TOWARDS ADAPTIVE ENTERPRISES USING DIGITAL TWINS

Modern enterprises are large complex systems operating in highly dynamic environments thus requiring quick response to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We present an approach that combines ideas from modeling & simulation, reinforcement learning and control theory to make enterprises adaptive. The approach hinges on the concept of Digital Twin - a set of relevant models that are amenable to analysis and simulation. The paper describes illustration of approach in two real world use cases

An Adaptive Design Methodology for Reduction of Product Development Risk

Embedded systems interaction with environment inherently complicates understanding of requirements and their correct implementation. However, product uncertainty is highest during early stages of development. Design verification is an essential step in the development of any system, especially for Embedded System. This paper introduces a novel adaptive design methodology, which incorporates step-wise prototyping and verification. With each adaptive step product-realization level is enhanced while decreasing the level of product uncertainty, thereby reducing the overall costs. The back-bone of this frame-work is the development of Domain Specific Operational (DOP) Model and the associated Verification Instrumentation for Test and Evaluation, developed based on the DOP model. Together they generate functionally valid test-sequence for carrying out prototype evaluation. With the help of a case study 'Multimode Detection Subsystem' the application of this method is sketched. The design methodologies can be compared by defining and computing a generic performance criterion like Average design-cycle Risk. For the case study, by computing Average design-cycle Risk, it is shown that the adaptive method reduces the product development risk for a small increase in the total design cycle time.Comment: 21 pages, 9 figure

Model-Driven Engineering for Artificial Intelligence - A Systematic Literature Review

Objective: This study aims to investigate the existing body of knowledge in the field of Model-Driven Engineering MDE in support of AI (MDE4AI) to sharpen future research further and define the current state of the art. Method: We conducted a Systemic Literature Review (SLR), collecting papers from five major databases resulting in 703 candidate studies, eventually retaining 15 primary studies. Each primary study will be evaluated and discussed with respect to the adoption of (1) MDE principles and practices and (2) the phases of AI development support aligned with the stages of the CRISP-DM methodology. Results: The study's findings show that the pillar concepts of MDE (metamodel, concrete syntax and model transformation), are leveraged to define domain-specific languages (DSL) explicitly addressing AI concerns. Different MDE technologies are used, leveraging different language workbenches. The most prominent AI-related concerns are training and modeling of the AI algorithm, while minor emphasis is given to the time-consuming preparation of the data sets. Early project phases that support interdisciplinary communication of requirements, such as the CRISP-DM \textit{Business Understanding} phase, are rarely reflected. Conclusion: The study found that the use of MDE for AI is still in its early stages, and there is no single tool or method that is widely used. Additionally, current approaches tend to focus on specific stages of development rather than providing support for the entire development process. As a result, the study suggests several research directions to further improve the use of MDE for AI and to guide future research in this area

HybridMDSD: Multi-Domain Engineering with Model-Driven Software Development using Ontological Foundations

