Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006. / Schwan, Karsten, Committee Member ; Schimmel, David, Committee Member ; Copeland, John, Committee Member ; Owen, Henry, Committee Chair ; Wills, Linda, Committee Member.

As software evolves and becomes more complex, self-adaptive systems become a moreinteresting solution. Self-adaptive software systems are capable to perform changes inthemselves without human intervention. To make this possible it is necessary toperform a good observation of the system and its environment. This observation is madeby a monitoring system.In this paper, a framework for monitoring OSGi based applications is presented.OSGi is a module system and service platform for Java. This framework offers run-timeinformation about OSGi modules, services and their behavior.The first step is to make a state-of-the-art survey of existing methods to monitor inthe field of self-adaptive systems and OSGi based applications. The survey reviews aset of articles in the area. It is performed to discover what are the common objectivesand problems that any monitoring system faces. After that, the requirements for theframework are stated. These requirements specify the functionality that the frameworkis required to provide, along with the quality attributes that it has to meet. Todemonstrate use of the contributed monitoring framework, we have developed twoexample demonstrators. The objective of these demonstrators is to provide users of theframework with working examples, so that they can use the framework to develop theirown monitoring systems.

In this work, I view incremental experiential learning in intelligent software agents as progressive agent self-adaptation. When an agent produces an incorrect behavior, then it may reflect on, and thus diagnose and repair, the reasoning and knowledge that produced the incorrect behavior. In particular, I focus on the self-diagnosis and self-repair of an agent's domain knowledge. The implementation of systems with the capability to self-diagnose and self-repair involves building both reasoning processes capable of such learning and knowledge representations capable of supporting those reasoning processes. The core issue my dissertation addresses is: what kind of metaknowledge (knowledge about knowledge) may enable the agent to diagnose faults in its domain knowledge? In providing a solution to this issue, the central contribution of this research is a theory of the kind of metaknowledge that enables a system to reason about and adapt its conceptual knowledge. For this purpose, I propose a representation that explicitly encodes metaknowledge in the form of procedures called Empirical Verification Procedures (EVPs). In the proposed knowledge representation, an EVP is associated with each concept within the agent's domain knowledge. Each EVP explicitly semantically grounds the associated concept in the agent's perception, and can thus be used as a test to determine the validity of knowledge of that concept during diagnosis. I present the formal and empirical evaluation of a system, Augur, that makes use of EVP metaknowledge to adapt its own domain knowledge in the context of a particular subclass of classification problem that I call compositional classification, in which the overall classification task can be broken into a hierarchically organized set of subtasks. I hypothesize that EVP metaknowledge will enable a system to automatically adapt its knowledge in two ways: first, by adjusting the ways that inputs are categorized by a concept, in accordance with semantics fixed by an associated EVP; and second, by adjusting the semantics of concepts themselves when they fail to contribute appropriately to system goals. The latter adaptation is realized by altering the EVP associated with the concept in question. I further hypothesize that the semantic grounding of domain concepts in perception through the use of EVPs will increase the generalization power of a learner that operates over those concepts, and thus make learning more efficient. Beyond the support of these hypotheses, I also present results pertinent to the understanding of learning in compositional classification settings using structured knowledge representations.

Thesis (M.S.)--Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.

Modern software systems are increasingly characterized by uncertainties in the operating context and user requirements. These uncertainties are difficult to predict at design time. Achieving the quality goals of such systems depends on the ability of the software to deal with these uncertainties at runtime. A self-adaptive system employs a feedback loop to continuously monitor and adapt itself to achieve particular quality goals (i.e., adaptation goals) regardless of uncertainties. Current research applies formal techniques to provide guarantees for adaptation goals, typically using exhaustive verification techniques. Although these techniques offer strong guarantees for the goals, they suffer from well-known state explosion problem. In this thesis, we take a broader perspective and focus on two types of guarantees: (1) functional correctness of the feedback loop, and (2) guaranteeing the adaptation goals in an efficient manner. To that end, we present ActivFORMS (Active FORmal Models for Self-adaptation), a formally founded model-driven approach for engineering self-adaptive systems with guarantees. ActivFORMS achieves functional correctness by direct execution of formally verified models of the feedback loop using a reusable virtual machine. To efficiently provide guarantees for the adaptation goals with a required level of confidence, ActivFORMS applies statistical model checking at runtime. ActivFORMS supports on the fly changes of adaptation goals and updates of the verified feedback loop models that meet the changed goals. To demonstrate the applicability and effectiveness of the approach, we applied ActivFORMS in several domains: warehouse transportation, oceanic surveillance, tele assistance, and IoT building security monitoring. / Marie Curie CIG, FP7-PEOPLE-2011-CIG, Project ID: 303791

