A software development framework for context-aware systems

The beginning of the new century has been characterised by the miniaturisation and accessibility of electronics, which has enabled its widespread usage around the world. This technological background is progressively materialising the future of the remainder of the century, where industry-based societies have been moving towards information-based societies. Information from users and their environment is now pervasively available, and many new research areas have born in order to shape the potential of such advancements. Particularly, context-aware computing is at the core of many areas such as Intelligent Environments, Ambient Intelligence, Ambient Assisted Living or Pervasive Computing. Embedding contextual awareness into computers promises a fundamental enhancement in the interaction between computers and humans. While traditional computers require explicit commands in order to operate, contextually aware computers could also use information from the background and the users to provide services according to the situation. But embedding this contextual awareness has many unresolved challenges. The area of context-aware computing has attracted the interest of many researchers that have presented different approaches to solve particular aspects on the implementation of this technology. The great corpus of research in this direction indicates that context-aware systems have different requirements than those of traditional computing. Approaches for developing context-aware systems are typically scattered or do not present compatibility with other approaches. Existing techniques for creating context-aware systems also do not focus on covering all the different stages of a typical software development life-cycle. The contribution of this thesis is towards the foundation layers of a more holistic approach, that tries to facilitate further research on the best techniques for developing these kinds of systems. The approach presents a framework to support the development not only with methodologies, but with open-source tools that facilitate the implementation of context-aware systems in mobile and stationary platforms

Modelling and use of SysML behaviour models for achieving dynamic use cases of technical products in different VR-systems

Digital methods and models help the product designers in performing early evaluations on a product that eventually help to gain understanding about a product’s behaviour and its interactions with neighbouring systems in its later life-phases. Virtual Reality (VR) is a technology that can facilitate the early evaluation process by showing later life situations of a product as early as at the design stage. However, the application of VR in the industry is currently limited due to high model preparation effort and poor reusability of already prepared models. Therefore, this thesis pursues towards the development of a method that can facilitate the early evaluations of the product in VR and thus, facilitate the use of VR in the product development process. This method aims at achieving generic behavioural descriptions for use in VR that can be reused as well to form dynamic use cases of a product in different VR-systems. The focus lies on reducing the overall preparation effort of VR-models and on achieving high reusability of already created models. The core components of the thesis consist of the use of Model Based Systems Engineering (MBSE) to develop generic behavioural model descriptions, their use in building different use cases of a product in one VR-system and their reuse in different VR-systems as well. The Systems Modeling Language (SysML) is used to describe the behavioural models, the modelling process is described systematically and is also summarized in the form of general-purpose guidelines for later use. Furthermore, a dedicated physics engine is used to perform the physical calculations on virtual objects in VR and is integrated with the SysML. These SysML behaviour models together with the physics engine are used to achieve a real-time product use case simulation inside VR. The same SysML behaviour models are used across different VR-systems to achieve real-time simulations and to validate their reuse. Two VR prototypes are developed to demonstrate the effectivity and use of the presented method. Finally, one of the prototypes is put to the empirical evaluation performed with the help of experts from academia as well as the industry.Digitale Methode und Modellen ermöglichen den Produktdesignern eine frühzeitige Evaluierung des Produkts, damit sie das Verhalten des Produkts und seine Interaktionen mit benachbarten Systemen in seinen späteren Lebensphasen besser verstehen können. Virtual Reality (VR) ist eine Technologie, die zum frühen Evaluierungsprozess beitragen kann, indem spätere Lebenssituationen eines Produkts schon in der Entwurfsphase angezeigt werden können. Die Anwendung von VR in der Industrie ist jedoch derzeit aufgrund des hohen Modellaufbereitungsaufwands und der limitierten Wiederverwendbarkeit vorhandener Modelle begrenzt. Daher befasst sich diese Arbeit mit der Entwicklung einer Methode, die die frühzeitige Evaluierung des Produkts innerhalb von VR und die Verwendung von VR im Produktentwicklungsprozess erleichtern kann. Diese Methode befasst sich mit dem Prozess der Entwicklung allgemeiner Verhaltensbeschreibungen zur Verwendung in VR, die auch wiederverwendet werden können, um dynamische Anwendungsfälle eines Produkts in den verschiedenen VR-Systemen abzubilden. Der Fokus liegt auf der Reduzierung des gesamten Aufbereitungsaufwands von VR-Modellen und auf das Verwirklichen einer hohen Wiederverwendbarkeit bereits vorhandener Modelle. Die Kernkomponenten der Arbeit bestehen in der Verwendung von Model Based Systems Engineering (MBSE) zur Entwicklung allgemeingültiger Verhaltensmodellbeschreibungen, ihrer Verwendung beim Erstellen verschiedener Anwendungsfälle eines Produkts in einem VR-System und ihrer Wiederverwendung in den verschiedenen VR-Systemen. Die Systems Modeling Language (SysML) wird zur Beschreibung der Verhaltensmodelle verwendet, der Modellierungsprozess wird systematisch beschrieben und auch in Form allgemeiner Anwendungsrichtlinien für die spätere Verwendung zusammengefasst. Darüber hinaus wird eine dedizierte Physik-Engine verwendet, um die physikalischen Berechnungen für virtuelle Objekte in VR durchzuführen, welche auch mit SysML integriert ist. Diese SysML-Verhaltensmodelle zusammen mit der Physik-Engine bilden eine echtzeitfähige Produktanwendungssimulation in VR. Dieselben SysML-Verhaltensmodelle werden für verschiedene VR-Systeme verwendet, um Echtzeitsimulationen abzubilden und ihre Wiederverwendung zu validieren. Zwei VR-Prototypen wurden entwickelt, um die Wirksamkeit und Verwendung der vorgestellten Methoden zu demonstrieren. Schließlich wurde einer der Prototypen einer empirischen Untersuchung unterzogen, die mithilfe von Experten aus Wissenschaft und Industrie durchgeführt wurde

