It is concluded that one of the most influential factors today is the de-shadowization of the economy. The level of unit testing of variables of de-shadowization of tax gaps from indicators of financial and economic development of the EU and Ukraine is analyzed. A methodical approach to estimating the level of de-shadowization of the tax gap on the income tax of economic entities is proposed. The method of calculation of the integrated indicator of strategic alternatives is presented and represents the configuration of the modified system of fiscal innovations in relation to the taxation of economic entities. The methodological approach to cointegration of the level of de-shadowization of tax gaps into the system-compositional model of fiscal policy is substantiated, taking into account the strategic determinants of financial and economic development of the state.
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The purpose of the article is to consider the empirical calculations of the level of de-shadowization of tax gaps in the system-compositional model of the fiscal policy of the state. Finally, dynamic-coverage techniques should be favored over static-coverage techniques due to their acceptable analysis overhead however, in settings where the time for prioritzation is limited, static-coverage techniques provide an attractive alternative.
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The results demonstrate that the total strategy is superior to the additional strategy. The most effective technique has an overhead of 11% of the total microbenchmark suite execution time, making TCP a viable option for performance regression testing. Our efficiency analysis reveals that the runtime overhead of TCP varies considerably depending on the exact parameterization. We find that TCP techniques have a mean APFD-P (average percentage of fault-detection on performance) effectiveness between 0.54 and 0.71 and are able to capture the three largest performance changes after executing 29% to 66% of the whole microbenchmark suite.
#Aplikasi menggunakan bahasa assembly software
In this paper, we empirically study coverage-based TCP techniques, employing total and additional greedy strategies, applied to software microbenchmarks along multiple parameterization dimensions, leading to 54 unique technique instantiations. However, it is unclear whether traditional unit testing TCP techniques work equally well for software microbenchmarks. This may especially be beneficial for microbenchmark suites, because they take considerably longer to execute than unit test suites. Applying test case prioritization (TCP), a regression testing technique, to software microbenchmarks may help capturing large performance regressions sooner upon new versions. While regression testing is widely studied for functional tests, performance regression testing, e.g., with software microbenchmarks, is hardly investigated. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation for real-life and synthetic data.ĪbstractRegression testing comprises techniques which are applied during software evolution to uncover faults effectively and efficiently.
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#Aplikasi menggunakan bahasa assembly code
The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. The scikit-dimension package provides a uniform implementation of most of the known ID estimators based on the scikit-learn application programming interface to evaluate the global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. This technical note introduces scikit-dimension, an open-source Python package for intrinsic dimension estimation.
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A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID).