Specialized path-based technique to test Internet of Things system functionality under limited network connectivity

Contemporary Internet-of-Things (IoT) systems are hindered by several reliability-related issues, especially, the dynamic behavior of IoT systems caused by limited and often unstable network connectivity. Several intuitive ad-hoc approaches can be employed to test this behavior; however, the effectiveness of these approaches in detecting defects and their overall testing costs remain questionable. Therefore, we present a new specialized path-based technique to test the processes of an IoT system in scenarios wherein parts of these processes are influenced by limited or disrupted network connectivity. The proposed technique can be scaled using two levels of test coverage criteria to determine the strengths of the test cases. For this purpose, we propose two algorithms for generating test cases to implement the technique: an ant colony optimization-based search and a graph-traversal-based test case composition. We compared the efficiency of the proposed approach with possible solutions obtained using a standard path-based testing approach based on prime paths computed by a set-covering algorithm. We consider the total number of test case steps as the main proxy for test effort in experiments employing 150 problem models. For the less intensive of the two used test-coverage criteria, EachBorderOnce, an ant colony optimization-based algorithm, produced test sets with the same averaged number of steps as the graph traversal-based test-case composition; however, this algorithm performed with averaged number of steps 10% lower than a prime paths-based algorithm. For the more intensive test coverage criterion, AllBorderCombinations, these differences favoring the ant colony optimization-based algorithm were 18% and 25%, respectively. For these two types of defined test coverage criteria, the ant colony optimization-based search, graph-traversal-based algorithm, and standard path-based testing approach based on prime paths achieved the best results for 93 and 78, 14 and 24, and 13 and 17 models for AllBorderCombinations and EachBorderOnce criterion, respectively. Therefore, to guarantee the best test set, all compared algorithms are combined in a portfolio strategy that yields the best results based on the potential of the produced test sets to detect simulated defects caused by limited network connectivity. Additionally, this portfolio strategy also yields test sets, implying the lowest test effort for experimental problem instances

PatrIoT : IoT automated interoperability and integration testing framework

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

Overview of medical errors and adverse events

Safety is a global concept that encompasses efficiency, security of care, reactivity of caregivers, and satisfaction of patients and relatives. Patient safety has emerged as a major target for healthcare improvement. Quality assurance is a complex task, and patients in the intensive care unit (ICU) are more likely than other hospitalized patients to experience medical errors, due to the complexity of their conditions, need for urgent interventions, and considerable workload fluctuation. Medication errors are the most common medical errors and can induce adverse events. Two approaches are available for evaluating and improving quality-of-care: the room-for-improvement model, in which problems are identified, plans are made to resolve them, and the results of the plans are measured; and the monitoring model, in which quality indicators are defined as relevant to potential problems and then monitored periodically. Indicators that reflect structures, processes, or outcomes have been developed by medical societies. Surveillance of these indicators is organized at the hospital or national level. Using a combination of methods improves the results. Errors are caused by combinations of human factors and system factors, and information must be obtained on how people make errors in the ICU environment. Preventive strategies are more likely to be effective if they rely on a system-based approach, in which organizational flaws are remedied, rather than a human-based approach of encouraging people not to make errors. The development of a safety culture in the ICU is crucial to effective prevention and should occur before the evaluation of safety programs, which are more likely to be effective when they involve bundles of measures

Methodological challenges in researching activism in action: civil society engagement towards health for all

Civil society engagement around health care and population health improvement is an important driver towards Health for All. Research can improve the effectiveness of health activism by examining the resources, structures and strategies of civil society engagement. However, research to support such engagement faces epistemological and methodological challenges which call for specific research strategies. A four year multi-country study was undertaken by the People’s Health Movement, a global network working for health for all. The research took place in six countries (Brazil, Colombia, DR Congo, India, Italy, South Africa) and globally, and was directed to understanding five domains of civil society engagement: movement building; campaigning and advocacy; capacity building; knowledge generation, access and use; and engaging with governance

Volně použitelný testovací přípravek s injekcí defektů pro vyhodnocování testování

Běžně používaná metoda pro vyhodnocení efektivity testovací techniky je měřit poměr nalezených defektů při použití hodnocených testovacích případů. Do zdrojového kódu testovaného software mohou být injektovány reálné či uměle vytvořené defekty. Pro sofistikovanější vyhodnocování se používá mutační testování, které uměle mění zdrojový kód. Mutační testování nedokáže nasimulovat rozsáhlejší a realistické defekty, vzniklé například špatným pochopením specifikace. Ve článku je představena open-source aplikace sloužící jako testovací přípravek umožňující injekci sofistikovaných defektů. Defekty se nacházejí v předem připravených blocích a výsledná aplikace je z těchto bloků sestavena. Naprostá většina funkcionality a elementů GUI je pokryta automatickými frond-end testy.A natural method to evaluate the effectiveness of a testing technique is to measure the defect detection rate when applying the created test cases. Here, real or artificial software defects can be injected into the source code of software. For a more extensive evaluation, injection of artificial defects is usually needed and can be performed via mutation testing using code mutation operators. However, to simulate complex defects arising from a misunderstanding of design specifications, mutation testing might reach its limit in some cases. In this paper, we present an open-source benchmark testbed application that employs a complement method of artificial defect injection. The application is compiled after artificial defects are injected into its source code from predefined building blocks. The majority of the functions and user interface elements are covered by creating front-end-based automated test cases that can be used in experiments