30,034 research outputs found

    Measuring Qualities for OSGi Component-Based Applications

    Get PDF
    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

    Full text link
    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

    Get PDF
    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

    Get PDF
    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

    Get PDF
    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
    corecore