Cyber-Physical-Human Systems (CPHSs) are the integration, mostly focused on the interactions, of cyber, physical and humans elements that work together towards the achievement of the objectives of the system. Users continuously rely on CPHSs to fulfil personal goals, thus becoming active, relevant, and necessary components of the designed system. The gap between humans and technology is getting smaller. Users are increasingly demanding smarter and personalized applications, capable of understanding and acting upon changing situations. However, humans are highly dynamic, their decisions might not always be predictable, and they expose themselves to unforeseeable situations that might impact their interactions with their physical and cyber elements. The problem addressed in this dissertation is the support of CPHSs' user-centric requirements at runtime. Therefore, this dissertation focuses on the investigation of runtime models and infrastructures for: (1) understanding users, their personal goals and changing situations, (2) causally connecting the cyber, physical and human components involved in the achievement of users' personal goals, and (3) supporting runtime adaptation to respond to relevant changes in the users' situations. Situation-awareness and runtime adaptation pose significant challenges for the engineering of user-centric CPHSs. There are three challenges associated with situation-awareness: first, the complexity and dynamism of users' changing situations require specifications that explicitly connect users with personal goals and relevant context. Second, the achievement of personal goals entails comprehensive representations of user's tasks and sequences and measurable outcomes. Third, situation-awareness implies the analysis of context towards an understanding of users' changing conditions. Therefore, there is a need for representations and reasoning techniques to infer emerging situations. There are three challenges associated with runtime adaptation: first, the dynamic nature of CPHSs and users require runtime models to make explicit the components of CPHSs and their interactions. Second, the definition of architectural and functional requirements of CPHSs to support runtime user-centric awareness and adaptation. Finally, the design and implementation of runtime adaptation techniques to support dynamic changes in the specification of the CPHSs' runtime models. The four contributions of this dissertation add to the body of knowledge for the development of smart applications centred around the achievement of users' personal goals. First, we propose a definition and architectural design for the implementation of user-centric smart cyber-physical-human applications (UCSAs). Our design proposes a context-aware self-adaptive system supported by a runtime infrastructure to manage CRUD operations. Second, we propose two models at runtime (MARTs): (1) our Galapagos Metamodel, which defines the concepts of a UCSA; and (2) our Galapagos Model, which supports the specification of evolving tasking goals, personal interactions, and the relevant contexts. Third, we propose our operational framework, which defines model equivalences between human-readable and machine-readable, available runtime operations and semantics, to manage runtime operations on MARTs. Finally, we propose our processing infrastructure for models at runtime (PRIMOR), which is a component-based system responsible for providing reading access from software components to the MARTs, executing model-related runtime operations, and managing the propagation of changes among interconnected MARTs and their realities. To evaluate our contributions, we conducted a literature review of models and performed a qualitative analysis to demonstrate the novelty of our approach by comparing it with related approaches. We demonstrated that our models satisfy MARTs characteristics, therefore making them proper models at runtime. Furthermore, we performed an experimental analysis based on our case study on online grocery shopping for the elderly. We focused our analysis on the runtime operations specified in the framework as supported by the corresponding MART (accuracy and scalability), and our infrastructure to manage runtime operation and growing MARTs (performance). / Graduate

Software engineers and researchers in the field are constantly developing new technologies to manage the complexity of current software systems. There is an increasing need for mechanisms that can deal with dynamics in the systems' environment, goals, and requirements. Self-adaptive software systems are a solution to manage the complexity caused by dynamics or runtime variations. Software reuse is a classical solution to deal with complexity and increase the quality of a system in a systematic and efficient way. Despite the large amount of research on self-adaptation, no systematic study has been found, which surveys and reports the application of reuse methods and techniques for the development of self-adaptive software systems. A systematic analysis of reuse methods and techniques for the development of self-adaptive systems is interesting as it provides useful insights for researchers and practitioners in the self-adaptive area. This study systematically reviews relevant research work published between the years 2000 and 2020 at eight well-known venues on self-adaptation and software engineering. By following the systematic literature review method, 97 studies were reviewed and 40 primary studies identified for addressing the research questions. The main objectives of the review are 1) to collect and analyse the reuse-based methods studied and applied for the design and development of self-adaptive software systems, 2) analyse the challenges in the application of reuse-based methods for the development of self-adaptive software systems. The review shows that most of the analysed studies support reuse with component-based software engineering. The primary studies propose different reuse-based methods to allow faster and simpler development of self-adaptive systems. Furthermore, the analysis shows that the reviewed studies report several challenges related to the configuration process, design, performance and uncertainty in the application of reuse methods for the development of self-adaptive systems.

