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A test suite of 16 optimisation problem instances, based on the SAFARI-1 nuclear research reactor, has been established for the comparative study.
The algorithm used in the study follows a primal-dual interior point approach and shows encouraging results on a test suite of MPECs.
Comparisons are conducted in the context of a test suite of several problem instances based on the SAFARI-1 nuclear research reactor.
We analyze the computational complexity of this approach, and evaluate its performance on a test suite of NGS datasets, demonstrating its superiority to traditional cross-correlation analysis.
We analyze the computational complexity of this algorithm and evaluate its performance on a test suite of NGS datasets, demonstrating its superiority to traditional cross-correlation analysis.
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What is the quality of a test suite for a product line, anyway?
There are several methods to assess the capability of a test suite to detect faults in a potentially wrong system.
To demonstrate that our code can retrieve continuum solutions, we perform a test-suite of classic hydrodynamic shock problems consisting of the Sod, the Noh, and the Sedov tests.
Experiments undertaken to study the performance of the extended algorithm reveal that the extended algorithm outperforms its competitors with respect to four performance metrics, when examined on a test-suite of 23 standard benchmarks with additive noise of three statistical distributions.
Each testing approach deploys different metrics (quantitative measures) to evaluate the quality of a test suite (or of the testing process as a whole) [4].
An input sequence α∈Ω M starting with a reset symbol r is a test case of M. A test suite T consists of a finite set of test cases of M, such that there are no α,β∈T where α<β.
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