Evaluating Mission-Critical Self-Adaptive Software Systems: A Testing-Based Approach

Self-adaptive software is a closed-loop system that tries to manage, direct, or regulate its own behavior dynamically. Such a system aims at providing an automated and systematic approach to handling the increasing complexity of operation management. Mission-critical systems (e.g., e-business and telecommunication systems) are usually large, complex, and distributed. These systems must preserve their Quality of Service (QoS) at runtime under highly dynamic and non-deterministic conditions; therefore, they are suitable candidates for being equipped with self-adaptive capabilities. Although significant efforts have been devoted to modeling, designing, developing and deploying self-adaptive software since a decade ago, there is still a lack of well-established concrete processes for evaluating such systems. This dissertation proposes a systematic evaluation process for mission-critical self-adaptive software systems. The process is a well-defined testing approach that needs a post-mortem analysis, takes the quantified QoS requirements as inputs, and comprises two main phases: i) conducting system-level testing, and ii) evaluating QoS requirements satisfaction. The process uses Service Level Agreements (SLAs) as quantified QoS requirements, and consequently as the adaptation requirements of mission-critical systems. Adaptation requirements are specific types of requirements used to engineer self-adaptive software. Moreover, for the first phase, the dissertation discusses the uniqueness and necessity of conducting system-level load and stress testing on a self-adaptive software system, for collecting runtime QoS data. In the second phase, the process makes use of utility functions to generate a single value indicating the QoS satisfaction of the evaluated system. The dissertation mainly focuses on evaluating the performance, availability and reliability characteristics of QoS. An open source service-oriented Voice over IP (VoIP) application was selected as a case study. The VoIP application was transformed into a self-adaptive software system with various types of adaptation mechanisms. A set of empirical experiments was performed on the developed self-adaptive VoIP application, and the proposed process was adopted for evaluating the effectiveness of different adaptation mechanisms. To this end, the dissertation defines a sample SLA for the VoIP application, presents a report on the load and stress testing performed on the self-adaptive VoIP application, and presents a set of utility functions for evaluation. The experiments illustrate the validity, reliability, flexibility, and cost of the proposed evaluation process. In sum, this dissertation introduces a novel evaluation process for mission-critical self-adaptive software systems, and shows that the proposed process can help researchers to systematically evaluate their self-adaptive systems

A Framework for Evaluating Model-Driven Self-adaptive Software Systems

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

mRUBiS: An Exemplar for Model-Based Architectural Self-Healing and Self-Optimization

Self-adaptive software systems are often structured into an adaptation engine that manages an adaptable software by operating on a runtime model that represents the architecture of the software (model-based architectural self-adaptation). Despite the popularity of such approaches, existing exemplars provide application programming interfaces but no runtime model to develop adaptation engines. Consequently, there does not exist any exemplar that supports developing, evaluating, and comparing model-based self-adaptation off the shelf. Therefore, we present mRUBiS, an extensible exemplar for model-based architectural self-healing and self-optimization. mRUBiS simulates the adaptable software and therefore provides and maintains an architectural runtime model of the software, which can be directly used by adaptation engines to realize and perform self-adaptation. Particularly, mRUBiS supports injecting issues into the model, which should be handled by self-adaptation, and validating the model to assess the self-adaptation. Finally, mRUBiS allows developers to explore variants of adaptation engines (e.g., event-driven self-adaptation) and to evaluate the effectiveness, efficiency, and scalability of the engines

Robustness-Driven Resilience Evaluation of Self-Adaptive Software Systems

An increasingly important requirement for certain classes of software-intensive systems is the ability to self-adapt their structure and behavior at run-time when reacting to changes that may occur to the system, its environment, or its goals. A major challenge related to self-adaptive software systems is the ability to provide assurances of their resilience when facing changes. Since in these systems, the components that act as controllers of a target system incorporate highly complex software, there is the need to analyze the impact that controller failures might have on the services delivered by the system. In this paper, we present a novel approach for evaluating the resilience of self-adaptive software systems by applying robustness testing techniques to the controller to uncover failures that can affect system resilience. The approach for evaluating resilience, which is based on probabilistic model checking, quantifies the probability of satisfaction of system properties when the target system is subject to controller failures. The feasibility of the proposed approach is evaluated in the context of an industrial middleware system used to monitor and manage highly populated networks of devices, which was implemented using the Rainbow framework for architecture-based self-adaptation

Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations

The large number of possible configurations of modern software-based systems, combined with the large number of possible environmental situations of such systems, prohibits enumerating all adaptation options at design time and necessitates planning at run time to dynamically identify an appropriate configuration for a situation. While numerous planning techniques exist, they typically assume a detailed state-based model of the system and that the situations that warrant adaptations are known. Both of these assumptions can be violated in complex, real-world systems. As a result, adaptation planning must rely on simple models that capture what can be changed (input parameters) and observed in the system and environment (output and context parameters). We therefore propose planning as optimization: the use of optimization strategies to discover optimal system configurations at runtime for each distinct situation that is also dynamically identified at runtime. We apply our approach to CrowdNav, an open-source traffic routing system with the characteristics of a real-world system. We identify situations via clustering and conduct an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav

Layered evaluation of interactive adaptive systems : framework and formative methods

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