Ernst Denert Award for Software Engineering 2020

This open access book provides an overview of the dissertations of the eleven nominees for the Ernst Denert Award for Software Engineering in 2020. The prize, kindly sponsored by the Gerlind & Ernst Denert Stiftung, is awarded for excellent work within the discipline of Software Engineering, which includes methods, tools and procedures for better and efficient development of high quality software. An essential requirement for the nominated work is its applicability and usability in industrial practice. The book contains eleven papers that describe the works by Jonathan Brachthäuser (EPFL Lausanne) entitled What You See Is What You Get: Practical Effect Handlers in Capability-Passing Style, Mojdeh Golagha’s (Fortiss, Munich) thesis How to Effectively Reduce Failure Analysis Time?, Nikolay Harutyunyan’s (FAU Erlangen-Nürnberg) work on Open Source Software Governance, Dominic Henze’s (TU Munich) research about Dynamically Scalable Fog Architectures, Anne Hess’s (Fraunhofer IESE, Kaiserslautern) work on Crossing Disciplinary Borders to Improve Requirements Communication, Istvan Koren’s (RWTH Aachen U) thesis DevOpsUse: A Community-Oriented Methodology for Societal Software Engineering, Yannic Noller’s (NU Singapore) work on Hybrid Differential Software Testing, Dominic Steinhofel’s (TU Darmstadt) thesis entitled Ever Change a Running System: Structured Software Reengineering Using Automatically Proven-Correct Transformation Rules, Peter Wägemann’s (FAU Erlangen-Nürnberg) work Static Worst-Case Analyses and Their Validation Techniques for Safety-Critical Systems, Michael von Wenckstern’s (RWTH Aachen U) research on Improving the Model-Based Systems Engineering Process, and Franz Zieris’s (FU Berlin) thesis on Understanding How Pair Programming Actually Works in Industry: Mechanisms, Patterns, and Dynamics – which actually won the award. The chapters describe key findings of the respective works, show their relevance and applicability to practice and industrial software engineering projects, and provide additional information and findings that have only been discovered afterwards, e.g. when applying the results in industry. This way, the book is not only interesting to other researchers, but also to industrial software professionals who would like to learn about the application of state-of-the-art methods in their daily work

Computational Augmentation of Model Based System Engineering: Supporting Mechatronic System Model Development with AI Technologies