The development of self-adaptive software requires the engineering of an adaptation engine that controls and adapts the underlying adaptable software by means of feedback loops. The adaptation engine often describes the adaptation by using runtime models representing relevant aspects of the adaptable software and particular activities such as analysis and planning that operate on these runtime models. To systematically address the interplay between runtime models and adaptation activities in adaptation engines, runtime megamodels have been proposed for self-adaptive software. A runtime megamodel is a specific runtime model whose elements are runtime models and adaptation activities. Thus, a megamodel captures the interplay between multiple models and between models and activities as well as the activation of the activities. In this article, we go one step further and present a modeling language for ExecUtable RuntimE MegAmodels (EUREMA) that considerably eases the development of adaptation engines by following a model-driven engineering approach. We provide a domain-specific modeling language and a runtime interpreter for adaptation engines, in particular for feedback loops. Megamodels are kept explicit and alive at runtime and by interpreting them, they are directly executed to run feedback loops. Additionally, they can be dynamically adjusted to adapt feedback loops. Thus, EUREMA supports development by making feedback loops, their runtime models, and adaptation activities explicit at a higher level of abstraction. Moreover, it enables complex solutions where multiple feedback loops interact or even operate on top of each other. Finally, it leverages the co-existence of self-adaptation and off-line adaptation for evolution. / Die Entwicklung selbst-adaptiver Software erfordert die Konstruktion einer sogenannten "Adaptation Engine", die mittels Feedbackschleifen die unterliegende Software steuert und anpasst. Die Anpassung selbst wird häufig mittels Laufzeitmodellen, die die laufende Software repräsentieren, und Aktivitäten wie beispielsweise Analyse und Planung, die diese Laufzeitmodelle nutzen, beschrieben. Um das Zusammenspiel zwischen Laufzeitmodellen und Aktivitäten systematisch zu erfassen, wurden Megamodelle zur Laufzeit für selbst-adaptive Software vorgeschlagen. Ein Megamodell zur Laufzeit ist ein spezielles Laufzeitmodell, dessen Elemente Aktivitäten und andere Laufzeitmodelle sind. Folglich erfasst ein Megamodell das Zusammenspiel zwischen verschiedenen Laufzeitmodellen und zwischen Aktivitäten und Laufzeitmodellen als auch die Aktivierung und Ausführung der Aktivitäten. Darauf aufbauend präsentieren wir in diesem Artikel eine Modellierungssprache für ausführbare Megamodelle zur Laufzeit, EUREMA genannt, die aufgrund eines modellgetriebenen Ansatzes die Entwicklung selbst-adaptiver Software erleichtert. Der Ansatz umfasst eine domänen-spezifische Modellierungssprache und einen Laufzeit-Interpreter für Adaptation Engines, insbesondere für Feedbackschleifen. EUREMA Megamodelle werden über die Spezifikationsphase hinaus explizit zur Laufzeit genutzt, um mittels Interpreter Feedbackschleifen direkt auszuführen. Zusätzlich können Megamodelle zur Laufzeit dynamisch geändert werden, um Feedbackschleifen anzupassen. Daher unterstützt EUREMA die Entwicklung selbst-adaptiver Software durch die explizite Spezifikation von Feedbackschleifen, der verwendeten Laufzeitmodelle, und Adaptionsaktivitäten auf einer höheren Abstraktionsebene. Darüber hinaus ermöglicht EUREMA komplexe Lösungskonzepte, die mehrere Feedbackschleifen und deren Interaktion wie auch die hierarchische Komposition von Feedbackschleifen umfassen. Dies unterstützt schließlich das integrierte Zusammenspiel von Selbst-Adaption und Wartung für die Evolution der Software.

Our society is increasingly demanding situation-aware smarter software (SASS) systems, whose goals change over time and depend on context situations. A system with such properties must sense their dynamic environment and respond to changes quickly, accurately, and reliably, that is, to be context-aware and self-adaptive. The problem addressed in this dissertation is the dynamic management of context information, with the goal of improving the relevance of SASS systems' context-aware capabilities with respect to changes in their requirements and execution environment. Therefore, this dissertation focuses on the investigation of dynamic context management and self-adaptivity to: (i) improve context-awareness and exploit context information to enhance quality of user experience in SASS systems, and (ii) improve the dynamic capabilities of self-adaptivity in SASS systems. Context-awareness and self-adaptivity pose signi cant challenges for the engineering of SASS systems. Regarding context-awareness, the rst challenge addressed in this dissertation is the impossibility of fully specifying environmental entities and the corresponding monitoring requirements at design-time. The second challenge arises from the continuous evolution of monitoring requirements due to changes in the system caused by self-adaptation. As a result, context monitoring strategies must be modeled and managed in such a way that they support the addition and deletion of context types and monitoring conditions at runtime. For this, the user must be integrated into the dynamic context management process. Concerning self-adaptivity, the third challenge is to control the dynamicity of adaptation goals, adaptation mechanisms, and monitoring infrastructures, and the way they a ect each other in the adaptation process. This is to preserve the eff ectiveness of context monitoring requirements and thus self-adaptation. The fourth challenge, related also to self-adaptivity,concerns the assessment of adaptation mechanisms at runtime to prevent undesirable system states as a result of self-adaptation. Given these challenges, to improve context-awareness we made three contributions. First, we proposed the personal context sphere concept to empower users to control the life cycle of personal context information in user-centric SASS systems. Second, we proposed the SmarterContext ontology to model context information and its monitoring requirements supporting changes in these models at runtime. Third, we proposed an effi cient context processing engine to discover implicit contextual facts from context information speci fied in changing context models. To improve self-adaptivity we made three contributions. First, we proposed a framework for the identi cation of adaptation properties and goals, which is useful to evaluate self-adaptivity and to derive monitoring requirements mapped to adaptation goals. Second, we proposed a reference model for designing highly dynamic self-adaptive systems, for which the continuous pertinence between monitoring mechanisms and both changing system goals and context situations is a major concern. Third, we proposed a model with explicit validation and veri cation (V&V) tasks for self-adaptive software, where dynamic context monitoring plays a major role. The seventh contribution of this dissertation, the implementation of Smarter-Context infrastructure, addresses both context-awareness and self-adaptivity. To evaluate our contributions, qualitatively and quantitatively, we conducted several comprehensive literature reviews, a case study on user-centric situation-aware online shopping, and a case study on dynamic governance of service-oriented applications. / Graduate