Towards run-time testing of dynamic adaptive systems

Component-based software engineering has been continuously improved and successfully applied over the past years. Future systems, like ultra-large scale systems, are a vast array of decentralized, distributed, autonomic, heterogeneous, organically grown and continually evolving subsystems respectively components. Components may join or leave these systems during the life cycle of these systems, even at runtime. Despite this enormous complexity, we depend more and more on these dynamic adaptive systems. Hence we have to care about dependability although the systems are evolving at runtime. To achieve dependable dynamic adaptive systems which support hot plug and binding of components during runtime we claim to integrate specific concepts, like runtime testing, into the component infrastructure. In this paper we will show that existing approaches cannot guarantee the correctness of component bindings in dynamic adaptive systems. Therefore we introduce a technique for runtime testing and show how it can be integrated into a component-based approach.

Self-adaptation is emerging as an increasingly important capability for many applications, particularly those deployed in dynamically changing environments, such as ecosystem monitoring and disaster management. One key challenge posed by Dynamically Adaptive Systems (DASs) is the need to handle changes to the requirements and corresponding behavior of a DAS in response to varying environmental conditions. Berry et al. previously identified four levels of RE that should be performed for a DAS. In this paper, we propose the Levels of RE for Modeling that reify the original levels to describe RE modeling work done by DAS developers. Specifically, we identify four types of developers: the system developer, the adaptation scenario developer, the adaptation infrastructure developer, and the DAS research community. Each level corresponds to the work of a different type of developer to construct goal model(s) specifying their requirements. We then leverage the Levels of RE for Modeling to propose two complementary processes for performing RE for a DAS. We describe our experiences with applying this approach to GridStix, an adaptive flood warning system, deployed to monitor the River Ribble in Yorkshire, England.

Runtime testing of context-aware variability in adaptive systems

Dynamically adaptive systems (DAS) such as smart grids, cloud computing applications, sensor networks and P2P networks tend to change their structure at runtime. Therefore, design-time modeling for such systems are sometimes not enough to incorporate self-* properties. To this end, we have developed a dynamic mathematical modeling framework for runtime optimizations for DAS. In this paper, we describe how our system engineers a linear programming model by using a smart-grid application for power distribution as a case-study. At runtime whenever an optimization is desired this modeling framework captures the state of the system, converts it into an appropriate linear programming model, plan the changes using mathematical manipulations and apply the changes to the actual system. Our results show that this framework is able to capture accurate runtime models of large power systems and is able to adapt itself with the change in the size or structure of the system.

Increasingly, high-assurance applications rely on dynamically adaptive systems (DASs) to respond to environmental changes, while satisfying functional requirements and non-functional preferences. Examples include critical infrastructure protection and transportation systems. A DAS comprises a collection of (non-adaptive) target systems (represented as UML models) and a set of adaptations that realize transitions among target systems. Two sources of uncertainty inherent to DASs are: (1) predicting the future execution environment, and (2) using functional and non-functional trade-offs to respond to the changing environment. To address this uncertainty, we are inspired by living organisms that are astonishingly adept at adapting to changing environmental conditions using evolution. In this paper, we describe a digital evolution-based approach to generating models that represent possible target systems suitable for different environmental conditions, enabling the developer to identify the functional and non-functional trade-offs between the models, and then assisting the developer in selecting target systems for the DAS.KeywordsAdaptive SystemBehavioral ModelTarget SystemClass DiagramState DiagramThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