Efforts in applying computational support for automatic design synthesis and configuration generation as well as efforts to support descriptive and computational model development for system design and verification has been approached with semantic formalisation of modelling languages and of generic structural and functional concepts using meta-models. Modelling the system using descriptive models helps the designer to explicitly document dependencies between properties and parameters of system and external entities. The descriptive models thus produced often do not consider physics based justification for presence and/or absence of relations. It is often the case, the simulation results obtained at later stages requires changing requirements as well as modifying logical (modelling relations between high level functions parameters/properties and parameters/properties of high level entities) and physical architectures (modelling relations between component’s parameters and properties) to accommodate those requirements. The current MBSE (Model Based System Engineering) tools have capabilities to verify construction of models according to predefined model formats i.e. meta-models. However, these tools and current research in augmenting capabilities of these tools lacks the focus on evaluating content inside the models i.e. whether the system modelled by models represents a system that can be physically realized. This work has tried to avail the potential of available AI (Artificial Intelligence) technologies for assisting modelling activities performed for requirement definition and analysis, architecture design and verification phase of system development process by directing designer to tools that can formalise outputs of model development activities. The proposed problem formulation is based on the insight that a system modelled at both conceptual and detailed design level can be represented by logical and mathematical relations between the properties and parameters of internal and external components or functions of the system and domain. Therefore formulation defines concepts used in requirement, logical architecture and physical architecture models using relation between parameters and properties in those models. Concepts, such as operational requirements (or non-functional requirements for particular use case scenario), are defined through the usage of sets and linking value domains of those sets to particular system application domain for which system model is being developed. These relations enables systematic elaboration of requirements into logical and physical architecture models as well as storage and retrieval of existing model knowledge using existing AI tools. A novel framework has been developed to retrieve existing descriptive structure and function models using logical reasoning as well as to retrieve existing simulation models stored in embedding space of auto-encoder neural network. Beside adopting the concepts of semantic formalisation and meta-model based descriptive knowledge retrieval it utilises novel application of unsupervised representation learning capability of neural network auto-encoders to store known physically and technologically feasible designs in low dimensional representation that cluster similar designs therefore inducing similarity or distance metric that can be used to retrieve the known design with similar behaviour as new required behaviour. Framework also enable application of generic and domain specific logical constraints (as other works has done before) and introduces new concept of system application domain to ensures that at every stage of the model development leading to conceptual physical design architecture stays inside the physical constraints as per system usage domain. The instantiated meta-model elements which are classified to a system application domain (SAD) are implicitly constraint by system usage context constraints (e.g. parameter value restriction), similarly known simulation models can also be categorised to different SADs. The proposed framework extends the conventional approach of automated design synthesis which is only based only on decomposition of high level function (summarizing input to output mapping) into basic functions and selecting components to realize those basic functions. "A system is designed with the aim that it can execute its function(s) as per performance requirements of that function(s) in required operational conditions"- By concentrating on this statement it can be seen that conventional approach of functional decomposition and function allocation to known structural components cannot guarantee to yield a working system in required scenarios by ignoring the dependencies between environment or operating conditions and operating modes of prospective designs satisfying high level function. The results obtained from the implementation of domain specific knowledge representation and retrieval (involving mixture of numerical and logical constraints) as well as the results obtained from implementation of neural network auto-encoder for representation and retrieval of domain specific simulation model demonstrates the viability of these technologies to support the proposed framework

Ernst Denert Award for Software Engineering 2020

This open access book provides an overview of the dissertations of the eleven nominees for the Ernst Denert Award for Software Engineering in 2020. The prize, kindly sponsored by the Gerlind & Ernst Denert Stiftung, is awarded for excellent work within the discipline of Software Engineering, which includes methods, tools and procedures for better and efficient development of high quality software. An essential requirement for the nominated work is its applicability and usability in industrial practice. The book contains eleven papers that describe the works by Jonathan Brachthäuser (EPFL Lausanne) entitled What You See Is What You Get: Practical Effect Handlers in Capability-Passing Style, Mojdeh Golagha’s (Fortiss, Munich) thesis How to Effectively Reduce Failure Analysis Time?, Nikolay Harutyunyan’s (FAU Erlangen-Nürnberg) work on Open Source Software Governance, Dominic Henze’s (TU Munich) research about Dynamically Scalable Fog Architectures, Anne Hess’s (Fraunhofer IESE, Kaiserslautern) work on Crossing Disciplinary Borders to Improve Requirements Communication, Istvan Koren’s (RWTH Aachen U) thesis DevOpsUse: A Community-Oriented Methodology for Societal Software Engineering, Yannic Noller’s (NU Singapore) work on Hybrid Differential Software Testing, Dominic Steinhofel’s (TU Darmstadt) thesis entitled Ever Change a Running System: Structured Software Reengineering Using Automatically Proven-Correct Transformation Rules, Peter Wägemann’s (FAU Erlangen-Nürnberg) work Static Worst-Case Analyses and Their Validation Techniques for Safety-Critical Systems, Michael von Wenckstern’s (RWTH Aachen U) research on Improving the Model-Based Systems Engineering Process, and Franz Zieris’s (FU Berlin) thesis on Understanding How Pair Programming Actually Works in Industry: Mechanisms, Patterns, and Dynamics – which actually won the award. The chapters describe key findings of the respective works, show their relevance and applicability to practice and industrial software engineering projects, and provide additional information and findings that have only been discovered afterwards, e.g. when applying the results in industry. This way, the book is not only interesting to other researchers, but also to industrial software professionals who would like to learn about the application of state-of-the-art methods in their daily work

Collaborative perception architecture in smart cities

Autonomous Driving Systems have become a reality in our society. Everyday, progress is made to increase vehicles' autonomy to drive without restrictions in roads and cities. To achieve that, researchers are always seeking for new methods to ensure the safety of the vehicles. A promising strategy is to improve the quality of the collected perception data as it directly influences the overall performance of the autonomous system. However, despite the advances achieved in detection methods and algorithms, perception is currently physically restricted by the available on-board sensors and their line-of-sight. To overcome this limitation, the autonomous system should not only capture on-board perception data, but also enhance it with data exchanged with other agents in the environment. This is known in research as Collaborative Perception, where mobile and stationary agents share object detection and sensor data inside an Intelligent Transport Systems network. This master's thesis brings together a collection of ETSI standards with the goal of developing a well-defined architecture for future implementation of a Secure Collaborative Perception Network in the context of Smart Cities. The architecture has been designed using the open-source software Capella Arcadia following a Model Based Software Engineering methodology