Software engineers and researchers in the field are constantly developing new technologies to manage the complexity of current software systems. There is an increasing need for mechanisms that can deal with dynamics in the systems’ environment, goals, and requirements. Self-adaptive software systems are a solution to manage the complexity caused by dynamics or runtime variations. Software reuse is a classical solution to deal with complexity and increase the quality of a system in a systematic and efficient way. Despite the large amount of research on self-adaptation, no systematic study has been found, which surveys and reports the application of reuse methods and techniques for the development of self-adaptive software systems. A systematic analysis of reuse methods and techniques for the development of self-adaptive systems is interesting as it provides useful insights for researchers and practitioners in the self-adaptive area. This study systematically reviews relevant research work published between the years 2000 and 2020 at eight well-known venues on self-adaptation and software engineering. By following the systematic litera-ture review method, 97 studies were reviewed and 40 primary studies identi-fied for addressing the research questions. The main objectives of the review are 1) to collect and analyse the reuse-based methods studied and applied for the design and development of self-adaptive software systems, 2) analyse the challenges in the application of reuse-based methods for the development of self-adaptive software systems. The review shows that most of the analysed studies support reuse with component-based software engineering. The pri-mary studies propose different reuse-based methods to allow faster and sim-pler development of self-adaptive systems. Furthermore, the analysis shows that the reviewed studies report several challenges related to the configura-tion process, design, performance and uncertainty in the application of reuse methods for the development of self-adaptive systems.

Connected aviation, the Internet of Flying Things and related emerging technologies, such as the System-Wide Information Management infrastructure of the FAA NextGen program, present numerous opportunities for the aviation sector. The ubiquity of aeronautical, flight, weather, aerodrome, and maintenance data accelerates the development of smarter software systems to cope with the ever increasing requirements of the industry sector. The increasing amount, frequency and variety of real-time data available to modern air transport and tactical systems, and their crews, creates exciting new challenges and research opportunities. We present an architectural approach toward the vision of increasingly self-separating and self-governed flight operations within the bigger picture of an evolving set of future Autonomous Flight Rules. The challenges in this field of research are manifold and include autonomic airborne trajectory optimization, data sharing, fusion and information derivation, the incorporation of and communication with rational actors—both human and machine—via a connected aviation infrastructure, to facilitate smarter decision making and support while generating economical, environmental and tactical advantages. We developed a concept and prototype implementation of our Smart Autoflight Control System. The concept and implemented system follow the design principle of an Autonomic Element, consisting of an Autonomic Manager and its Managed Element, acting within an Autonomic Context. The Managed Element concept embraces an infrastructure featuring suitable models of manageable environments, airborne agents, planners, applicable operational cost and risk policies, and connections to the System-Wide Information Management cloud as well as to relevant rational actors, such as Air Traffic Control, Command and Control, Operations or Dispatch. The Autonomic Manager concept incorporates the extraction, that is, short-term sensing, of features from operational scenarios and the categorization of these scenarios according to their level of criticality and associated flight phase. The Autonomic Manager component, furthermore, continuously tunes, that is, actuates, manageable items of its Managed Element, such as environments and planners, and triggers competitions to assess their performance under the various extracted and dynamically changing features of their Autonomic Context. The performance reputations of the tuned manageable items are collected in a knowledge base and may serve as a long-term sensor. Both the managed items of the Managed Element as well the managing items of the Autonomic Manager are extendable and may realize very different paradigms, including deterministic, non-deterministic, heuristically guided, and biologically inspired approaches. We assessed the extensibility and maintainability of our Smart Autoflight Control System infrastructure by including manageable environments and planners of the Classical Grid Search, Probabilistic Roadmaps, and Rapidly-Exploring Random Trees families into its core component. Furthermore, we evaluated the viability of a simple heuristic and a more sophisticated Sequential Model-Based Algorithm Configuration Autonomic Manager to adaptively select and tune manageable planners of the supported families based on the extracted features from very simple to highly challenging scenarios. We were able to show that a self-adaptive approach, that heuristically tunes and selects the best performing planner following a performance competition, produces suitable flight trajectories within reasonable deliberation times. Additionally, we discovered options for improving our heuristic Autonomic Manager through a series of evaluation runs of the Sequential Model-Based Algorithm Configuration Autonomic Manager. Our contributions answer how the manageable items, that is, environments and planners, of our Smart Autoflight Control System core component have to be modified in order to embed System-Wide Information Management data that feature both spatial and temporal aspects. We show how operational cost and risk policies help to assess environments differently and plan suitable flight trajectories accordingly. We identify and implement the necessary extensions and capabilities that have to be supported by manageable and managing items, respectively, to enable continuous feature extraction, adaptive tuning, performance competitions, and planner selection in dynamic flight scenarios. / Graduate

