Towards a method to quantitatively measure toolchain interoperability in the engineering lifecycle: A case study of digital hardware design

The engineering lifecycle of cyber-physical systems is becoming more challenging than ever. Multiple engineering disciplines must be orchestrated to produce both a virtual and physical version of the system. Each engineering discipline makes use of their own methods and tools generating different types of work products that must be consistently linked together and reused throughout the lifecycle. Requirements, logical/descriptive and physical/analytical models, 3D designs, test case descriptions, product lines, ontologies, evidence argumentations, and many other work products are continuously being produced and integrated to implement the technical engineering and technical management processes established in standards such as the ISO/IEC/IEEE 15288:2015 "Systems and software engineering-System life cycle processes". Toolchains are then created as a set of collaborative tools to provide an executable version of the required technical processes. In this engineering environment, there is a need for technical interoperability enabling tools to easily exchange data and invoke operations among them under different protocols, formats, and schemas. However, this automation of tasks and lifecycle processes does not come free of charge. Although enterprise integration patterns, shared and standardized data schemas and business process management tools are being used to implement toolchains, the reality shows that in many cases, the integration of tools within a toolchain is implemented through point-to-point connectors or applying some architectural style such as a communication bus to ease data exchange and to invoke operations. In this context, the ability to measure the current and expected degree of interoperability becomes relevant: 1) to understand the implications of defining a toolchain (need of different protocols, formats, schemas and tool interconnections) and 2) to measure the effort to implement the desired toolchain. To improve the management of the engineering lifecycle, a method is defined: 1) to measure the degree of interoperability within a technical engineering process implemented with a toolchain and 2) to estimate the effort to transition from an existing toolchain to another. A case study in the field of digital hardware design comprising 6 different technical engineering processes and 7 domain engineering tools is conducted to demonstrate and validate the proposed method.The work leading to these results has received funding from the H2020-ECSEL Joint Undertaking (JU) under grant agreement No 826452-“Arrowhead Tools for Engineering of Digitalisation Solutions” and from specific national programs and/or funding authorities. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2023)

EU H2020 MSCA RISE Project FIRST - “virtual Factories: Interoperation suppoRting buSiness innovation”

FIRST – “virtual Factories: Interoperation suppoRting buSiness innovation”, is a European H2020 project, founded by the RESEARCH AND INNOVATION STAFF EXCHANGE (RISE) Work Programme as part of the Marie Skłodowska-Curie actions. The project concerns with Manufacturing 2.0 and aims at providing the new technology and methodology to describe manufacturing assets; to compose and integrate the existing services into collaborative virtual manufacturing processes; and to deal with evolution of changes. This Chapter provides an overview of the state of the art for the research topics related to the project research objectives, and then it presents the progresses the project achieved up to now towards the implementation of the proposed innovations

Conservative and traceable executions of heterogeneous model management workflows

One challenge of developing large scale systems is knowing how artefacts are interrelated across tools and languages, especially when traceability is mandated e.g., by certifying authorities. Another challenge is the interoperability of all required tools to allow the software to be built, tested, and deployed efficiently as it evolves. Build systems have grown in popularity as they facilitate these activities. To cope with the complexities of the development process, engineers can adopt model-driven practices that allow them to raise the system abstraction level by modelling its domain, therefore, reducing the accidental complexity that comes from e.g., writing boilerplate code. However, model-driven practices come with challenges such as integrating heterogeneous model management tasks e.g., validation, and modelling technologies e.g., Simulink (a proprietary modelling environment for dynamic systems). While there are tools that support the execution of model-driven workflows, some support only specific modelling technologies, lack the generation of traceability information, or do not offer the cutting-edge features of build systems like conservative executions i.e., where only tasks affected by changes to resources are executed. In this work we propose ModelFlow, a workflow language and interpreter able to specify and execute model management workflows conservatively and produce traceability information as a side product. In addition, ModelFlow reduces the overhead of model loading and disposal operations by allowing model management tasks to share already loaded models during the workflow execution. Our evaluation shows that ModelFlow can perform conservative executions which can improve the performance times in some scenarios. ModelFlow is designed to support the execution of model management tasks targeting various modelling frameworks and can be used in conjunction with models from heterogeneous technologies. In addition to EMF models, ModelFlow can also handle Simulink models through a driver developed in the context of this thesis which was used to support one case study

Internet of Things Applications - From Research and Innovation to Market Deployment

