253,901 research outputs found

    A regression test case selection and prioritization for object-oriented programs using dependency graph and genetic algorithm

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    Regression testing is very important activity in software testing. The re-execution of all test cases during regression testing will be costly. The effective and efficient test case selection from the existing test suite becomes very critical issue in regression testing. This paper presents an evolutionary regression test case prioritization for object-oriented software based on dependence graph model analysis of the affected program using Genetic Algorithm. The approach is based on optimization of selected test case from test suite T. The goal is to identify changes in a method's body due to data dependence, control dependence and dependent due to object relation such as inheritance and polymorphism, select the test cases based on affected statements and ordered them based on their fitness by using GA. The number of affected statements determined how fit a test case is good for regression testing. A case study was reported to provide evidence of the feasibility of the approach and its benefits in increasing the rate of fault detection and reduction in regression testing effort. The goodness of this ordering is measured using Average Percentage of rate of Faults Detection (APFD) metric to evaluate the effectiveness and efficiency of the approach. It was observed that our proposed approach is more efficient and effective in regression testing

    A Quadratically Regularized Functional Canonical Correlation Analysis for Identifying the Global Structure of Pleiotropy with NGS Data

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    Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore multiple levels of representations of genetic variants, learn their internal patterns involved in the disease development, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new framework referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the nine competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and nine other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the nine other statistics.Comment: 64 pages including 12 figure

    Calibration of the seasonal growth model. Final report

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    Statistical methods of SNP data analysis with applications

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    Various statistical methods important for genetic analysis are considered and developed. Namely, we concentrate on the multifactor dimensionality reduction, logic regression, random forests and stochastic gradient boosting. These methods and their new modifications, e.g., the MDR method with "independent rule", are used to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and external risk factors are examined. To perform the data analysis concerning the ischemic heart disease and myocardial infarction the supercomputer SKIF "Chebyshev" of the Lomonosov Moscow State University was employed
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