Due to increased expectations as well as advances in technology, the interest in Self-adaptive Systems has increased in the last few years. As a consequence of this, thebody of work has also dramatically expanded, and it is essential to have an overviewin order to identify common problems and knowledge gaps. This paper introduces avisualization tool for the state of the art of Self-adaptive Systems. The tool shouldprovide all the needed insights into the body of work in the field of Self-adaptiveSystems.

Self-organizing, multi-agent systems provide a suitable paradigm for agents to manage themselves. We demonstrate a robust, decentralized approach for structural adaptation in explicitly modelled problem-solving agent organizations. Based on self-organization princi- ples, our method enables the agents to modify their structural relations to achieve a better completion rate of tasks in the environment. Reasoning on adaptation is based only on the agent's history of interactions. Agents use the history of tasks assigned to their neighbors and completion rate as a measure of evaluation. This evaluation suggests the most suitable agents for reorganization (Meta-Reasoning). In the rst part of this research we propose Selective-Adaptation method. Our Selective-Adaptation has four different approaches of Meta-Reasoning, which are 1) Fixed Approach, 2) Need-Based Approach, 3) Performance- Based Approach, and 4) Satisfaction-Based Approach along with a Reorganization method, which needs less data but makes better decisions. Interaction between agents is one of the key factors in Multi-Agent societies. Using interaction, agents communicate with each other and cooperatively execute complex tasks which are beyond the capability of a single agent. Cooperatively executing tasks may endanger the success of an agent by selecting poor choices for peers. Therefore, agents need to have a better evaluation mechanism in selecting peers. Trust is one of the measures commonly used to evaluate the effectiveness of agents in cooperative societies. Since all of the interactions are subjected to uncertainty, the risk behavior of agents is considered as a contextual factor in decision making. In the second part of this research we propose the concept of adaptive risk and the use of recommendation-based trust in our adaptive society. We also introduce the agent's strategy and propose an algorithm which helps agents to make decision in an adaptive society using adaptive risk and recommendation-based trust.

The Internet of Things (IoT) is a fast-spreading technology that enables new types of services in several domains, such as transportation, health, and building automation. To exploit the potential of the IoT effectively, several challenges have to be tackled including the following ones. First, the proposed IoT visions provide a fragmented picture, leading to a lack of consensus about IoT systems and their constituents. A second set of challenges concerns the environment of IoT systems that is often dynamic and uncertain, e.g. devices can appear and be discovered at runtime as well as become suddenly unavailable. Additionally, the in- volvement of human users complicates the scene as people’s activities are not always predictable . The majority of existing approaches to en- gineer IoT systems rely on predefined processes to achieve users’ goals. Consequently, such systems have significant shortcomings in coping with dynamic and uncertain environments. To piece together the fragmented picture of IoT systems, we sys- tematically identified their characteristics by analyzing and synthesizing existing taxonomies. To address the challenges related to the IoT envir- onment and the involvement of human users, we used the concept of Emergent Configurations (ECs) to engineer IoT systems. An EC consists of a dynamic set of devices that cooperate temporarily to achieve a user goal. To realize this vision, we proposed novel approaches that enable users to achieve their goals by supporting the automated formation, en- actment, and self-adaptation of IoT systems. / <p>Note: The papers are not included in the fulltext online.</p><p>Paper I in dissertation as manuscript.</p>

