30,034 research outputs found
Measuring Qualities for OSGi Component-Based Applications
International audienceComponent-based software engineering (CBSE) begins to reach a certain level of maturity. Indeed, for the development of complex applications the use of component paradigm has become common. Therefore, the evaluation of the quality of these applications becomes necessary. In this context, the use of metrics is considered very important. Several metrics specific to component-based applications have been proposed. However, any of these metrics gained the consensus of the CBSE community and mainly there is no proposed tool to support them. As a large part of frameworks for component-based application development is based on object-oriented technology, we propose to use some object-oriented (OO) metrics to evaluate component-based applications produced with this kind of framework. Indeed, these metrics became a standard in OO community. So, they are well-defined, well-known and empirically validated. To identify which object-oriented metrics are useful for the evaluation of component-based applications, we have conducted an experimental study on 10 OSGi applications. This study also gives us the opportunity to discuss on the respect by OSGi developers of some properties pointed out by the literatur
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
An intuitive control space for material appearance
Many different techniques for measuring material appearance have been
proposed in the last few years. These have produced large public datasets,
which have been used for accurate, data-driven appearance modeling. However,
although these datasets have allowed us to reach an unprecedented level of
realism in visual appearance, editing the captured data remains a challenge. In
this paper, we present an intuitive control space for predictable editing of
captured BRDF data, which allows for artistic creation of plausible novel
material appearances, bypassing the difficulty of acquiring novel samples. We
first synthesize novel materials, extending the existing MERL dataset up to 400
mathematically valid BRDFs. We then design a large-scale experiment, gathering
56,000 subjective ratings on the high-level perceptual attributes that best
describe our extended dataset of materials. Using these ratings, we build and
train networks of radial basis functions to act as functionals mapping the
perceptual attributes to an underlying PCA-based representation of BRDFs. We
show that our functionals are excellent predictors of the perceived attributes
of appearance. Our control space enables many applications, including intuitive
material editing of a wide range of visual properties, guidance for gamut
mapping, analysis of the correlation between perceptual attributes, or novel
appearance similarity metrics. Moreover, our methodology can be used to derive
functionals applicable to classic analytic BRDF representations. We release our
code and dataset publicly, in order to support and encourage further research
in this direction
An approach based on genetic algorithms for clustering classes in components
The goal of this work is to create a model that allows identification of the software components (or subsystems according to the unified process terminology) based on the design models, or more exactly, based on the classes diagrams (for the static aspects) and on the interaction diagrams (for the dynamic aspects). The work also presents a genetic algorithm used for the clustering of classes into modules
A COUPLING AND COHESION METRICS SUITE FOR
The increasing need for software quality measurements has led to extensive research
into software metrics and the development of software metric tools. To maintain high
quality software, developers need to strive for a low-coupled and highly cohesive
design. One of many properties considered when measuring coupling and cohesion is the
type of relationships that made up coupling and cohesion. What these specific
relationships are is widely understood and accepted by researchers and practitioners.
However, different researchers base their metrics on a different subset of these
relationships.
Studies have shown that because of the inclusion of multiple subsets of relationships
in one measure of coupling and cohesion metrics, the measures tend to correlate among
each other. Validation of these metrics against maintainability index of a Java program
suggested that there is high multicollinearity among coupling and cohesion metrics.
This research introduces an approach of implementing coupling and cohesion
metrics. Every possible relationship is considered and, for each, addressed the issue of
whether or not it has significant effect on maintainability index prediction. Validation of
orthogonality of the selected metrics is assessed by means of principal component
analysis. The investigation suggested that some of the metrics are independent set of
metrics, while some are measuring similar dimension
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