PrimitiveC-ADL: Primitive Component Architecture Description Language

In this paper, we introduce an architecture descrip- tion language (ADL) for PCOMs (a context oriented component model). The language is described at three levels: (1) Building blocks (PCOMs context oriented components types) (2) Connec- tors, which connect components externally and internally, and (3) Architectural Configuration, which includes a full description of composition and decomposition mechanisms. The contribution is designing ADL. That supports context- orinted component by providing new architecture elements, which fulfil the requirements of designing context oriented component based applications. Context oriented component is a behavioural unit composed of static parts and dynamic parts. A PCOMs component model design was introduced in our previous work. PCOMs proposes a component model design to compose context-aware system by capturing context condition at runtime. The model is a component-based one that modifies the application architecture by subdividing components into subsystems of static and dynamic elements. We map each context condition to a composable template architectural configuration. Each context condition acts to select behavioural patterns, which combine to form application architectures. Different types of architecture elements are proposed in this work. We focus in defining the following new elements: Com- ponents’ dynamic and static parts, components’ layers, decision policies, and composition plan. Finally we introduce an ADL that fully supports context aware applications, by supporting the definition of a component as a unit of behaviour. Our ADL clearly defines the composition mechanisms, and provides proper definition for the composition’s design Patterns and composition plan. A Context oriented component is a behavioural unit composed with static parts and dynamic parts. A PCOMs component model design was introduced in our previous work. PCOMs proposes a component model design to compose context-aware system by capturing context condition at runtime. The model is a component-based one that modifies the application architecture by subdividing components into subsystems of static and dynamic elements. We map each context condition to a composable tem- plate architectural configuration. Each context condition acts to selected behavioural patterns, which combine to form application architectures

Work-in-progress Assume-guarantee reasoning with ioco

This paper presents a combination between the assume-guarantee paradigm and the testing relation ioco. The assume-guarantee paradigm is a ”divide and conquer” technique that decomposes the verification of a system into smaller tasks that involve the verification of its components. The principal aspect of assume-guarantee reasoning is to consider each component separately, while taking into account assumptions about the context of the component. The testing relation ioco is a formal conformance relation for model-based testing that works on labeled transition systems. Our main result shows that, with certain restrictions, assume-guarantee reasoning can be applied in the context of ioco. This enables testing ioco-conformance of a system by testing its components separately

Detection of Sparse Positive Dependence

In a bivariate setting, we consider the problem of detecting a sparse contamination or mixture component, where the effect manifests itself as a positive dependence between the variables, which are otherwise independent in the main component. We first look at this problem in the context of a normal mixture model. In essence, the situation reduces to a univariate setting where the effect is a decrease in variance. In particular, a higher criticism test based on the pairwise differences is shown to achieve the detection boundary defined by the (oracle) likelihood ratio test. We then turn to a Gaussian copula model where the marginal distributions are unknown. Standard invariance considerations lead us to consider rank tests. In fact, a higher criticism test based on the pairwise rank differences achieves the detection boundary in the normal mixture model, although not in the very sparse regime. We do not know of any rank test that has any power in that regime

Exact Dimensionality Selection for Bayesian PCA

We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high-dimensional dataset. To this end, we introduce a novel formulation of the probabilisitic principal component analysis model based on a normal-gamma prior distribution. In this context, we exhibit a closed-form expression of the marginal likelihood which allows to infer an optimal number of components. We also propose a heuristic based on the expected shape of the marginal likelihood curve in order to choose the hyperparameters. In non-asymptotic frameworks, we show on simulated data that this exact dimensionality selection approach is competitive with both Bayesian and frequentist state-of-the-art methods

A Framework for Agile Development of Component-Based Applications

Agile development processes and component-based software architectures are two software engineering approaches that contribute to enable the rapid building and evolution of applications. Nevertheless, few approaches have proposed a framework to combine agile and component-based development, allowing an application to be tested throughout the entire development cycle. To address this problematic, we have built CALICO, a model-based framework that allows applications to be safely developed in an iterative and incremental manner. The CALICO approach relies on the synchronization of a model view, which specifies the application properties, and a runtime view, which contains the application in its execution context. Tests on the application specifications that require values only known at runtime, are automatically integrated by CALICO into the running application, and the captured needed values are reified at execution time to resume the tests and inform the architect of potential problems. Any modification at the model level that does not introduce new errors is automatically propagated to the running system, allowing the safe evolution of the application. In this paper, we illustrate the CALICO development process with a concrete example and provide information on the current implementation of our framework

Resolving Architectural Mismatches of COTS Through Architectural Reconciliation

The integration of COTS components into a system under development entails architectural mismatches. These have been tackled, so far, at the component level, through component adaptation techniques, but they also must be tackled at an architectural level of abstraction. In this paper we propose an approach for resolving architectural mismatches, with the aid of architectural reconciliation. The approach consists of designing and subsequently reconciling two architectural models, one that is forward-engineered from the requirements and another that is reverse-engineered from the COTS-based implementation. The final reconciled model is optimally adapted both to the requirements and to the actual COTS-based implementation. The contribution of this paper lies in the application of architectural reconciliation in the context of COTS-based software development. Architectural modeling is based upon the UML 2.0 standard, while the reconciliation is performed by transforming the two models, with the help of architectural design decisions.

Context Aware Adaptable Applications - A global approach

Actual applications (mostly component based) requirements cannot be expressed without a ubiquitous and mobile part for end-users as well as for M2M applications (Machine to Machine). Such an evolution implies context management in order to evaluate the consequences of the mobility and corresponding mechanisms to adapt or to be adapted to the new environment. Applications are then qualified as context aware applications. This first part of this paper presents an overview of context and its management by application adaptation. This part starts by a definition and proposes a model for the context. It also presents various techniques to adapt applications to the context: from self-adaptation to supervised approached. The second part is an overview of architectures for adaptable applications. It focuses on platforms based solutions and shows information flows between application, platform and context. Finally it makes a synthesis proposition with a platform for adaptable context-aware applications called Kalimucho. Then we present implementations tools for software components and a dataflow models in order to implement the Kalimucho platform

Redefining Context Windows for Word Embedding Models: An Experimental Study

Distributional semantic models learn vector representations of words through the contexts they occur in. Although the choice of context (which often takes the form of a sliding window) has a direct influence on the resulting embeddings, the exact role of this model component is still not fully understood. This paper presents a systematic analysis of context windows based on a set of four distinct hyper-parameters. We train continuous Skip-Gram models on two English-language corpora for various combinations of these hyper-parameters, and evaluate them on both lexical similarity and analogy tasks. Notable experimental results are the positive impact of cross-sentential contexts and the surprisingly good performance of right-context windows