This research proposes, implements and analyses a novel framework for multiobjective optimisation through evolutionary computing aimed at, but not restricted to, real-world problems in the engineering design domain. Evolutionary algorithms have been used to tackle a variety of non-linear multiobjective optimisation problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the number of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimising evolutionary algorithm framework, incorporating a genetic algorithm, that uses self-adaptive mutation and crossover in an attempt to avoid such problems, and which has been benchmarked against both standard optimisation test problems in the literature and a real-world airfoil optimisation case. For this last case, the minimisation of drag and maximisation of lift coefficients of a well documented standard airfoil, the framework is integrated with a freeform deformation tool to manage the changes to the section geometry, and XFoil, a tool which evaluates the airfoil in terms of its aerodynamic efficiency. The performance of the framework on this problem is compared with those of two other heuristic MOO algorithms known to perform well, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this framework achieves better or at least no worse convergence. The framework of this research is then considered as a candidate for smart (electricity) grid optimisation. Power networks can be improved in both technical and economical terms by the inclusion of distributed generation which may include renewable energy sources. The essential problem in national power networks is that of power flow and in particular, optimal power flow calculations of alternating (or possibly, direct) current. The aims of this work are to propose and investigate a method to assist in the determination of the composition of optimal or high-performing power networks in terms of the type, number and location of the distributed generators, and to analyse the multi-dimensional results of the evolutionary computation component in order to reveal relationships between the network design vector elements and to identify possible further methods of improving models in future work. The results indicate that the method used is a feasible one for the achievement of these goals, and also for determining optimal flow capacities of transmission lines connecting the bus bars in the network.

Le calcul autonome vise à concevoir des logiciels qui prennent en compte les variations dans leur environnement d'exécution. Les boucles de rétro-action (FCL) fournissent un mécanisme d'auto-adaptation générique, mais leur intégration dans des systèmes logiciels soulève de nombreux défis. Cette thèse s'attaque au défi d'intégration, c.à.d. la composition de l'architecture de connexion reliant le système logiciel adaptable au moteur d'adaptation. Nous proposons pour cela le langage de modélisation spécifique au domaine FCDL. Il élève le niveau d'abstraction des FCLs, permettant l'analyse automatique et la synthèse du code. Ce langage est capable de composition, de distribution et de réflexivité, permettant la coordination de plusieurs boucles de rétro-action distribuées et utilisant des mécanismes de contrôle variés. Son utilisation est facilitée par l'environnement de modélisation ACTRESS qui permet la modélisation, la vérification et la génération du code. La pertinence de notre approche est illustrée à travers trois scénarios d'adaptation réels construits de bout en bout. Nous considérons ensuite la manipulation de modèles comme moyen d'implanter ACTRESS. Nous proposons un Langage Spécifique au Domaine interne qui utilise Scala pour implanter une famille de DSLs. Il permet la vérification de cohérence et les transformations de modèles. Les DSLs résultant ont des propriétés similaires aux approches existantes, mais bénéficient en plus de la souplesse, de la performance et de l'outillage associés à Scala. Nous concluons avec des pistes de recherche découlant de l'application de l'IDM au domaine du calcul autonome. / The vision of Autonomic Computing and Self-Adaptive Software Systems aims at realizing software that autonomously manage itself in presence of varying environmental conditions. Feedback Control Loops (FCL) provide generic mechanisms for self-adaptation, however, incorporating them into software systems raises many challenges. The first part of this thesis addresses the integration challenge, i.e., forming the architecture connection between the underlying adaptable software and the adaptation engine. We propose a domain-specific modeling language, FCDL, for integrating adaptation mechanisms into software systems through external FCLs. It raises the level of abstraction, making FCLs amenable to automated analysis and implementation code synthesis. The language supports composition, distribution and reflection thereby enabling coordination and composition of multiple distributed FCLs. Its use is facilitated by a modeling environment, ACTRESS, that provides support for modeling, verification and complete code generation. The suitability of our approach is illustrated on three real-world adaptation scenarios. The second part of this thesis focuses on model manipulation as the underlying facility for implementing ACTRESS. We propose an internal Domain-Specific Language (DSL) approach whereby Scala is used to implement a family of DSLs, SIGMA, for model consistency checking and model transformations. The DSLs have similar expressiveness and features to existing approaches, while leveraging Scala versatility, performance and tool support. To conclude this thesis we discuss further work and further research directions for MDE applications to self-adaptive software systems.

