Simulating SysML models: Overview and challenges

Νικολαϊδου, Μάρα; Kapos, George-Dimitrios; Tsadimas, Anargyros; Dalakas, Vassilis; Anagnostopoulos, Dimosthenis

doi:10.1109/sysose.2015.7151961

Cited by 17 publications

(6 citation statements)

References 15 publications

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“…People have to collaborate along the design process and form a social system that associates them as a team and to the overarching organization (Toepfer and Naumann 2017;Zouari 2015). According to the principle of equivalence social complexity rises as a consequence of system complexity (Naumann 2005;Ropohl 2009). Complexity may rise due to the number of individuals and teams, their interactivity and formality, geographic dispersion, cultural differences, as well as human and organizational limitations (Toepfer and Naumann 2017;Törngren and Sellgren 2018;Wang et al 2016).…”

Section: Complexity Viewpointsmentioning

confidence: 99%

Engineering complexity beyond the surface: discerning the viewpoints, the drivers, and the challenges

2023

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Complexity is often regarded as a “problem” to solve. Instead of attempting to solve complexity, we follow systems engineering practices and switch back to the problem domain, where a major obstacle is the impossibility to universally define complexity. As a workaround, we explored complexity characterization and its existing shortcomings, including: lack of standardization, inconsistent semantics, system-centricity, insufficiently transparent reasoning, and lack of validation. To address these shortcomings, we proposed a compilatory framework to characterize complexity using the Five Ws information-gathering method. The answer to the WHO question proposed four complexity viewpoints; the answer to the WHY question proposed a two-dimensional structure for complexity drivers; and the answer to the WHAT question derived generalized complexity challenges. As a preliminary step to show the potential of the framework to characterize complexity, we used and validated it as a tool to structure general literature related to complexity. In general, our findings suggest that papers with complexity solutions do not frame their research within the complexity problem domain, hindering the contribution evaluation. Through the viewpoints, we identified general research gaps of six solution directions. From the drivers, we noted three observations in the discourse of complexity origins: (1) a system-driven tendency, (2) a preference for concreteness vs. abstraction, and (3) an unclear distinction between origins and effects. Through the challenges’ findings we explored two hypotheses: (1) a system-centric preference; and (2) a solution-oriented vision, both of which were supported by the results (most challenges relate to the system viewpoint and challenges are defined based on solution directions).

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Section: Complexity Viewpointsmentioning

confidence: 99%

Engineering complexity beyond the surface: discerning the viewpoints, the drivers, and the challenges

2023

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“…Early SysML integration endeavors focused on establishing links between SysML abstract models on the one hand, and existing domain‐specific executable models on the other hand . More recent works have however strived toward the synthesis of executable domain‐specific simulation code directly from abstract SysML models . These integrations are possible thanks to the use of metamodeling languages enabling the translation of domain‐specific SysML profiles to specific simulation environments (e.g., Modelica, DEVS, etc.).…”

Section: Sysml‐based Mbsd Of Cbpsmentioning

confidence: 99%

“…These tools have allowed, in particular, purely discrete systems such as embedded real‐time applications to be fully conceptualized at the abstract SysML level, to be translated to simulation code, to be formally verified, and eventually, in part, to be synthesized to machine code . A review of the state of the art in executable SysML models is proposed in Nikolaidou et al…”

Section: Sysml‐based Mbsd Of Cbpsmentioning

confidence: 99%

Model‐based systems engineering for life‐sciences instrumentation development

Patou

Maier

Svendsen

et al. 2018

Systems Engineering

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Next-generation genome sequencing machines and Point-of-Care (PoC) in vitro diagnostics devices are precursors of an emerging class of Cyber-Physical Systems (CPS), one that harnesses biomolecular-scale mechanisms to enable novel "wet-technology" applications in medicine, biotechnology, and environmental science. Although many such applications exist, testifying the importance of innovative life-sciences instrumentation, recent events have highlighted the difficulties that designing organizations face in their attempt to guarantee safety, reliability, and performance of this special class of CPS. New regulations and increasing competition pressure innovators to rethink their design and engineering practices, and to better address the above challenges. The pace of innovation will be determined by how organizations manage to ensure the satisfaction of aforementioned constraints while also streamlining product development, maintaining high cost-efficiency and shortening time-to-market. Model-Based Systems Engineering provides a valuable framework for addressing these challenges. In this paper, we demonstrate that existing and readily available model-based development frameworks can be adopted early in the life-sciences instrumentation design process. Such frameworks are specifically helpful in describing and characterizing CPS including elements of a biological nature both at the architectural and performance level. We present the SysML model of a smartphone-based PoC diagnostics system designed for detecting a particular molecular marker. By modeling components and behaviors spanning across the biological, physical-nonbiological, and computational domains, we were able to characterize the important systemic relations involved in the specification of our system's Limit of Detection. Our results illustrate the suitability of such an approach and call for further work toward formalisms enabling the formal verification of systems including biomolecular components. K E Y W O R D Scyber-physical systems, life-sciences, model-based systems engineering, model-based systems design, sensitivity analysis, SysML 98

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“…Depending on the nature and specific characteristics of the systems under study, there is a diversity of ways proposed to simulate SysML models, utilizing different diagrams. The authors have recently presented a comprehensive understanding of the similarities and differences of existing approaches targeting simulation code generation from SysML models in [10] , where current challenges in fully automating simulation of SysML models are also identified. The most well-known of them are briefly discussed in the following.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, SysML system models should be defined, independently of the methods and tools adopted for simulation. Such a task still remains a challenge [10] .…”

Section: Introductionmentioning

confidence: 99%