80,966 research outputs found
Mapping Feature Models Onto Component Models to Build Dynamic Software Product Lines
Systems such as adaptative and context–aware ones must adapt themselves to changing requirements at runtime. Modeling and implementing this kind of systems is a diffi- cult operation. Software Product Lines (SPL) approach has already coped with modeling a set of software products that share a common base of features by means of feature models. We propose using feature models to model the potential states of a product in what it is called Dynamic SPL. The objective of this paper is generating a component architecture that supports the dynamics of products and which is easily inferred from a feature model. The resultant model performs an automated analysis of the feature model in real–time to correctly response to changes
Early aspects: aspect-oriented requirements engineering and architecture design
This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications
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Towards an aspect weaving BPEL engine
This position paper proposes the use of dynamic aspects and
the visitor design pattern to obtain a highly configurable and
extensible BPEL engine. Using these two techniques, the
core of this infrastructural software can be customised to
meet new requirements and add features such as debugging,
execution monitoring, or changing to another Web Service
selection policy. Additionally, it can easily be extended to
cope with customer-specific BPEL extensions. We propose
the use of dynamic aspects not only on the engine itself
but also on the workflow in order to tackle the problems of
Web Service hot deployment and hot fixes to long running
processes. In this way, composing aWeb Service "on-the-fly"
means weaving its choreography interface into the workflow
Robot introspection through learned hidden Markov models
In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behavioural models to provide a robot with an introspective capability. We assume that the behaviour of a robot in achieving a task can be modelled as a finite stochastic state transition system. Beginning with data recorded by a robot in the execution of a task, we use unsupervised learning techniques to estimate a hidden Markov model (HMM) that can be used both for predicting and explaining the behaviour of the robot in subsequent executions of the task. We demonstrate that it is feasible to automate the entire process of learning a high quality HMM from the data recorded by the robot during execution of its task.The learned HMM can be used both for monitoring and controlling the behaviour of the robot. The ultimate purpose of our work is to learn models for the full set of tasks associated with a given problem domain, and to integrate these models with a generative task planner. We want to show that these models can be used successfully in controlling the execution of a plan. However, this paper does not develop the planning and control aspects of our work, focussing instead on the learning methodology and the evaluation of a learned model. The essential property of the models we seek to construct is that the most probable trajectory through a model, given the observations made by the robot, accurately diagnoses, or explains, the behaviour that the robot actually performed when making these observations. In the work reported here we consider a navigation task. We explain the learning process, the experimental setup and the structure of the resulting learned behavioural models. We then evaluate the extent to which explanations proposed by the learned models accord with a human observer's interpretation of the behaviour exhibited by the robot in its execution of the task
Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach
Goals are first-class entities in a self-adaptive system (SAS) as they guide
the self-adaptation. A SAS often operates in dynamic and partially unknown
environments, which cause uncertainty that the SAS has to address to achieve
its goals. Moreover, besides the environment, other classes of uncertainty have
been identified. However, these various classes and their sources are not
systematically addressed by current approaches throughout the life cycle of the
SAS. In general, uncertainty typically makes the assurance provision of SAS
goals exclusively at design time not viable. This calls for an assurance
process that spans the whole life cycle of the SAS. In this work, we propose a
goal-oriented assurance process that supports taming different sources (within
different classes) of uncertainty from defining the goals at design time to
performing self-adaptation at runtime. Based on a goal model augmented with
uncertainty annotations, we automatically generate parametric symbolic formulae
with parameterized uncertainties at design time using symbolic model checking.
These formulae and the goal model guide the synthesis of adaptation policies by
engineers. At runtime, the generated formulae are evaluated to resolve the
uncertainty and to steer the self-adaptation using the policies. In this paper,
we focus on reliability and cost properties, for which we evaluate our approach
on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the
validation are promising and show that our approach is able to systematically
tame multiple classes of uncertainty, and that it is effective and efficient in
providing assurances for the goals of self-adaptive systems
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