Les systèmes modernes subissent des pressions pour s'adapter à leur environnement en constante évolution afin de rester utiles. Traditionnellement, cette adaptation a été gérée lors des temps morts du système. il y a une demande croissante d'automatiser ce processus et de le réaliser pendant le fonctionnement du système. Les systèmes auto-adaptatifs ont été introduits en tant que réalisation de systèmes s'adaptant en permanence. Les systèmes auto-adaptatifs peuvent modifier au moment de l'exécution leur comportement et / ou leur structure en fonction de leur perception de l'environnement, du système lui-même et de leurs exigences. L'objectif de ce travail est de réaliser l'auto-configuration, une propriété essentielle et essentielle des systèmes auto-adaptatifs. L'auto-configuration est la capacité de reconfiguration automatique et dynamique en réponse aux changements. Cela peut inclure l’installation, l’intégration, le retrait et la composition / décomposition d’éléments du système.Cette thèse présente le framework Dr-BIP, une extension du framework BIP pour la modélisation de systèmes à configuration automatique qui repose sur une approche basée sur un modèle et basée sur des composants et des connecteurs pour prescrire des systèmes. La combinaison de ces deux approches exploite les avantages de chacune d’elles, faisant de leur combinaison une méthodologie idéale pour la réalisation de systèmes complexes à configuration automatique.Un modèle de système Dr-BIP est un modèle d'exécution qui capture le système en cours d'exécution à trois niveaux d'abstraction différents, à savoir les variantes de comportement, de configuration et de configuration. La configuration du système est capturée par le composant et les connecteurs. Dans un système de composants et de connecteurs, la configuration automatique peut avoir trois niveaux de granularité différents, notamment la possibilité d'ajouter ou de supprimer des connecteurs, d'ajouter ou de supprimer des composants et d'ajouter ou de supprimer des sous-systèmes. Dr-BIP prend en charge l'ajout et le retrait explicites de composants et de sous-systèmes, mais l'ajout et le retrait implicites de connecteurs. Le principal avantage de compter sur une addition et une suppression implicites de connecteurs est la possibilité de garantir, par la construction, des topologies de configuration spécifiques.Pour capturer les trois niveaux d'abstraction, nous introduisons des motifs en tant que structures principales pour prescrire un système Dr-BIP à configuration automatique. Un motif définit un ensemble de composants qui évoluent en fonction de règles d'interaction et de reconfiguration. Un système est composé de plusieurs motifs pouvant éventuellement partager des composants et évoluer ensemble. Les règles d'interaction dictent la manière dont les composants composant le système peuvent interagir, tandis que les règles de reconfiguration dictent l'évolution de la configuration du système. Enfin, nous montrons que le cadre proposé est à la fois minimal et expressif en modélisant quatre systèmes différents à configuration automatique. Enfin, nous proposons un langage de modélisation pour codifier les concepts du cadre et fournir une implémentation d’interprète. / Modern systems are pressured to adapt in response to their constantly changing environment to remain useful. Traditionally, this adaptation has been handled at down times of the system. there is an increased demand to automate this process and achieve it whilst the system is running. Self-adaptive systems were introduced as a realization of continuously adapting systems. Self-adaptive systems are able to modify at runtime their behavior and/or structure in response to their perception of the environment, the system itself, and their requirements. The focus of this work is on realizing self-configuration, a key and essential property of self-adaptive systems. Self-configuration is the capability of reconfiguring automatically and dynamically in response to changes. This may include installing, integrating, removing and composing/decomposing system elements.This thesis introduces the Dr-BIP framework, an extension of the BIP framework for modeling self-configuring systems that relies on a model-based and component & connector approach to prescribe systems. The combination of both of these approaches exploits the benefits of each, making their combination an ideal methodology to realize complex self-configuring systems.A Dr-BIP system model is a runtime model which captures the running system at three different levels of abstraction namely behavior, configuration, and configuration variants. The system's configuration is captured by component and connectors. In a component and connector system, self-configuration can have three different levels of granularity which includes the ability to add or remove connectors, add or remove components, and add or remove subsystems. Dr-BIP supports explicit addition and removal of both components and subsystems, but implicit addition and removal of connectors. The main advantage of relying on an implicit addition and removal of connectors is the ability to guarantee by construction specific configuration topologies.To capture the three levels of abstraction, we introduce motifs as primary structures to prescribe a self-configuring Dr-BIP system. A motif defines a set of components that evolve according to interaction and reconfiguration rules. A system is composed of multiple motifs that possibly share components and evolve together. Interaction rules dictate how components composing the system can interact and reconfiguration rules dictate how the system configuration can evolve over time. Finally, we show that the proposed framework is both minimal and expressive by modeling four different self-configuring systems. Last but not least, we propose a modeling language to codify the framework concepts and provision an interpreter implementation.