The book aims to provide a broad overview of various topics of Internet of Things from the research, innovation and development priorities to enabling technologies, nanoelectronics, cyber physical systems, architecture, interoperability and industrial applications. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from technology to international cooperation and the global "state of play".The book builds on the ideas put forward by the European research Cluster on the Internet of Things Strategic Research Agenda and presents global views and state of the art results on the challenges facing the research, development and deployment of IoT at the global level. Internet of Things is creating a revolutionary new paradigm, with opportunities in every industry from Health Care, Pharmaceuticals, Food and Beverage, Agriculture, Computer, Electronics Telecommunications, Automotive, Aeronautics, Transportation Energy and Retail to apply the massive potential of the IoT to achieving real-world solutions. The beneficiaries will include as well semiconductor companies, device and product companies, infrastructure software companies, application software companies, consulting companies, telecommunication and cloud service providers. IoT will create new revenues annually for these stakeholders, and potentially create substantial market share shakeups due to increased technology competition. The IoT will fuel technology innovation by creating the means for machines to communicate many different types of information with one another while contributing in the increased value of information created by the number of interconnections among things and the transformation of the processed information into knowledge shared into the Internet of Everything. The success of IoT depends strongly on enabling technology development, market acceptance and standardization, which provides interoperability, compatibility, reliability, and effective operations on a global scale. The connected devices are part of ecosystems connecting people, processes, data, and things which are communicating in the cloud using the increased storage and computing power and pushing for standardization of communication and metadata. In this context security, privacy, safety, trust have to be address by the product manufacturers through the life cycle of their products from design to the support processes. The IoT developments address the whole IoT spectrum - from devices at the edge to cloud and datacentres on the backend and everything in between, through ecosystems are created by industry, research and application stakeholders that enable real-world use cases to accelerate the Internet of Things and establish open interoperability standards and common architectures for IoT solutions. Enabling technologies such as nanoelectronics, sensors/actuators, cyber-physical systems, intelligent device management, smart gateways, telematics, smart network infrastructure, cloud computing and software technologies will create new products, new services, new interfaces by creating smart environments and smart spaces with applications ranging from Smart Cities, smart transport, buildings, energy, grid, to smart health and life. Technical topics discussed in the book include: • Introduction• Internet of Things Strategic Research and Innovation Agenda• Internet of Things in the industrial context: Time for deployment.• Integration of heterogeneous smart objects, applications and services• Evolution from device to semantic and business interoperability• Software define and virtualization of network resources• Innovation through interoperability and standardisation when everything is connected anytime at anyplace• Dynamic context-aware scalable and trust-based IoT Security, Privacy framework• Federated Cloud service management and the Internet of Things• Internet of Things Application

Towards a Model-Centric Software Testing Life Cycle for Early and Consistent Testing Activities

The constant improvement of the available computing power nowadays enables the accomplishment of more and more complex tasks. The resulting implicit increase in the complexity of hardware and software solutions for realizing the desired functionality requires a constant improvement of the development methods used. On the one hand over the last decades the percentage of agile development practices, as well as testdriven development increases. On the other hand, this trend results in the need to reduce the complexity with suitable methods. At this point, the concept of abstraction comes into play, which manifests itself in model-based approaches such as MDSD or MBT. The thesis is motivated by the fact that the earliest possible detection and elimination of faults has a significant influence on product costs. Therefore, a holistic approach is developed in the context of model-driven development, which allows applying testing already in early phases and especially on the model artifacts, i.e. it provides a shift left of the testing activities. To comprehensively address the complexity problem, a modelcentric software testing life cycle is developed that maps the process steps and artifacts of classical testing to the model-level. Therefore, the conceptual basis is first created by putting the available model artifacts of all domains into context. In particular, structural mappings are specified across the included domain-specific model artifacts to establish a sufficient basis for all the process steps of the life cycle. Besides, a flexible metamodel including operational semantics is developed, which enables experts to carry out an abstract test execution on the modellevel. Based on this, approaches for test case management, automated test case generation, evaluation of test cases, and quality verification of test cases are developed. In the context of test case management, a mechanism is realized that enables the selection, prioritization, and reduction of Test Model artifacts usable for test case generation. I.e. a targeted set of test cases is generated satisfying quality criteria like coverage at the model-level. These quality requirements are accomplished by using a mutation-based analysis of the identified test cases, which builds on the model basis. As the last step of the model-centered software testing life cycle two approaches are presented, allowing an abstract execution of the test cases in the model context through structural analysis and a form of model interpretation concerning data flow information. All the approaches for accomplishing the problem are placed in the context of related work, as well as examined for their feasibility by of a prototypical implementation within the Architecture And Analysis Framework. Subsequently, the described approaches and their concepts are evaluated by qualitative as well as quantitative evaluation. Moreover, case studies show the practical applicability of the approach

Model-Based Engineering of Collaborative Embedded Systems

This Open Access book presents the results of the "Collaborative Embedded Systems" (CrESt) project, aimed at adapting and complementing the methodology underlying modeling techniques developed to cope with the challenges of the dynamic structures of collaborative embedded systems (CESs) based on the SPES development methodology. In order to manage the high complexity of the individual systems and the dynamically formed interaction structures at runtime, advanced and powerful development methods are required that extend the current state of the art in the development of embedded systems and cyber-physical systems. The methodological contributions of the project support the effective and efficient development of CESs in dynamic and uncertain contexts, with special emphasis on the reliability and variability of individual systems and the creation of networks of such systems at runtime. The project was funded by the German Federal Ministry of Education and Research (BMBF), and the case studies are therefore selected from areas that are highly relevant for Germany’s economy (automotive, industrial production, power generation, and robotics). It also supports the digitalization of complex and transformable industrial plants in the context of the German government's "Industry 4.0" initiative, and the project results provide a solid foundation for implementing the German government's high-tech strategy "Innovations for Germany" in the coming years