Many current version control systems use a simple data model that is barely sufficient to manipulate source-code. This simple data model is not sufficient to provide versioning capabilities for software modeling envir...
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Networks-on-Chip (NoC) have been used as an interesting option in design of communication infrastructures for embedded systems, providing a scalable structure and balancing the communication between cores. Because sev...
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The Ray Tracing rendering algorithm can produce high-fidelity images of 3-D scenes, including shadow effects, as well as reflections and transparencies. This is currently done at a processing speed of at most 30 frame...
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The technical literature regarding Model-based Testing (MBT) has several techniques with different characteristics and goals available to be applied in software projects. Besides the lack of information regarding thes...
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ISBN:
(纸本)9781424437436
The technical literature regarding Model-based Testing (MBT) has several techniques with different characteristics and goals available to be applied in software projects. Besides the lack of information regarding these techniques, they could be applied together in a software project aiming at improving the testing coverage. However, this decision needs to be carefully analyzed to avoid loss of resources in a software project. Based on this scenario, this paper proposes an approach with the purpose of supporting the unique or combined selection of MBT techniques for a given software project considering two aspects: the adequacy level between MBT techniques and the software project characteristics and impact of more than one MBT technique in some testing process variables. At the end, preliminary results of an experimental evaluation are presented.
Experimental studies have been used as a mechanism to acquire knowledge through a scientific approach based on measurement of phenomena in different areas. However it is hard to run such studies when they require mode...
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Experimental studies have been used as a mechanism to acquire knowledge through a scientific approach based on measurement of phenomena in different areas. However it is hard to run such studies when they require models (simulation), produce amount of information, and explore science in scale. In this case, a computerized infrastructure is necessary and constitutes a complex system to be built. In this paper we discuss an experimentation environment that has being built to support large scale experimentation and scientific knowledge management in software engineering.
Business processes modeling projects are increasingly widespread in organizations, which usually invest much in hiring expert consultants to do such job. These consultants come from various organizations, and have dif...
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Software technologies, such as model-based testing approaches, have specific characteristics and limitations that can affect their use in software projects. To make available knowledge regarding such technologies is i...
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ISBN:
(纸本)9781605580302
Software technologies, such as model-based testing approaches, have specific characteristics and limitations that can affect their use in software projects. To make available knowledge regarding such technologies is important to support the decision regarding their use in software projects. In particular, a choice of model-based testing approach can influence testing success or failure. Therefore, this paper aims at describing knowledge acquired from a systematic review regarding model-based testing approaches and proposing an infrastructure towards supporting their selection for software projects. Copyright 2008 ACM.
The paper presents a comparison between two unsupervised neural network models: (i) the well-known fuzzy ART, and (ii) AUTOWISARD, a new unsupervised version of the classic WISARD weightless neural network model. It i...
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The paper presents a comparison between two unsupervised neural network models: (i) the well-known fuzzy ART, and (ii) AUTOWISARD, a new unsupervised version of the classic WISARD weightless neural network model. It is shown that AUTOWISARD is simple, fast and stable, whilst keeping compatibility with the original WISARD architecture. Experimental test results over binary patterns benchmarks have shown that, although both unsupervised learning models are remarkably simple, AUTOWISARD consistently exhibits better classification skills than fuzzy ART. It is also shown that such superiority happens thanks to AU-TOWISARD's richer internal representation of the trained patterns and the training methods employed by the algorithm, such as the learning window and partial training strategies.
The creation of tools, techniques and methodologies to support the manipulation of large data sets has been receiving special attention of both scientific and industrial communities, in order to discover new ways of d...
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Proposes a method for data clustering in a n-dimensional space using the elastic net algorithm which is a variant of the Kohonen topographic map learning algorithm. The elastic net algorithm is a mechanical metaphor i...
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Proposes a method for data clustering in a n-dimensional space using the elastic net algorithm which is a variant of the Kohonen topographic map learning algorithm. The elastic net algorithm is a mechanical metaphor in which an elastic ring is attracted by points in a bi-dimensional space while their internal elastic forces try to shun the elastic expansion. The different weights associated with these two kinds of forces lead the elastic to a gradual expansion in the direction of the bi-dimensional points. In this method, the elastic net algorithm is employed with the help of a heuristic framework that improves its performance for application in the n-dimensional space of cluster analysis. Tests were made with two types of data sets: (1) simulated data sets with up to 1000 points randomly generated in groups linearly separable with up to dimension 10 and (2) the Fisher Iris Plant database, a well-known database referred to in the pattern recognition literature. The advantages of the method presented are its simplicity, its fast and stable convergence, beyond efficiency in cluster analysis.
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