No / Self-adaptive systems are evolving systems that can adjust their behaviour to accommodate dynamic requirements or to better serve the goal. These systems can vary in their architecture, operation, or adaptive strategies based on the application. Moreover, the evaluation can happen in different ways depending on system architecture and its requirements. Self-adaptive systems can be prone to situations like adaptation faults, inconsistencies in context or low performance on tasks due to their dynamism and complexity. That is why it is important to have reliability assurance of the system to monitor such situations which can compromise the system functionality. In this paper, we provide a brief background on different types of self-adaptive systems and various ways a system can evolve. We discuss the different mechanisms that have been applied in the last two decades for reliability evaluation of such systems and identify challenges and limitations as research opportunities related to the self-adaptive system’s reliability evaluation. / This research was undertaken as a part of the “Model-based Reliability Evaluation for Autonomous Systems with Evolving Architectures” project funded by the University of Bradford under the SURE Grant scheme.

Software-intensive systems are increasingly expected to operate under changing and uncertain conditions, including not only varying user needs and workloads, but also fluctuating resource capacity. Self-adaptation is an approach that aims to address this problem, giving systems the ability to change their behavior and structure to adapt to changes in themselves and their operating environment without human intervention. Self-adaptive systems tend to be reactive and myopic, adapting in response to changes without anticipating what the subsequent adaptation needs will be. Adapting reactively can result in inefficiencies due to the system performing a suboptimal sequence of adaptations. Furthermore, some adaptation tactics—atomic adaptation actions that leave the system in a consistent state—have latency and take some time to produce their effect. In that case, reactive adaptation causes the system to lag behind environment changes. What is worse, a long running adaptation action may prevent the system from performing other adaptations until it completes, further limiting its ability to effectively deal with the environment changes. To address these limitations and improve the effectiveness of self-adaptation, we present proactive latency-aware adaptation, an approach that considers the timing of adaptation (i) leveraging predictions of the near future state of the environment to adapt proactively; (ii) considering the latency of adaptation tactics when deciding how to adapt; and (iii) executing tactics concurrently. We have developed three different solution approaches embodying these principles. One is based on probabilistic model checking, making it inherently able to deal with the stochastic behavior of the environment, and guaranteeing optimal adaptation choices over a finite decision horizon. The second approach uses stochastic dynamic programming to make adaptation decisions, and thanks to performing part of the computations required to make those decisions off-line, it achieves a speedup of an order of magnitude over the first solution approach without compromising optimality. A third solution approach makes adaptation decisions based on repertoires of adaptation strategies— predefined compositions of adaptation tactics. This approach is more scalable than the other two because the solution space is smaller, allowing an adaptive system to reap some of the benefits of proactive latency-aware adaptation even if the number of ways in which it could adapt is too large for the other approaches to consider all these possibilities. We evaluate the approach using two different classes of systems with different adaptation goals, and different repertoires of adaptation strategies. One of them is a web system, with the adaptation goal of utility maximization. The other is a cyberphysical system operating in a hostile environment. In that system, self-adaptation must not only maximize the reward gained, but also keep the probability of surviving a mission above a threshold. In both cases, our results show that proactive latency-aware adaptation improves the effectiveness of self-adaptation with respect to reactive time-agnostic adaptation.

Engineering contemporary distributed software applications is a challenging task due to the dynamic operating conditions in which these systems have to function. Examples are dynamic availability of resources, errors that are difficult to predict, and changing user requirements. These dynamics can affect a number of quality concerns of a system, such as robustness, openness, and performance. The challenges of engineering software systems with such dynamics have motivated the need for self-adaptation. Self-adaptation is based on the principle of separation of concerns, distinguishing two well defined systems: a managed system that deals with domain specific concerns and a managing system that deals with particular quality concerns of the managed system through adaptation with a feedback loop. State of the art in self- adaptation advocates the use of formal methods to specify and verify the system's behavior in order to provide evidence that the system's goals are satisfied. However, little work has been done on the consolidation of design knowledge to model and verify self-adaptation behaviors. To support designers, this thesis contributes with a set of formally specified templates for the specification and verification of self-adaptive behaviors of a family of distributed self-adaptive systems. The templates are based on the MAPE-K reference model (Monitor-Analyze-Plan-Execute plus Knowledge). The templates comprise: (1) behavior specification patterns for modeling the different MAPE components of a feedback loop, and (2) property specification patterns that support verification of the correctness of the adaptation behaviors. The target domain are distributed applications in which self-adaptation is used for managing resources for robustness and openness requirements. The templates are derived from expertise with developing several self-adaptive systems, including a collaborative mobile learning application in which we have applied self-adaptation to make the system robust to degrading GPS accuracy, and a robotic system in which we apply self-adaptation to support different types of openness requirements. We demonstrate the reusability of the templates in a number of case studies. / AMULETS

No description available.

Context is important to today's mobile and ubiquitous systems as operational requirements are only valid under certain context conditions. Detecting context and adapting automatically to that context is a key feature of many of these systems. However, when the operational context associated with a particular requirement changes drastically in a way that designers could not have anticipated, many systems are unable to effectively adapt their operating parameters to continue meeting user needs. Automatically detecting and implementing this system context evolution is highly desirable because it allows for increased uncertainty to be built into the system at design time in order to efficiently and effectively cope with these kinds of drastic changes. This thesis is an empirical investigation and discussion towards integrating data mining algorithms into self-adaptive systems to analyze and de fine new context relevant to specific system requirements when current system context parameters are no longer sufficient. / Graduate / 0984 / arook@uvic.ca

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.