Context] Model-based Systems Engineering (MBSE) comprises a set of models and techniques that is often suggested as solution to cope with the challenges of engineering complex systems. Although many practitioners agree with the arguments on the potential benefits of the techniques, companies struggle with the adoption of MBSE. [Goal] In this paper, we investigate the forces that prevent or impede the adoption of MBSE in companies that develop embedded software systems. We contrast the hindering forces with issues and challenges that drive these companies towards introducing MBSE. [Method] Our results are based on 20 interviews with experts from 10 companies. Through exploratory research, we analyze the results by means of thematic coding.[Results] Forces that prevent MBSE adoption mainly relate to immature tooling, uncertainty about the return-on-investment, and fears on migrating existing data and processes. On the other hand, MBSE adoption also has strong drivers and participants have high expectations mainly with respect to managing complexity, adhering to new regulations, and reducing costs. [Conclusions] We conclude that bad experiences and frustration about MBSE adoption originate from false or too high expectations. Nevertheless, companies should not underestimate the necessary efforts for convincing employees and addressing their anxiety.

Model-based Systems Engineering (MBSE) advocates the integrated use of models throughout all development phases of a system development life-cycle. It is also often suggested as a solution to cope with the challenges of engineering complex systems. However, MBSE adoption is no trivial task and companies, especially large ones, struggle to achieve it in a timely and effective way. [Goal] We aim to discover what are the best practices and strategies to implement MBSE in companies that develop embedded software systems. [Method] Using an inductive-deductive research approach, we conducted 14 semi-structured interviews with experts from 10 companies. Further, we analyzed the data and drew some conclusions which were validated by an on-line questionnaire in a triangulation fashion. [Results] Our findings are summarized in an empirically validated list of 18 best practices for MBSE adoption and through a prioritized list of the 5 most important best practices. [Conclusions] Raising engineers' awareness regarding MBSE advantages and acquiring experience through small projects are considered the most important practices to increase the success of MBSE adoption.

Future automotive systems will exhibit increased levels of automation as well as ever tighter integration with other vehicles, traffic infrastructure, and cloud services. From safety perspective, this can be perceived as boon or baneit greatly increases complexity and uncertainty, but at the same time opens up new opportunities for realizing innovative safety functions. Moreover, cyberse curity becomes important as additional concern because attacks are now much more likely and severe. Unfortunately, there is lack of experience with security concerns in context of safety engineering in general and in automotive safety departments in particular. To remediate this problem, we propose a systematic pattern-based approach that interlinks safety and security patterns and provides guidance with respect to selection and combination of both types of patterns in context of system engineering. The application of a combined safety and security pattern engineering workflow is shown and demonstrated by an automotive use case scenario.

Automotive systems will exhibit increased levels of automation as well as ever tighter integration with other vehicles, traffic infrastructure, and cloud services. From safety perspective, this can be perceived as boon or bane-it greatly increases complexity and uncertainty, but at the same time opens up new opportunities for realizing innovative safety functions. Moreover, cybersecurity becomes important as additional concern because attacks are now much more likely and severe. However, there is a lack of experience with security concerns in context of safety engineering in general and in automotive safety departments in particular. To address this problem, we propose a systematic pattern-based approach that interlinks safety and security patterns and provides guidance with respect to selection and combination of both types of patterns in context of system engineering. A combined safety and security pattern engineering workflow is proposed to provide systematic guidance to support non-expert engineers based on best practices. The application of the approach is shown and demonstrated by an automotive case study and different use case scenarios.

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.