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Searching for cognitively diverse tests: Towards universal test diversity metrics
 In Proceedings of the First Workshop on SearchBased Software Testing
, 2008
"... Searchbased software testing (SBST) has shown a potential to decrease cost and increase quality of testingrelated software development activities. Research in SBST has so far mainly focused on the search for isolated tests that are optimal according to a fitness function that guides the search. I ..."
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Searchbased software testing (SBST) has shown a potential to decrease cost and increase quality of testingrelated software development activities. Research in SBST has so far mainly focused on the search for isolated tests that are optimal according to a fitness function that guides the search. In this paper we make the case for fitness functions that measure test fitness in relation to existing or previously found tests; a test is good if it is diverse from other tests. We present a model for test variability and propose the use of a theoretically optimal diversity metric at variation points in the model. We then describe how to apply a practically useful approximation to the theoretically optimal metric. The metric is simple and powerful and can be adapted to a multitude of different test diversity measurement scenarios. We present initial results from an experiment to compare how similar to human subjects, the metric can cluster a set of test cases. To carry out the experiment we have extended an existing framework for test automation in an objectoriented, dynamic programming language. 1.
Article HydroZIP: How Hydrological Knowledge can Be Used to Improve Compression of Hydrological Data
, 2013
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Clustering
, 2009
"... The problem is to construct an optimal weight tree from the 3 () n 4 weighted quartet topologies on n objects, where optimality means that the summed weight of the embedded quartet topologies is optimal (so it can be the case that the optimal tree embeds all quartets as nonoptimal topologies). We pr ..."
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The problem is to construct an optimal weight tree from the 3 () n 4 weighted quartet topologies on n objects, where optimality means that the summed weight of the embedded quartet topologies is optimal (so it can be the case that the optimal tree embeds all quartets as nonoptimal topologies). We present a Monte Carlo heuristic, based on randomized hill climbing, for approximating the optimal weight tree, given the quartet topology weights. The method repeatedly transforms a bifurcating tree, with all objects involved as leaves, achieving a monotonic approximation to the exact single globally optimal tree. The method has been extensively used for general hierarchical clustering of nontreelike (nonphylogeny) data in various domains and across domains with heterogenous data, and is implemented and available, as part of the CompLearn package. We compare performance and running time with those of UPGMA, BioNJ, and NJ, as implemented in the SplitsTree package on genomic data for which the latter are optimized.
Effect of Image Linearization on Normalized Compression Distance
"... Abstract. Normalized Information Distance, based on Kolmogorov complexity, is an emerging metric for image similarity. It is approximated by the Normalized Compression Distance (NCD) which generates the relative distance between two strings by using standard compression algorithms to compare linear ..."
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Abstract. Normalized Information Distance, based on Kolmogorov complexity, is an emerging metric for image similarity. It is approximated by the Normalized Compression Distance (NCD) which generates the relative distance between two strings by using standard compression algorithms to compare linear strings of information. This relative distance quantifies the degree of similarity between the two objects. NCD has been shown to measure similarity effectively on information which is already a string: genomic string comparisons have created accurate phylogeny trees and NCD has also been used to classify music. Currently, to find a similarity measure using NCD for images, the images must first be linearized into a string, and then compared. To understand how linearization of a 2D image affects the similarity measure, we perform four types of linearization on a subset of the Corel image database and compare each for a variety of image transformations. Our experiment shows that different linearization techniques produce statistically significant differences in NCD for identical spatial transformations.
Defaults and principal parts: an empirical investigation
"... Our purpose is to use an empirical method to explore and contrast the role of default and principal part information in the differentiation of inflectional classes. We use a machinelearning method to cluster high frequency Russian nouns into their appropriate inflectional classes, first with full p ..."
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Our purpose is to use an empirical method to explore and contrast the role of default and principal part information in the differentiation of inflectional classes. We use a machinelearning method to cluster high frequency Russian nouns into their appropriate inflectional classes, first with full paradigm information, and then with particular types of information removed. When we remove default
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"... requirements for the degree of Master of Science in Computer Science. The thesis is equivalent to 20 weeks of full time studies. Contact Information: Author(s): ..."
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requirements for the degree of Master of Science in Computer Science. The thesis is equivalent to 20 weeks of full time studies. Contact Information: Author(s):
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, 2008
"... Engineering. The thesis is equivalent to 40 weeks of full time studies. Contact Information: Author(s): ..."
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Engineering. The thesis is equivalent to 40 weeks of full time studies. Contact Information: Author(s):
A.Meenakshi et al. / International Journal on Computer Science and Engineering (IJCSE) Efficient Storage Reduction of Frequency of Items in Vertical Data Layout
"... Abstract — The digital databases are immersed with large amount of data. The explosive growth of massive amounts of data leads to space complexity, performance degradation, scalability and time complexity. We cannot stop the incoming data, but we can fix some limitations to reduce the massive collec ..."
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Abstract — The digital databases are immersed with large amount of data. The explosive growth of massive amounts of data leads to space complexity, performance degradation, scalability and time complexity. We cannot stop the incoming data, but we can fix some limitations to reduce the massive collection of data. The crux of the matter is how to accommodate all these data in an efficient manner, minimize storage space and lossless data. There are many algorithms which have been developed so far. Even though there is a cutthroat competition to satisfy all these criteria, we have developed a novelty approach in our proposed work to reduce the frequencies of items in vertical data layout.In our work, we have concentrated on Run length encoding method in applying vertical layout and moreover, we have done experimental results in very large databases. Keywords Compression, vertical layout, vertical RLE, frequency, splitmatrix. I.