Software development is a complex task. Executable applications comprise a mutlitude of diverse components that are developed with various frameworks, libraries, or communication platforms. The technical complexity in development retains resources, hampers efficient problem solving, and thus increases the overall cost of software production. Another significant challenge in market-driven software engineering is the variety of customer needs. It necessitates a maximum of flexibility in software implementations to facilitate the deployment of different products that are based on one single core. To reduce technical complexity, the paradigm of Model-Driven Software Development (MDSD) facilitates the abstract specification of software based on modeling languages. Corresponding models are used to generate actual programming code without the need for creating manually written, error-prone assets. Modeling languages that are tailored towards a particular domain are called domain-specific languages (DSLs). Domain-specific modeling (DSM) approximates technical solutions with intentional problems and fosters the unfolding of specialized expertise. To cope with feature diversity in applications, the Software Product Line Engineering (SPLE) community provides means for the management of variability in software products, such as feature models and appropriate tools for mapping features to implementation assets. Model-driven development, domain-specific modeling, and the dedicated management of variability in SPLE are vital for the success of software enterprises. Yet, these paradigms exist in isolation and need to be integrated in order to exhaust the advantages of every single approach. In this thesis, we propose a way to do so. We introduce the paradigm of Multi-Domain Engineering (MDE) which means model-driven development with multiple domain-specific languages in variability-intensive scenarios. MDE strongly emphasize the advantages of MDSD with multiple DSLs as a neccessity for efficiency in software development and treats the paradigm of SPLE as indispensable means to achieve a maximum degree of reuse and flexibility. We present HybridMDSD as our solution approach to implement the MDE paradigm. The core idea of HybidMDSD is to capture the semantics of particular DSLs based on properly defined semantics for software models contained in a central upper ontology. Then, the resulting semantic foundation can be used to establish references between arbitrary domain-specific models (DSMs) and sophisticated instance level reasoning ensures integrity and allows to handle partiucular change adaptation scenarios. Moreover, we present an approach to automatically generate composition code that integrates generated assets from separate DSLs. All necessary development tasks are arranged in a comprehensive development process. Finally, we validate the introduced approach with a profound prototypical implementation and an industrial-scale case study.Softwareentwicklung ist komplex: ausführbare Anwendungen beinhalten und vereinen eine Vielzahl an Komponenten, die mit unterschiedlichen Frameworks, Bibliotheken oder Kommunikationsplattformen entwickelt werden. Die technische Komplexität in der Entwicklung bindet Ressourcen, verhindert effiziente Problemlösung und führt zu insgesamt hohen Kosten bei der Produktion von Software. Zusätzliche Herausforderungen entstehen durch die Vielfalt und Unterschiedlichkeit an Kundenwünschen, die der Entwicklung ein hohes Maß an Flexibilität in Software-Implementierungen abverlangen und die Auslieferung verschiedener Produkte auf Grundlage einer Basis-Implementierung nötig machen. Zur Reduktion der technischen Komplexität bietet sich das Paradigma der modellgetriebenen Softwareentwicklung (MDSD) an. Software-Spezifikationen in Form abstrakter Modelle werden hier verwendet um Programmcode zu generieren, was die fehleranfällige, manuelle Programmierung ähnlicher Komponenten überflüssig macht. Modellierungssprachen, die auf eine bestimmte Problemdomäne zugeschnitten sind, nennt man domänenspezifische Sprachen (DSLs). Domänenspezifische Modellierung (DSM) vereint technische Lösungen mit intentionalen Problemen und ermöglicht die Entfaltung spezialisierter Expertise. Um der Funktionsvielfalt in Software Herr zu werden, bietet der Forschungszweig der Softwareproduktlinienentwicklung (SPLE) verschiedene Mittel zur Verwaltung von Variabilität in Software-Produkten an. Hierzu zählen Feature-Modelle sowie passende Werkzeuge, um Features auf Implementierungsbestandteile abzubilden. Modellgetriebene Entwicklung, domänenspezifische Modellierung und eine spezielle Handhabung von Variabilität in Softwareproduktlinien sind von entscheidender Bedeutung für den Erfolg von Softwarefirmen. Zur Zeit bestehen diese Paradigmen losgelöst voneinander und müssen integriert werden, damit die Vorteile jedes einzelnen für die Gesamtheit der Softwareentwicklung entfaltet werden können. In dieser Arbeit wird ein Ansatz vorgestellt, der dies ermöglicht. Es wird das Multi-Domain Engineering Paradigma (MDE) eingeführt, welches die modellgetriebene Softwareentwicklung mit mehreren domänenspezifischen Sprachen in variabilitätszentrierten Szenarien beschreibt. MDE stellt die Vorteile modellgetriebener Entwicklung mit mehreren DSLs als eine Notwendigkeit für Effizienz in der Entwicklung heraus und betrachtet das SPLE-Paradigma als unabdingbares Mittel um ein Maximum an Wiederverwendbarkeit und Flexibilität zu erzielen. In der Arbeit wird ein Ansatz zur Implementierung des MDE-Paradigmas, mit dem Namen HybridMDSD, vorgestellt

Engineering methods and tools for cyber–physical automation systems

Much has been published about potential benefits of the adoption of cyber–physical systems (CPSs) in manufacturing industry. However, less has been said about how such automation systems might be effectively configured and supported through their lifecycles and how application modeling, visualization, and reuse of such systems might be best achieved. It is vitally important to be able to incorporate support for engineering best practice while at the same time exploiting the potential that CPS has to offer in an automation systems setting. This paper considers the industrial context for the engineering of CPS. It reviews engineering approaches that have been proposed or adopted to date including Industry 4.0 and provides examples of engineering methods and tools that are currently available. The paper then focuses on the CPS engineering toolset being developed by the Automation Systems Group (ASG) in the Warwick Manufacturing Group (WMG), University of Warwick, Coventry, U.K. and explains via an industrial case study how such a component-based engineering toolset can support an integrated approach to the virtual and physical engineering of automation systems through their lifecycle via a method that enables multiple vendors' equipment to be effectively integrated and provides support for the specification, validation, and use of such systems across the supply chain, e.g., between end users and system integrators

Development of a network-integrated feature-driven engineering environment

Ph.DDOCTOR OF PHILOSOPH