Path-based testing is an established method for creating test cases comprising sequences of steps executed in a System Under Test (SUT). Several algorithms for generating the test sequences (paths) that satisfy various test coverage criteria determining their properties are published in the literature. However, existing path-based testing techniques have limited applicability in numerous practical cases, such as when executing a particular test step in the test further excludes the execution of another test step. More complex exclusion requirements (further denoted negative constraints) exist in real systems, depending on the number of executions of a particular test step. In the paper, we discuss two possible negative constraints, namely, (1) the complete exclusion of a step as a consequence of the execution of a particular previous step and (2) the requirement to include a particular step maximally once in one test path, when another step was executed previously. We present a novel ant-colony-optimization (ACO) principle-based algorithm, accepting a SUT model based and a set of negative constraints, and computing a set of test paths while maximizing edge coverage and satisfying the given set of negative constraints. We compare the results of the ACO-based algorithm with those returned by a baseline, an alternative algorithm that excludes specific test paths from a set of test paths satisfying edge coverage, and, for reference, with results returned by an algorithm that generates test paths that satisfy edge coverage. Evaluated on 152 problem instances, the presented ACO-based algorithm outperformed the baseline in the average length of test paths (representing the testing costs) lower by 32.62%. Also, the ratio of edge coverage satisfaction in the set of test paths computed by the ACO-based algorithm is better by 3.41% compared to the baseline.

A considerable number of patients with COVID-19 suffer from respiratory problems in the post-acute phase of the disease (the second-third month after disease onset). Individual telerehabilitation and telecoaching are viable, effective options for treating these patients. To treat patients individually, medical staff must have detailed knowledge of their physical activity and condition. A sensor network that utilizes medical-grade devices can be created to collect these data, but the price and availability of these devices might limit such a network's scalability to larger groups of patients. Hence, the use of low-cost commercial fitness wearables is an option worth exploring. This article presents the concept and technical infrastructure of such a telerehabilitation program that started in April 2021 in the Czech Republic. A pilot controlled study with 14 patients with COVID-19 indicated the program's potential to improve patients' physical activity, (85.7% of patients in telerehabilitation versus 41.9% educational group) and exercise tolerance (71.4% of patients in telerehabilitation versus 42.8% of the educational group). Regarding the accuracy of collected data, the used commercial wristband was compared with the medical-grade device in a separate test. Evaluating [Formula: see text]-scores of the intensity of participants' physical activity in this test, the difference in data is not statistically significant at level [Formula: see text]. Hence, the used infrastructure can be considered sufficiently accurate for the telerehabilitation program examined in this study. The technical and medical aspects of the problem are discussed, as well as the technical details of the solution and the lessons learned, regarding using this approach to treat COVID-19 patients in the post-acute phase.

With the rapid growth of the contemporary Internet of Things (IoT) market, the established systems raise a number of concerns regarding the reliability and the potential presence of critical integration defects. In this paper, we present a PatrIoT framework that aims to provide flexible support to construct an effective IoT system testbed to implement automated interoperability and integration testing. The framework allows scaling from a pure physical testbed to a simulated environment using a number of predefined modules and elements to simulate an IoT device or part of the tested infrastructure. PatrIoT also contains a set of reference example testbeds and several sets of example automated tests for a smart street use case.