Johan Cederbladh

Systems Engineering (SE) relies on industry–academia collaboration for much of its research. Such collaborations are frequently described valuable in practice. At the same time, there is a need to align between partners to maximize the value of collaboration through acknowledging and working with nuances between parties to share and develop knowledge. At its essence, collaborative efforts might not achieve complete partner alignment to drive value if not emphasized, which could be averted with foresight and planning efforts. In the educational sciences there is a rich history of collaboration, and many models and theories for multi‐stakeholder collaboration have emerged over time. In this work, we investigate the potential carry‐over from educational sciences to collaboration between industry and academia in systems engineering, and discuss how an existing model of activity theory could be used to increase value in collaborative efforts between different partners. We formulate a model of activity theory applicable for systems engineering and present the Frame, Investigate, Negotiate, Do, Understand, Share (FINDUS) process to help practitioners apply the model pragmatically. The FINDUS process is presented through a set of questions that can be used as a checklist during collaboration.

With the current trend in Model-Based Systems Engineering towards Digital Engineering and early Validation & Verification, experiments are increasingly used to estimate system parameters and explore design decisions. Managing such experimental configuration metadata and results is of utmost importance in accelerating overall design effort. In particular, we observe it is important to 'intelligent-ly' reuse experiment-related data to save time and effort by not performing potentially superfluous, time-consuming, and resource-intensive experiments. In this work, we present a framework for managing experiments on digital and/or physical assets with a focus on case-based reasoning with domain knowledge to reuse experimental data efficiently by deciding whether an already-performed experiment (or associated answer) can be reused to answer a new (potentially different) question from the engineer/user without having to set up and perform a new experiment. We provide the general architecture for such an experiment manager and validate our approach using an industrial vehicular energy system-design case study.

At its very core, model‐based systems engineering uses models to enable a multidisciplinary view on a system design in the early stages. These early stage models evolve horizontally : new diagrams for further perspectives and disciplines are added, using the same notation and the same abstraction level. Just as any other process in systems engineering, horizontal model evolution is subject to guidelines and standards, and the multidisciplinary view on a horizontal evolution, involving at least two disciplines, requires referring to multiple guidelines. Despite the significant effort invested in this process, there is no framework or tool support for engineering reviews of horizontal model evolution. In this paper, we aim to support engineering reviews by providing traceability for engineering activities that evolve a model horizontally. Our contribution is a process‐agnostic framework that relies on capturing the intent of model changes in addition to the changes themselves. We group the model changes into transactional units called deltas , which are subsequently annotated with the engineer's intent to perform these specific changes. We give a methodology to integrate such intent‐annotated deltas into engineering reviews and audits, an ontology to capture intent, and a meta‐model for the deltas to achieve a language‐ and guideline‐independent framework. We use an example from the earth moving machinery domain to exemplify the need for horizontal model evolution and provide a prototypical proof‐of‐concept implementation in the SysML Papyrus Plugin for Eclipse and a SysML case study using a machine brake system.

In this article, we discuss how we can facilitate the growing need for early validation and verification (V&V) of system behaviour in Model-Based Systems Engineering (MBSyE). Several aspects, such as reducing cost and time to market, push companies towards integration of V&V methods earlier in development to support effective decision-making. One foundational methodology seeing increased attention in industry is the use of MBSyE, which brings benefits of models with well-defined syntax and semantics to support V&V activities, rather than relying on natural language text documentation. Despite their promise, industrial adoption of these practices is still challenging. This article presents a vision for readily available early V&V . We present a summary of the literature on early V&V in MBSyE and position existing challenges regarding potential solutions and future investigations towards this vision. We elaborate our vision by means of challenges with a specific emphasis on early V&V of system behaviour . We identify three specific challenge areas: Creating and managing Models , Organisational systems engineering aspects, and early V&V Methods . Finally, we outline a road-map to address these categories of challenges, in which we propose the transfer of established best practices from the software engineering domain to support emerging technologies in the systems engineering domain.

Appropriateness of models through modelling languages, tools, and methods is at the core of systems modelling. A concrete example is that useful abstraction depends on the contextual case of modelling purpose and need. Naturally, this results in different modelling formalisms and languages being appropriate at different stages of a common development process and for different stakeholders. In Model-Based Systems Engineering (MBSE) many stakeholders are involved in procedural modelling activities. Consequently, there is a need to identify appropriate modelling approaches at each modelling activity and development stage. Current MBSE adoption and application is still in early stages, and consequently lacking in overall modelling contextualisation. In this paper we discuss what facilitates an appropriate systems engineering model and how practitioners can reason about models in industrial contexts by providing an initial conceptual model of how model artefacts support governing systems engineering concerns.

