Current systems engineering efforts are increasingly driven by trade-offs and limitations imposed by multiple factors: Growing product complexity as well as stricter regulatory requirements in domains such as automotive or aviation necessitate advanced design and development methods. At the core of these influencing factors lies a consideration of competing non-functional concerns, such as safety and reliability, performance, and the fulfillment of quality requirements. In an attempt to cope with these aspects, incremental evolution of model-based engineering practice has produced heterogeneous tool environments without proper integration and exchange of design artifacts. In order to overcome these shortcomings of current engineering practice, we propose a holistic, model-based architecture and analysis framework for seamless design, analysis, and evolution of integrated system models. We describe how heterogeneous domain-specific modeling languages can be embedded into a common general-purpose model in order to facilitate the integration between previously disjoint design artifacts. A case study demonstrates the suitability of this methodology for the design of a safety-critical embedded system, a hypothetical gas heating, with respect to reliability engineering and further quality assurance activities.
Over the last decades, systems immanent complexity has significantly increased. In order to cope with the emerging challenges during the development of such systems, modeling approaches become an indispensable part. While many process steps are applicable to the model-level, there are no sufficient realizations for test execution yet. As a result, we present a semi-formal approach enabling developers to perform abstract test execution straight on the modeled artifacts to support the overarching objective of a shift left of verification and validation tasks. Our concept challenges an abstract test case (derived from test model) against a system model utilizing an integrated set of domain-specific models, i.e. the omni model. Driven by an optimistic dataflow analysis based on a combined view of an abstract test case and its triggered system behavior, possible test verdicts are assigned. Based on a prototypical implementation of the concept, the proof of concept is demonstrated and further on put in the context of related research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.