fulfilling the eligibility criteria. All the three studies selected present a mobile application that works on the QoL for adolescents. Only two of them are available in app stores (Android and iOS) for free. No application correlates QoL with the academic index of the user and only one of them allows the data extraction.

The most common way of performing regression testing is by executing all test cases associated with a software system. However, this approach is not scalable since time and cost to execute the test cases increase together with the system’s size. A way to address this consists of prioritizing the existing test cases, aiming to maximize a test suite’s fault detection rate. To address the limitations of existing approaches, in this paper we propose a new approach to maximize the rate of fault detection of test suites. Our proposal has three steps: i) infer code components’ criticality values using a fuzzy inference system; ii) calculate test cases’ criticality; iii) prioritize the test cases using ant colony optimization. The test cases are prioritized considering criticality, execution time and history of faults, and the resulting test suites are evaluated according to their fault detection rate. The evaluation was performed in eight programs, and the results show that the fault detection rate of the solutions was higher than in the non-ordered test suites and ones obtained using a greedy approach, reaching the optimal value when possible to verify. A sanity check was performed, comparing the obtained results to the results of a random search. The approach performed better at significant levels of statistic and practical difference, evidencing its true applicability to the prioritization of test cases.

Software testing consists in the dynamic verification of the behavior of a program on a set of test cases. When a program is modified, it must be tested to verify if the changes did not imply undesirable effects on its functionality. The rerunning of all test cases can be impossible, due to cost, time and resource constraints. So, it is required the creation of a test cases subset before the test execution. This is a hard problem and the use of standard Software Engineering techniques could not be suitable. This work presents an approach for test case prioritization and selection, based in relevant inputs obtained from a software development environment. The approach uses Software Quality Function Deployment (SQFD) to deploy the features relevance among the system components, Mamdani fuzzy inference systems to infer the criticality of each class and Ant Colony Optimization to select test cases. An evaluation of the approach is presented, using data from simulations with different number of tests.

Computational Intelligence (CI) embraces techniques designed to address complex real-world problems in which traditional approaches are ineffective or infeasible. Some of these techniques are being used to solve several complex problems, such as the team allocation, building products portfolios in a software product line and test case selection/prioritization. However, despite the usefulness of these applications, the development of solutions based in CI techniques is not a trivial activity, since it involves the implementation/adaptation of algorithms to specific context and problems. This work presents Athena, a visual tool developed aiming at offering a simple approach to develop CI-based software systems. In order to do this, we proposed a drag-and-drop approach, which we called CI as a Service (CIaaS). Based on a preliminary study, we can state that Athena can help researchers to save time during the development of computational intelligence approaches.