In this article, we propose a study of the characteristics of the R.A NMJ algorithm which is a new regenerator of pseudo-random sequences. The functions and classes of three states functions are fully used by the algo...
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ISBN:
(纸本)9781424416875
In this article, we propose a study of the characteristics of the R.A NMJ algorithm which is a new regenerator of pseudo-random sequences. The functions and classes of three states functions are fully used by the algorithm RA NMJ to such a point that the theoretical analysis of the latter is connected to the one of the functions and the classes of functions. This analysis will study the linearity and injectivity of these three states functions as well as estimation on a collective complexity of the classes of the three states functions with the help of the MDS algorithm.
In this article, we propose a study of the characteristics of the R.A NMJ algorithm which is a new regenerator of pseudo-random sequences. The functions and classes of three states functions are fully used by the algo...
详细信息
In this article, we propose a study of the characteristics of the R.A NMJ algorithm which is a new regenerator of pseudo-random sequences. The functions and classes of three states functions are fully used by the algorithm R.A NMJ to such a point that the theoretical analysis of the latter is connected to the one of the functions and the classes of functions. This analysis will study the linearity and injectivity of these three states functions as well as estimation on a collective complexity of the classes of the three states functions with the help of the MDS algorithm.
The level of confidence in a software component is often linked to the quality of its test cases. This quality can in turn be evaluated with mutation analysis: faults are injected into the software component (making m...
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The level of confidence in a software component is often linked to the quality of its test cases. This quality can in turn be evaluated with mutation analysis: faults are injected into the software component (making mutants of it) to check the proportion of mutants detected ('killed') by the test cases. But while the generation of a set of basic test cases is easy, improving its quality may require prohibitive effort. This paper focuses on the issue of automating the test optimization. The application of genetic algorithms would appear to be an interesting way of tackling it. The optimization problem is modelled as follows: a test case can be considered as a predator while a mutant program is analogous to a prey. The aim of the selection process is to generate test cases able to kill as many mutants as possible, starting from an initial set of predators, which is the test cases set provided by the programmer. To overcome disappointing experimentation results, on Net components and unit Eiffel classes, a slight variation on this idea is studied, no longer at the 'animal' level (lions killing zebras, say) but at the bacteriological level. The bacteriological level indeed better reflects the test case optimization issue: it mainly differs from the genetic one by the introduction of a memorization function and the suppression of the crossover operator. The purpose of this paper is to explain how the genetic algorithms have been adapted to fit with the issue of test optimization. The resulting algorithm differs so much from genetic algorithms that it has been given another name: bacteriological algorithm. Copyright (c) 2005 John Wiley & Sons, Ltd.
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