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Howie Shrobe – One of the best experts on this subject based on the ideXlab platform.

  • IWSAS – Probabilistic dispatch, dynamic domain architecture, and self-Adaptive Software
    Self-Adaptive Software: Applications, 2003
    Co-Authors: Robert Laddaga, Paul Robertson, Howie Shrobe

    Abstract:

    In this paper we report on a beginning effort in the self Adaptive Software research area of improving function or method dispatch. We extend type-signature based method dispatch in a dynamic object oriented programming language with probabilistic dispatch, where the choice of method to use is determined by statistical means. This research direction is part of a larger self Adaptive Software effort at the MIT Artificial Intelligence Laboratory, called Dynamic Domain Architectures.

  • IWSAS – Introduction to self-Adaptive Software: applications
    Self-Adaptive Software: Applications, 2003
    Co-Authors: Robert Laddaga, Paul Robertson, Howie Shrobe

    Abstract:

    The second International Workshop on Self Adaptive Software was held on scenic Lake Balaton, in Hungary during May 17- 19, 2001. The workshop was sponsored by the Technical University of Budapest, and organized by Gabor Peceli, Head of the Department of Measurement and Information Systems, assisted by Simon Gyula, Senior Lecturer in the department. This book presents the collection of papers delivered at this workshop, along with some related papers, and reports of the workshop activities.

  • Probabilistic dispatch, dynamic domain architecture, and self-Adaptive Software
    Lecture Notes in Computer Science, 2003
    Co-Authors: Robert Laddaga, Paul Robertson, Howie Shrobe

    Abstract:

    In this paper we report on a beginning effort in the self Adaptive Software research area of improving function or method dispatch. We extend type-signature based method dispatch in a dynamic object oriented programming language with probabilistic dispatch, where the choice of method to use is determined by statistical means. This research direction is part of a larger self Adaptive Software effort at the MIT Artificial Intelligence Laboratory, called Dynamic Domain Architectures.

Robert Laddaga – One of the best experts on this subject based on the ideXlab platform.

  • Self Adaptive Software Problems and Projects
    2006 Second International IEEE Workshop on Software Evolvability (SE'06), 2006
    Co-Authors: Robert Laddaga

    Abstract:

    Self Adaptive Software is a relatively new approach to dynamic self management of Software based systems. This paper discusses the concepts, history and applications of Self Adaptive Software over the past ten years. It also presents a set of crucial research problems for the next ten years of research in this area.

  • IWSAS – Probabilistic dispatch, dynamic domain architecture, and self-Adaptive Software
    Self-Adaptive Software: Applications, 2003
    Co-Authors: Robert Laddaga, Paul Robertson, Howie Shrobe

    Abstract:

    In this paper we report on a beginning effort in the self Adaptive Software research area of improving function or method dispatch. We extend type-signature based method dispatch in a dynamic object oriented programming language with probabilistic dispatch, where the choice of method to use is determined by statistical means. This research direction is part of a larger self Adaptive Software effort at the MIT Artificial Intelligence Laboratory, called Dynamic Domain Architectures.

  • IWSAS – Introduction to self-Adaptive Software: applications
    Self-Adaptive Software: Applications, 2003
    Co-Authors: Robert Laddaga, Paul Robertson, Howie Shrobe

    Abstract:

    The second International Workshop on Self Adaptive Software was held on scenic Lake Balaton, in Hungary during May 17- 19, 2001. The workshop was sponsored by the Technical University of Budapest, and organized by Gabor Peceli, Head of the Department of Measurement and Information Systems, assisted by Simon Gyula, Senior Lecturer in the department. This book presents the collection of papers delivered at this workshop, along with some related papers, and reports of the workshop activities.

Sam Malek – One of the best experts on this subject based on the ideXlab platform.

  • uncertainty in self Adaptive Software systems
    Lecture Notes in Computer Science, 2013
    Co-Authors: Naeem Esfahani, Sam Malek

    Abstract:

    The ever-growing complexity of Software systems coupled with their stringent availability requirements are challenging the manual management of Software after its deployment. This has motivated the development of self-Adaptive Software systems. Self-adaptation endows a Software system with the ability to satisfy certain objectives by automatically modifying its behavior at runtime. While many promising approaches for the construction of self-Adaptive Software systems have been developed, the majority of them ignore the uncertainty underlying the adaptation. This has been one of the key inhibitors to widespread adoption of self-adaption techniques in risk-averse real-world applications. Uncertainty in this setting is a vaguely understood term. In this paper, we characterize the sources of uncertainty in self-Adaptive Software system, and demonstrate its impact on the system’s ability to satisfy its objectives. We then provide an alternative notion of optimality that explicitly incorporates the uncertainty underlying the knowledge (models) used for decision making. We discuss the state-of-the-art for dealing with uncertainty in this setting, and conclude with a set of challenges, which provide a road map for future research.

  • Software Engineering for Self-Adaptive Systems – Uncertainty in Self-Adaptive Software Systems
    Software Engineering for Self-Adaptive Systems II, 2013
    Co-Authors: Naeem Esfahani, Sam Malek

    Abstract:

    The ever-growing complexity of Software systems coupled with their stringent availability requirements are challenging the manual management of Software after its deployment. This has motivated the development of self-Adaptive Software systems. Self-adaptation endows a Software system with the ability to satisfy certain objectives by automatically modifying its behavior at runtime. While many promising approaches for the construction of self-Adaptive Software systems have been developed, the majority of them ignore the uncertainty underlying the adaptation. This has been one of the key inhibitors to widespread adoption of self-adaption techniques in risk-averse real-world applications. Uncertainty in this setting is a vaguely understood term. In this paper, we characterize the sources of uncertainty in self-Adaptive Software system, and demonstrate its impact on the system’s ability to satisfy its objectives. We then provide an alternative notion of optimality that explicitly incorporates the uncertainty underlying the knowledge (models) used for decision making. We discuss the state-of-the-art for dealing with uncertainty in this setting, and conclude with a set of challenges, which provide a road map for future research.

  • A Learning-Based Framework for Engineering Feature-Oriented Self-Adaptive Software Systems
    IEEE Transactions on Software Engineering, 2013
    Co-Authors: Naeem Esfahani, Ahmed Elkhodary, Sam Malek

    Abstract:

    Self-Adaptive Software systems are capable of adjusting their behavior at runtime to achieve certain functional or quality-of-service goals. Often a representation that reflects the internal structure of the managed system is used to reason about its characteristics and make the appropriate adaptation decisions. However, runtime conditions can radically change the internal structure in ways that were not accounted for during their design. As a result, unanticipated changes at runtime that violate the assumptions made about the internal structure of the system could degrade the accuracy of the adaptation decisions. We present an approach for engineering self-Adaptive Software systems that brings about two innovations: 1) a feature-oriented approach for representing engineers’ knowledge of adaptation choices that are deemed practical, and 2) an online learning-based approach for assessing and reasoning about adaptation decisions that does not require an explicit representation of the internal structure of the managed Software system. Engineers’ knowledge, represented in feature-models, adds structure to learning, which in turn makes online learning feasible. We present an empirical evaluation of the framework using a real-world self-Adaptive Software system. Results demonstrate the framework’s ability to accurately learn the changing dynamics of the system while achieving efficient analysis and adaptation.