Model-based testing (MBT) has been extensively researched for software-intensive systems but, despite the academic interest, adoption of the technique in industry has been sparse. This phenomenon has been observed by our industrial partners for MBT with graphical models. They perceive one cause to be a lack of evidence-based MBT guidelines that, in addition to technical guidelines, also take non-technical aspects into account. This hypothesis is supported by a lack of such guidelines in the literature. The objective of this study is to elicit, and synthesize, MBT experts' best practices for MBT with graphical models. The results aim to give guidance to practitioners and aspire to give researchers new insights to inspire future research. An interview survey is conducted using deep, semi-structured, interviews with an international sample of 17 MBT experts, in different roles, from software industry. Interview results are synthesised through semantic equivalence analysis and verified by MBT experts from industrial practice. 13 synthesised conclusions are drawn from which 23 best-practice guidelines are derived for the adoption, use and abandonment of the technique. In addition, observations and expert insights are discussed that help explain the lack of wide-spread adoption of MBT with graphical models in industrial practice. Several technical aspects of MBT are covered by the results as well as conclusions that cover process- and organizational factors. These factors relate to the mindset, knowledge, organization, mandate and resources that enable the technique to be used effectively within an organization. The guidelines presented in this work complement existing knowledge and, as a primary objective, provide guidance for industrial practitioners to better succeed with MBT with graphical models.
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http://dx.doi.org/10.1007/s10664-022-10145-2 | DOI Listing |
Stat Interface
January 2024
Purdue University, West Lafayette, IN 47907, United States of America.
Graphical models have long been studied in statistics as a tool for inferring conditional independence relationships among a large set of random variables. The most existing works in graphical modeling focus on the cases that the data are Gaussian or mixed and the variables are linearly dependent. In this paper, we propose a double regression method for learning graphical models under the high-dimensional nonlinear and non-Gaussian setting, and prove that the proposed method is consistent under mild conditions.
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January 2025
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
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Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, California 91016, United States.
Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially.
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Department of Hematology, Kitasato University School of Medicine, 1-15-1 Kitasato, Minami-Ku, 252-0374, Sagamihara, Kanagawa, Japan.
Mean circulatory filling pressure, venous return curve, and Guyton's graphical analysis are basic concepts in cardiovascular physiology. However, some medical students may not know how to view and interpret or understand them adequately. To deepen students' understanding of the graphical analysis, in place of having to perform live animal experiments, we developed an interactive cardiovascular simulator, as a self-learning tool, as a web application.
View Article and Find Full Text PDFJ Physiol Sci
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Department of Physical Education and Sport Sciences, Faculty of Literature and Human Sciences, Lorestan University, Khoramabad, Iran.
Exercise increases the pain threshold in healthy people. However, the pain threshold modulation effect of exercise and hawthorn is unclear because of its potential benefits in people with persistent pain, including those with Alzheimer's disease. Accordingly, after the induction of Alzheimer's disease by trimethyl chloride, male rats with Alzheimer's disease were subjected to a 12-week training regimen consisting of resistance training, swimming endurance exercises, and combined exercises.
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