To address the critical industry pain points in streaming data scenarios across industrial production, intelligent transportation and other related fields—namely continuous performance degradation of classification models induced by concept drift, the absence of dedicated closed-loop drift adaptation processing capability in mainstream stream processing systems, poor engineering implementability of specialized tools, and the inherent difficulty in balancing real-time performance and classification accuracy—this paper designs and implements a Dynamic Adaptive Concept Drift Processing System (DACPS) for streaming data classification. The system adopts a hierarchical modular architecture, integrating five core functional modules: streaming data preprocessing, real-time concept drift detection, dynamic sample optimization, incremental model training, and full-process management and control. This architecture enables fully automated and adaptive processing for the end-to-end workflow of streaming data classification and prediction. Extensive multi-dimensional functional tests, specialized performance tests, and scenario-based validation demonstrate that the system can stably adapt to diverse types of concept drift scenarios, and meets all design specifications in terms of classification accuracy, convergence speed, operating efficiency, and resource occupation control. Meanwhile, the system is equipped with an accessible visual interactive interface, making it widely adaptable to the streaming data classification and processing requirements across multiple application domains.

An adaptive dynamic focusing system used for the ultrasonic nondestructive testing of pipeline girth welds is developed. This system relies on ultrasonic linear phased array transducers connected to a multichannel acquisition system, which provides delay and amplitude laws, allowing one to control the ultrasonic beams dynamically. We introduce a testing method for welds and the system’s structure. Optimization of array transducers has also been pursued based on a mathematical model of the acoustic field for a linear phased array. The experimental results have proved that the phased array transducers system has the same capability to detect as a conventional ultrasonic transducers system, but the components of the system can be simplified greatly, and the testing flexibility and the testing speed can be improved greatly. It provides an excellent basis for further research and development of defect inspection of pipeline girth welds.

Ant System (AS) is a novel simulated evolutionary algorithm which shows many good characters, but it has some typical shortcomings, such as high time complexity, stagnation behavior. An Ant Adaptive Dynamic Ant System (ADAS) based on Acceleration is proposed, which is improved from AS by modifying the pheromone updating rule and the transition rule with evenness of solution, interesting and acceleration. Simulation shows that the ADAS can solve the contradictory between convergence speed and stagnation behavior efficiently and has a better solution.

Little has been written in the new product development literature about the simulation technique agent‐based modeling, which is a by‐product of recent explorations into complex adaptive systems in other disciplines. Agent‐based models (ABM) are commonly used in other social sciences to represent individual actors (or groups) in a dynamic adaptive system. The social system may be a marketplace, an organization, or any type of system that acts as a collective of individuals. Agents represent autonomous decision‐making entities that interact with each other and/or with their environment based on a set of rules. These rules dictate the behavioral choices of the agents. In these simulation models, heterogeneous agents interact with each other in a repetitive process. It is from the interactions between agents that aggregate macroscale behaviors or trends emerge. The simulated environment can be thought of as a “virtual” society in which actions taken by one agent may have an effect on the resulting actions of another agent.This article is an introduction to the ABM methodology and its possible uses for innovation and new product development researchers. It explores the benefits and issues with modeling dynamic systems using this methodology. Benefits of ABMs found in sociology and management studies have found that as the heterogeneity of individuals increase in a system or as network effects become more important in a system, the effectiveness of ABMs as a methodology increases. Additionally, the more adaptive a system or the more the system evolves over time, the greater the opportunity to learn more about the adaptive system using ABMs. Limitations to using this methodology include some knowledge of computer‐programming techniques.Three potential areas of research are introduced: diffusion of innovations, organizational strategy, and knowledge and information flows. A common use of ABMs in the extant literature has been the modeling of the diffusion process between networked heterogeneous agents. ABMs easily allow the modeling of different types of networks and the impact of these networks on the diffusion process. A demonstrative example of an agent‐based model to address the research question of how should manufacturers allocate resources to research (exploration) and development (exploitation) projects is provided. Future courses of study using ABMs also are explored.

