Integrating models and simulations of continuous dynamic system behavior into SysML

Contemporary systems engineering problems are becoming increasingly complex as they are handled by geographically distributed design teams, constrained by the objectives of multiple stakeholders, and inundated by large quantities of design information. According to the principles of model-based systems engineering (MBSE), engineers can effectively manage increasing complexity by replacing document-centric design methods with computerized, model-based approaches. In this thesis, modeling constructs from SysML and Modelica are integrated to improve support for MBSE. The Object Management Group has recently developed the Systems Modeling Language (OMG SysML ) to provide a comprehensive set constructs for modeling many common aspects of systems engineering problems (e.g. system requirements, structures, functions). Complementing these SysML constructs, the Modelica language has emerged as a standard for modeling the continuous dynamics (CD) of systems in terms of hybrid discrete- event and differential algebraic equation systems. The integration of SysML and Modelica is explored from three different perspectives: the definition of CD models in SysML; the use of graph transformations to automate the transformation of SysML CD models into Modelica models; and the integration of CD models and other SysML models (e.g. structural, requirements) through the depiction of simulation experiments and engineering analyses. Throughout the thesis, example models of a car suspension and a hydraulically-powered excavator are used for demonstration. The core result of this work is the provision of modeling abilities that do not exist independently in SysML or Modelica. These abilities allow systems engineers to prescribe necessary system analyses and relate them to stakeholder concerns and other system aspects. Moreover, this work provides a basis for model integration which can be generalized and re-specialized for integrating other modeling formalisms into SysML.M.S.Committee Chair: Chris Paredis; Committee Member: Dirk Schaefer; Committee Member: Russell Pea

Configurable Software Performance Completions through Higher-Order Model Transformations

Chillies is a novel approach for variable model transformations closing the gap between abstract architecture models, used for performance prediction, and required low-level details. We enable variability of transformations using chain of generators based on the Higher-Order Transformation (HOT). HOTs target different goals, such as template instantiation or transformation composition. In addition, we discuss state-dependent behavior in prediction models and quality of model transformations

Proceedings of the 2nd Int'l Workshop on Enterprise Modelling and Information Systems Architectures - Concepts and Applications (EMISA'07)

The 2nd International Workshop on “Enterprise Modelling and Information Systems Architectures – Concepts and Applications” (EMISA’07) addresses all aspects relevant for enterprise modelling as well as for designing enterprise architectures in general and information systems architectures in particular. It was jointly organized by the GI Special Interest Group on Modelling Business Information Systems (GI-SIG MoBIS) and the GI Special Interest Group on Design Methods for Information Systems (GI-SIG EMISA). -- These proceedings feature a selection of 15 high quality contributions from academia and practice on enterprise architecture models, business processes management, information systems engineering, and other important issues in enterprise modelling and information systems architectures

A model-based systems engineering methodology to make engineering analysis of discrete-event logistics systems more cost-accessible

This dissertation supports human decision-making with a Model-Based Systems Engineering methodology enabling engineering analysis, and in particular Operations Research analysis of discrete-event logistics systems, to be more widely used in a cost-effective and correct manner. A methodology is a collection of related processes, methods, and tools, and the process of interest is posing a question about a system model and then identifying and building answering analysis models. Methods and tools are the novelty of this dissertation, which when applied to the process will enable the dissertation's goal. One method which directly enables the goal is adding automation to analysis model-building. Another method is abstraction, to make explicit a frequently-used bridge to analysis and also expose analysis model-building repetition to justify automation. A third method is formalization, to capture knowledge for reuse and also enable automation without human interpreters. The methodology, which is itself a contribution, also includes two supporting tool contributions. A tool to support the abstraction method is a definition of a token-flow network, an abstract concept which generalizes many aspects of discrete-event logistics systems and underlies many analyses of them. Another tool to support the formalization method is a definition of a well-formed question, the result of an initial study of semantics, categories, and patterns in questions about models which induce engineering analysis. This is more general than queries about models in any specific modeling language, and also more general than queries answerable by navigating through a model and retrieving recorded information. A final contribution follows from investigating tools for the automation method. Analysis model-building is a model-to-model transformation, and languages and tools for model-to-model transformation already exist in Model-Driven Architecture of software. The contribution considers if and how these tools can be re-purposed by contrasting software object-oriented code generation and engineering analysis model-building. It is argued that both use cases share a common transformation paradigm but executed at different relative levels of abstraction, and the argument is supported by showing how several Operations Research analyses can be defined in an object-oriented way across multiple layered instance-of abstraction levels. Enabling Operations Research analysis of discrete-event logistics systems to be more widely used in a cost-effective and correct manner requires considering fundamental questions about what knowledge is required to answer a question about a system, how to formally capture that knowledge, and what that capture enables. Developments here are promising, but provide only limited answers and leave much room for future work.Ph.D

