AdapTV: A Model-Based Test Adaptation Approach for End-to-End User Interface Testing of Smart TVs

We introduce a model-based feedback-driven test adaptation approach for end-to-end user interface testing of smart TVs. From the perspective of the TV software, the proposed approach is a non-intrusive and completely black-box approach, which operates by interpreting the screen images. Given a test suite, which is known to work in an older version of the TV, and a new version of the TV, to which the test suite should be adapted, the proposed approach first automatically discovers user interface models for both the older and the new version of TV by opportunistically crawling the TVs. Then, each test case in the test suite is executed on the old version, and the path traversed by the test case in the respective UI model is found. Finally, a semantically equivalent path in the UI model discovered for the new version of the TV is determined and dynamically executed on the new version in a feedback-driven manner. We empirically evaluate the proposed approach in a setup that closely mimics the industrial setup used by a large consumer electronics company. While the proposed approach successfully adapted all the test cases, the alternative approaches used in the experiments could not adapt any of them

Model-based test adaptation for smart TVs

In this work, we briefly introduce a model-based test adaptation approach for testing smart TVs produced by Arçelik - the fourth largest home appliances manufacturer in Europe operating in 100 different countries under 10 different brand names, including Beko and Grundig. Although our focus is on smart TVs produced by a single company, the proposed approach can readily be applied to any consumer electronics with a screen-based user interface. This is mainly due to the fact that we present a non-intrusive and completely black-box approach that operates by interpreting the images of user interfaces to interact with the system. More specifically, given a test suite, which is known to work on an older version of the system, and a new version of the system, to which the test cases should be adapted, the proposed approach automatically discovers the user interface models of both the older and the new version of the system by systematically crawling the respective user interfaces; figures out the path traversed by a test case in the model discovered from the old system; dynamically (i.e., in a feedback-driven manner) determines the most 'semantically' similar path in the model discovered from the new system; and finally executes the path on the new system. The rationale behind using a modelbased approach is to minimize the guesswork (thus, to improve both the effectiveness and the efficiency of the test adaptation) in the presence of significant changes in the user interfaces, such as the ones affecting the order of the screens/interactions

Clinical and molecular evaluation of MEFV gene variants in the Turkish population: a study by the National Genetics Consortium

Familial Mediterranean fever (FMF) is a monogenic autoinflammatory disorder with recurrent fever, abdominal pain, serositis, articular manifestations, erysipelas-like erythema, and renal complications as its main features. Caused by the mutations in the MEditerranean FeVer (MEFV) gene, it mainly affects people of Mediterranean descent with a higher incidence in the Turkish, Jewish, Arabic, and Armenian populations. As our understanding of FMF improves, it becomes clearer that we are facing with a more complex picture of FMF with respect to its pathogenesis, penetrance, variant type (gain-of-function vs. loss-of-function), and inheritance. In this study, MEFV gene analysis results and clinical findings of 27,504 patients from 35 universities and institutions in Turkey and Northern Cyprus are combined in an effort to provide a better insight into the genotype-phenotype correlation and how a specific variant contributes to certain clinical findings in FMF patients. Our results may help better understand this complex disease and how the genotype may sometimes contribute to phenotype. Unlike many studies in the literature, our study investigated a broader symptomatic spectrum and the relationship between the genotype and phenotype data. In this sense, we aimed to guide all clinicians and academicians who work in this field to better establish a comprehensive data set for the patients. One of the biggest messages of our study is that lack of uniformity in some clinical and demographic data of participants may become an obstacle in approaching FMF patients and understanding this complex disease