Simulations have long been part of hardware-centric system domains. Similarly, architecture design is a common practice for complex industrial systems, which comprise many components that can be arranged in different layouts according to given requirements. Configuring simulation models and choosing the architecture design can be time-consuming activities. This paper presents a model-driven approach to automate the simulation configuration and architecture layouting engineering activities by leveraging model-driven optimisation techniques. The approach leverages a research solution, MOMoT (Marrying Optimisation and Model Transformations), an academic tool that combines search-based algorithms and model transformations. MOMoT is extended with two software modules, leveraging the Functional Mock-up Interface standard for simulation configuration and an architectural description language to design architecture layouts. Our solution is presented in the context of Volvo Construction Equipment's industrial use case, which is part of the European-funded project AIDOaRt. Our approach contributes to automated decision support to simulation and architecture design through model-driven optimisation while preserving the organisation's engineering practices.

In this paper we propose an extension to the MagicGrid framework to support virtual prototyping for early system performance Validation & Verification (V&V). Model-Based Systems Engineering (MBSE) is at a maturity where V&V of system performance is expected to be automated for mature analysis. However, current practices do not adequately cover nor describe how this can be enabled in standard MBSE processes using SysML models as the baseline for the system descriptions and knowledge capture. Therefore we propose an extension of the industrially accepted MagicGrid framework to cover virtual V&V in a tool and process agnostic method, supporting practitioners to develop and use models in MBSE for this purpose without a specific vendor or method lock-in. The framework extension is discussed for each new cell in the grid, and we provide guidelines and best practice discussions for how V&V should be enabled for each cell. Specifically, we discuss simulating/analysing SysML directly or through (co-)simulation. Automotive development is used as a running use case.

Systems Engineering (SE) is often visualised through distinct development stages. The first stages started in development relate to the system requirements with several sub-activities related to gathering, defining, and evaluating requirements. A necessity at this stage is the involvement of many heterogeneous stakeholders requiring efficient collaboration and communication to elicit requirements. In SE, models are emerging as a solution for managing complexity through abstraction, but also as a means of finding communication means and collaborative channels across traditional "silos" in development. In this paper we discuss the potential use of models at the early requirements stage in large industrial cyber-physical system development as a crucial enabler for further modelling activities, and how collaboration and participation of multiple internal and external stakeholders can be supported for requirements elicitation. We emphasise the overall modelling needs from the perspective of industrial railway projects, providing context for future research.

In the Systems Engineering (SE) domain there has been a paradigm shift from document-based to model-based system development artefacts; in fact, new methodologies are emerging to meet the increasing complexity of current systems and the corresponding growing need of digital workflows. In this regard, Model-Based Systems Engineering (MBSE) is considered as a key enabler by many central players of the SE community. MBSE has reached an adequate level of maturity, and there exist documented success stories in its adoption in industry. In particular, one significant benefit of utilising MBSE when compared to the traditional manual and document-centric workflows is that models are available from early phases of systems development; these enable a multitude of analyses prior any implementation effort together with other relevant capabilities, like the automation of development tasks. Nonetheless, it is noticeable there is a lack of a common understanding for how formal analyses for the verification and validation (V&V) of systems behaviour, specifically in the early phases of development, could be placed in an MBSE setting. In this article, we report on the planning, execution, and results of a systematic literature review regarding the early V&V of systems behaviour in the context of model-based systems engineering. The review aims to provide a structured representation of the state of the art with respect to motivations, proposed solutions, and limitations. From an initial set of potentially relevant 701 peer-reviewed publications we selected 149 primary studies, which we analysed according to a rigorous data extraction, analysis, and synthesis process. Based on our results, early V&V has usually the goal of checking the quality of a system design to avoid discovering flaws when parts are being concretely realised; SysML is a de facto standard for describing the system under study, while the solutions for the analyses tend to be varied; also V&V analyses tend to target varied properties with a slight predominance of functional concerns, and following the variation mentioned so far the proposed solutions are largely context specific; the proposed approaches are usually presented without explicit limitations, while when limitations are discussed, readiness of the solutions, handling of analyses simplifications/assumptions, and languages/tools integration are among the most frequently mentioned issues. Based on the survey results and the standard SE practices, we discuss how the current state-of-the-art MBSE supports early V&V of systems behaviour with a special focus on industrial adoption and identify relevant challenges to be researched further.