Quality assurance with dynamic meta modeling

Dynamic Meta Modeling (DMM) ist eine Semantikbeschreibungstechnik, die sich auf MOF-basierte Sprachen fokussiert und deren Verhalten durch graphische, operationale Regeln beschreibt. Der DMM-Ansatz wurde im Jahr 2000 von Engels et al. erstmals beschrieben und von Hausmann in 2006 in seiner Dissertation ausgearbeitet. Der nächste Schritt war nun, an verschiedenen Modellierungssprachen zu erproben, um die gemachten Erfahrungen in die Verbesserung von DMM und seinen Werkzeugen einfließen zu lassen. Das Ergebnis ist die DMM++-Methode, die in dieser Arbeit vorgestellt wird. Wir haben vorwiegend an drei Stellen Verbesserungen vorgenommen: Erstens haben wir basierend auf unseren Erfahrungen mit DMM neue Sprachkonzepte wie die Verfeinerung von Regeln entwickelt, und wir haben bestehende Konzepte wie die Behandlung von universell quantifizierten Strukturen oder Attributen verbessert. Zweitens haben wir einen testgetriebenen Semantikspezifizierungsprozess entwickelt: Zunächst wird eine Menge von Beispielmodellen erzeugt und deren erwartetes Verhalten formalisiert. Die DMM-Regeln werden dann inkrementell entwickelt, wobei geprüft wird, ob die Beispielmodelle tatsächlich das erwartete Verhalten erzeugen. Zudem haben wir Abdeckungskriterien für Tests von DMM-Spezifikationen entwickelt, die die Beurteilung der Qualität der Tests erlauben. Drittens haben wir gezeigt, wie funktionale und nichtfunktionale Anforderungen an Modelle und ihre DMM-Spezifikation formuliert und geprüft werden können. Für ersteres haben wir eine graphische Sprache zur Formulierung temporallogischer Eigenschaften zur Verfügung gestellt, die dann mit Model Checking geprüft werden. Für zweiteres ermöglichen wir dem Modellierer das Hinzufügen von Performanceinformationen zu den Modellen, aufgrund dessen dann z.B. der average throughput eines Modells berechnet werden kann.Dynamic Meta Modeling (DMM) is a semantics specification technique targeted at MOF-based modeling languages, where a language's behavior is defined by means of graphical operational rules which change runtime models. The DMM approach has first been suggested by Engels et al. in 2000; Hausmann has then defined the DMM language on a conceptual level within his PhD thesis in 2006. Consequently, the next step was to bring the existing DMM concepts alive, and then to apply them to different modeling languages, making use of the lessons learned to improve the DMM concepts as well as the DMM tooling. The result of this process is the DMM++ method, which is presented within this thesis. Our contributions are three-fold: First, and according to our experiences with the DMM language, we have introduced new concepts such as refinement by means of rule overriding, and we have strengthened existing concepts such as the dealing with universal quantified structures or attributes. Second, we have developed a test-driven process for semantics specification: A set of test models is created, and their expected behavior is fixed. Then, the DMM rules are created incrementally, finally resulting in a DMM ruleset realizing at least the expected behavior of the test models. Additionally, we have defined a set of coverage criteria for DMM rulesets which allow to measure the quality of a set of test models. Third, we have shown how functional as well as non-functional requirements can be formulated against models and their DMM specifications. The former is achieved by providing a visual language for formulating temporal logic properties, which are then verified with model checking techniques, and by allowing for visual debugging of models failing a requirement. For the latter, the modeler can add performance information to models and analyze their performance properties, e.g. average throughput.Tag der Verteidigung: 04.07.2013Paderborn, Univ., Diss., 201

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 23 full papers, 1 tool paper and 6 testing competition papers presented in this volume were carefully reviewed and selected from 81 submissions. The papers cover topics such as requirements engineering, software architectures, specification, software quality, validation, verification of functional and non-functional properties, model-driven development and model transformation, software processes, security and software evolution

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate