8,739 research outputs found
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
Runtime verification for stochastic systems
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 97-101).We desire a capability for the safety monitoring of complex, mixed hardware/software systems, such as a semi-autonomous car. The field of runtime verification has developed many tools for monitoring the safety of software systems in real time. However, these tools do not allow for uncertainty in the system's state or failure, both of which are essential for the problems we care about. In this thesis I propose a capability for monitoring the safety criteria of mixed hardware/software systems that is robust to uncertainty and hardware failure. I start by framing the problem as runtime verification of stochastic, faulty, hidden-state systems. I solve this problem by performing belief state estimation over a novel set of models that combine Büchi automata, for modeling safety requirements, with probabilistic hierarchical constraint automata, for modeling mixed hardware/software systems. This method is innovative in its melding of safety monitoring techniques from the runtime verification community with probabilistic mode estimation techniques from the field of model-based diagnosis. I have verified my approach by testing it on automotive safety requirements for a model of an actuator component. My approach shows promise as a real-time safety monitoring tool for such systems.by Cristina M. Wilcox.S.M
Hashmod: A Hashing Method for Scalable 3D Object Detection
We present a scalable method for detecting objects and estimating their 3D
poses in RGB-D data. To this end, we rely on an efficient representation of
object views and employ hashing techniques to match these views against the
input frame in a scalable way. While a similar approach already exists for 2D
detection, we show how to extend it to estimate the 3D pose of the detected
objects. In particular, we explore different hashing strategies and identify
the one which is more suitable to our problem. We show empirically that the
complexity of our method is sublinear with the number of objects and we enable
detection and pose estimation of many 3D objects with high accuracy while
outperforming the state-of-the-art in terms of runtime.Comment: BMVC 201
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