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• W2402804972 abstract "Safety-critical Cyber-Physical Systems (CPS) are growing increasingly more distributed, autonomous, and embedded in our society. CPS engineering relies on modeling methods from different fields. Such methods are difficult to combine due to their complexity and heterogeneity. Inconsistencies between models and analyses can lead to implicit design errors, which lead to critical CPS failures. Existing approaches to CPS model integration fall short in terms of their flexibility, effectiveness, and formal guarantees. To overcome these limitations and achieve better integration, I propose an integration approach based on architectural views and analysis contracts. To enable my approach I develop a model-view consistency support framework, an analysis contracts framework, and a verification method for multi-model integration properties. I claim that my approach is feasible, more effective, and more cost-efficient than the existing ones. I plan to validate my claims on realistic industrial academic case studies of CPS modeling. I. PROBLEM: MODELING METHODS INTEGRATION Modern software systems are growing increasingly more distributed, autonomous, and embedded in physical world. Such systems are important in science and technology because they offer socioeconomic benefits beyond classic embedded systems. For instance, self-driving cars promise dramatic reductions in the accident rate [1]. I will call systems with these characteristics Cyber-Physical Systems (CPS) because they are software-controlled and interact with complex physical world, although other names such as autonomous robotics and mechatronics are often used to describe such systems as well. Safety-critical CPS are difficult but important to engineer correctly. To tackle complex analog and digital processes, CPS design and quality assurance rely on model-driven engineering from various engineering fields, such as artificial intelligence, control theory, and mechatronics. This diversity of methods leads to complex and heterogeneous engineering processes that are hard to combine for one system’s design. For example, at least six distinct models of computation may need to co-exist in a single system model [2]. Ad hoc integration between diverse modeling methods may lead to miscommunication and inconsistencies, which turn into design errors and ultimately system failures [3]. I will refer to such critical lack of integration as the Problem of Modeling Methods Integration (MMI). Although partial solutions to the MMI problem exist, CPS community has not yet developed general, effective, and practical ways to integrate CPS modeling and design methods [4]. As a result, safetycritical CPS are prone to implicit errors that take a substantial amount of time, effort, and funds to discover and fix. For example, in the General Motors ignition switch recall case it took years to discover an unexpected interaction between the mechanical and electrical designs of the ignition switch that lead to failures, loss of lives, and expensive recalls [5]. Some aspects of the integration problem have been successfully addressed in related research (see next section for details). However, several important integration issues have not yet been adequately addressed. One of them is the informality of relations between models and their integrationlevel representations (such as views). This relationship may be straightforward to establish and maintain for componentbased models such as Simulink1 and Verilog2. However, some CPS models do not have syntactic support for component, or their components are significantly different from the traditional object-oriented modularization. For example, it is difficult to componentize hybrid programs [6] which formally are sequences of non-deterministic discrete jumps and continuous evolutions. One way to deal with the absence of model structure is to rely on the engineer’s judgment and insight to maintain the relationship to a view. However, this is effortintensive and error-prone. Another aspect of the problem is that system designs undergo constant change. It is increasingly common to use automated tools and algorithms to analyze models and derive their updated versions. I call such tools and algorithms analyses. Analyses are based on theories from specific engineering and scientific domains. For example, in the domain of processor scheduling one finds thread-to-processor allocation via binpacking and processor frequency scaling [7] to derive an optimal architecture of a real-time system. Some analyses change models: frequency scaling adjusts the frequency property of processor components. For such analyses, it is impractical to re-establish consistency after every change: for every change many global properties may need to be re-verified before another change is executed. Besides, analyses often make implicit assumptions about the system or its environment, and it is important to verify these assumptions. 1mathworks.com/products/simulink 2verilog.com Finally, some multi-model consistency properties and analytic assumptions need to be expressed not only in terms of architectural elements (like components and connectors), but also in domain-specific terms that are not defined in the architecture. Often such terms are too semantically lowlevel, and fully defining them in architectural views would be impractical because one would have to “import” the full semantics of the model, thus defeating the purpose of integration abstractions. As the next section describes, current integration approaches lack a way to express model-specific terms without fully bringing the model semantics to the architectural level." @default.
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• W2402804972 date "2015-01-01" @default.
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• W2402804972 title "Towards Integration of Modeling Methods for Cyber-Physical Systems." @default.
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