Strategies and Best Practices for Model-Based Systems Engineering Adoption in Embedded Systems Industry

Amorim, Tiago; Vogelsang, Andreas; Pudlitz, Florian; Gersing, Peter; Philipps, Jan

doi:10.1109/icse-seip.2019.00030

Cited by 19 publications

(41 citation statements)

References 15 publications

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“…Differing Opinions on Reported Best Practices. Furthermore, the validation survey conducted by Amorim et al (2019) shows that even on their final reported list of best practices, there are many differing opinions. In their study, 18 best practices were gathered from interviews, and the results were distributed to a set of engineers experienced in MBSE implementation.…”

Section: Company Location Affects Best Practicesmentioning

confidence: 99%

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Prioritization of Best Practices in the Implementation of Model‐Based Systems Engineering

Ploeg

Lai

Olechowski

2022

INCOSE International Symp

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Model-based systems engineering (MBSE) is a form of systems engineering that is reported to lead to many benefits; yet few companies have successfully implemented MBSE, partly due to a predominance of guidance for adoption that does not recognize differences in organizational context. This paper uses a combination of a literature review and semi-structured interviews to evaluate the hypothesis that the applicability of best practices varies on a case-by-case basis due to factors such as company size, industry, and location. A list of best practices is compiled from existing works and validated with interviews, ultimately resulting in a more complete and accurate list of best practices, along with a recommendation that each company should uniquely assess their circumstances and prioritize this list of best practices accordingly. The findings presented in this paper provide improved guidance towards the use of best practices for successful MBSE implementation.

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Section: Company Location Affects Best Practicesmentioning

confidence: 99%

“…Out of the provided best practices, the only one that was agreed upon universally by all the engineers was "Technically prepared staff should support engineers" (Amorim et al 2019). The three most disagreed upon statements are outlined below with the percentage of respondents who disagreed with each statement.…”

Section: Company Location Affects Best Practicesmentioning

confidence: 99%

Prioritization of Best Practices in the Implementation of Model‐Based Systems Engineering

Ploeg

Lai

Olechowski

2022

INCOSE International Symp

View full text Add to dashboard Cite

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“…Most empirical studies on modeling in practice are case studies analyzing limited sets of models in specific contexts (e.g., [11,26,32,43,64]) or qualitative studies including interviews and surveys (e.g., [1,44,49]). Some studies approach the use of models in FOSS from a quantitative perspective, studying a large number and variety of projects.…”

Section: Relevance Of Open Source Models For Empirical Researchmentioning

confidence: 99%

Characteristics, potentials, and limitations of open-source Simulink projects for empirical research

et al. 2021

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Simulink is an example of a successful application of the paradigm of model-based development into industrial practice. Numerous companies create and maintain Simulink projects for modeling software-intensive embedded systems, aiming at early validation and automated code generation. However, Simulink projects are not as easily available as code-based ones, which profit from large publicly accessible open-source repositories, thus curbing empirical research. In this paper, we investigate a set of 1734 freely available Simulink models from 194 projects and analyze their suitability for empirical research. We analyze the projects considering (1) their development context, (2) their complexity in terms of size and organization within projects, and (3) their evolution over time. Our results show that there are both limitations and potentials for empirical research. On the one hand, some application domains dominate the development context, and there is a large number of models that can be considered toy examples of limited practical relevance. These often stem from an academic context, consist of only a few Simulink blocks, and are no longer (or have never been) under active development or maintenance. On the other hand, we found that a subset of the analyzed models is of considerable size and complexity. There are models comprising several thousands of blocks, some of them highly modularized by hierarchically organized Simulink subsystems. Likewise, some of the models expose an active maintenance span of several years, which indicates that they are used as primary development artifacts throughout a project’s lifecycle. According to a discussion of our results with a domain expert, many models can be considered mature enough for quality analysis purposes, and they expose characteristics that can be considered representative for industry-scale models. Thus, we are confident that a subset of the models is suitable for empirical research. More generally, using a publicly available model corpus or a dedicated subset enables researchers to replicate findings, publish subsequent studies, and use them for validation purposes. We publish our dataset for the sake of replicating our results and fostering future empirical research.

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“…To give a short overview of the state of research on the introduction of the MBSE approach in the industrial environment, some current publications are listed below. Amorim et al have summarized implementation strategies of MBSE for companies based on 10 interviews (Amorim et al 2019). Cameron and Adsit also developed conclusions about the introduction of MBSE in industrial companies from a large dataset (Cameron & Adsit 2018).…”

Section: Model-based Systems Engineeringmentioning

confidence: 99%

“…have summarized implementation strategies of MBSE for companies based on 10 interviews (Amorim et al. 2019). Cameron and Adsit also developed conclusions about the introduction of MBSE in industrial companies from a large dataset (Cameron & Adsit 2018).…”

Section: State Of the Artmentioning

confidence: 99%

Model-based systems engineering in modular design

et al. 2019

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Modular design allows to reduce costs based on scaling effects. However, due to strong alternating effects between the resulting modules and products, methods and tools are required that enable engineers to use specific views in which the respective information can be linked and retrieved according to the situation. Within the scope of this paper, the model-based systems engineering (MBSE) approach is used to model the complex real-world problem of vehicle modular kits. The aim is to investigate the potentials in this context, how modular kits and products can be efficiently modeled and finally how MBSE can support modular design. In order to investigate this in detail, two extensive studies are carried out in a company over a period of three years. The studies show that modular kits lead to an increased complexity of development. Across industries and companies, the demand for reference product models is shown, which facilitate the unification of inhomogeneous partial models and serve as a knowledge repository for the development of future product generations. On this basis, a framework is derived which enables the reuse of large proportions of the product models of previous product generations. This framework is evaluated on the basis of five case studies.

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Strategies and Best Practices for Model-Based Systems Engineering Adoption in Embedded Systems Industry

Cited by 19 publications

References 15 publications

Prioritization of Best Practices in the Implementation of Model‐Based Systems Engineering

Prioritization of Best Practices in the Implementation of Model‐Based Systems Engineering

Characteristics, potentials, and limitations of open-source Simulink projects for empirical research

Model-based systems engineering in modular design

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