Cognitive neuroscience approaches the brain as a cognitive system: a system that functionally is conceptualized in terms of information processing. We outline some aspects of this concept and consider a physical system to be an information processing device when a subclass of its physical states can be viewed as representational/cognitive and transitions between these can be conceptualized as a process operating on these states by implementing operations on the corresponding representational structures. We identify a generic and fundamental problem in cognition: sequentially organized structured processing. Structured sequence processing provides the brain, in an essential sense, with its processing logic. In an approach addressing this problem, we illustrate how to integrate levels of analysis within a framework of adaptive dynamical systems. We note that the dynamical system framework lends itself to a description of asynchronous event‐driven devices, which is likely to be important in cognition because the brain appears to be an asynchronous processing system. We use the human language faculty and natural language processing as a concrete example through out.

Components within service-oriented systems require a mechanism to get in touch with offered services. A traditional approach from the field of Web services is a discovery agency provided by the infrastructure, where service providers register and service requestors submit their queries. In the field of dynamic adaptive systems we deal with systems which change their behavior according to the needs of their users during runtime based on context information. Therefore, the wiring of service providers and service requestors - known as the system configuration - may change often. As a consequence of the dynamics it is reasonable not to let the service requestor take care about the service discovery. Instead we propose a component model enabling the proactive configuration of service-oriented systems which will be described in this paper.

Being self-adaptive is a subject which helps software in systems in order to respond to the surrounded changes that are not fully known while designing. Uncertainty over time is a challenge we face with when designing a dynamic adaptive system (DAS). Dynamic adaptive systems keep observing their environment and try to adapt their behavior to the changes made in the environment. Different techniques and strategies are used in requirements and decision-making models in Self-Adaptive Systems (SASs). Goal-achieving strategies have different effects on non-functional requirements (soft goals). The final decision on choosing the strategy is based on the realization time calculation, and on the total of more success possibilities in satisfying the non-functional requirements. In this paper, a new method is presented for decision making, using Constraint Satisfaction Problems (CSP). Evaluation results show the superior performance of the design phase, using the proposed method in performing time and the accuracy of decisions.

The article highlights the concepts of "family" and "business family" in the context of "family business" research as a phenomenon of the global business environment. Applying the macro and micro levels of analysis, the family is regarded as a specific and localized object in space and time. An attempt was made to outline the structure of the family (in a narrow and broad sense), which is based on family and marital relations, as well as to identify the totality of the characteristics that make up its essence.A number of leading overseas scholars seek to single out the business family as the primary unit of study of family business and search for its core characteristics, including: group emotions, moral consciousness, ethical behavior, culture and competencies. At the same time, the family, as part of the family business, has inherent negative phenomena that must be taken into account. These include: nepotism, asymmetric altruism, affluence, paternalism, presidential consolidation (usurpation of power).In the 3-Circle Model, in addition to other functional groups in the family business, we have identified a group of segments, namely 4, 6, 7, which form the functional group "business family" and represent family members who are owners, owners and directors, family employees who do not own property.Defining family business is one of the important issues in family business research. Although many attempts have been made to define, describe, summarize the term "family business", it should be noted that there is still no conventional approach. Family business is interpreted by us as a dynamic adaptive system that self-realizes, is focused on the long-term prospect of development, professes family values, builds a reputation, provides for qualified succession, life-long learning, positions itself as a social institute, seeks to increase professionalism of business and efficiency a business family that controls property and is involved in running a business.

Since software systems need to be continuously available under varying conditions, their ability to evolve at runtime is increasingly seen as one key issue. Modern programming frameworks already provide support for dynamic adaptations. However the high-variability of features in Dynamic Adaptive Systems (DAS) introduces an explosion of possible runtime system configurations (often called modes) and mode transitions. Designing these configurations and their transitions is tedious and error-prone, making the system feature evolution difficult. While Aspect-Oriented Modeling (AOM) was introduced to improve the modularity of software, this paper presents how an AOM approach can be used to tame the combinatorial explosion of DAS modes. Using AOM techniques, we derive a wide range of modes by weaving aspects into an explicit model reflecting the runtime system. We use these generated modes to automatically adapt the system. We validate our approach on an adaptive middleware for home-automation currently deployed in Rennes metropolis.

Indoor multi-sensor fusion positioning based on federated filtering