TestU01 is a software library, implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators. The library implements several types of random number generators in generic form, as well as many specific generators proposed in the literature or found in widely-used software. It provides general implementations of the classical statistical tests for random number generators, as well as several others proposed in the literature, and some original ones. These tests can be applied to the generators predefined in the library and to user-defined generators. Specific tests suites for either sequences of uniform random numbers in [0,1] or bit sequences are also available. Basic tools for plotting vectors of points produced by generators are provided as well. Additional software permits one to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of random number generators. That is, for a given kind of test and a given class of random number generators, to determine how large should be the sample size of the test, as a function of the generator’s period length, before the generator starts to fail the test systematically.

References in zbMATH (referenced in 59 articles )

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  1. Aljahdali, Asia; Mascagni, Michael: Feistel-inspired scrambling improves the quality of linear congruential generators (2017)
  2. Contassot-Vivier, Sylvain; Couchot, Jean-François; Guyeux, Christophe; Heam, Pierre-Cyrille: Random walk in a $\mathsf N$-cube without Hamiltonian cycle to chaotic pseudorandom number generation: theoretical and practical considerations (2017)
  3. Liu, Yunqi; Luo, Yuling; Song, Shuxiang; Cao, Lvchen; Liu, Junxiu; Harkin, Jim: Counteracting dynamical degradation of digital chaotic Chebyshev map via perturbation (2017)
  4. M. N. Gevorkyan, A. V. Demidova, A. V. Korolkova, D. S. Kulyabov, L. A. Sevastianov: The Stochastic Processes Generation in OpenModelica (2017) arXiv
  5. Vigna, Sebastiano: Further scramblings of Marsaglia’s $\mathsfxorshift$ generators (2017)
  6. Alvarez, Nicolás; Becher, Verónica; Ferrari, Pablo A.; Yuhjtman, Sergio A.: Perfect necklaces (2016)
  7. Balková, Ľubomíra; Bucci, Michelangelo; De Luca, Alessandro; Hladký, Jiří; Puzynina, Svetlana: Aperiodic pseudorandom number generators based on infinite words (2016)
  8. Faure, Emil V.; Shcherba, Anatoly I.; Rudnytskyi, V.M.: The method and criterion for quality assessment of random number sequences (2016)
  9. Len^otre, Lionel: A strategy for parallel implementations of stochastic Lagrangian simulation (2016)
  10. Li, Jie; Zheng, Jianliang; Whitlock, Paula: MaD0: an ultrafast nonlinear pseudorandom number generator (2016)
  11. Li, Yantao; Li, Xiang: Chaotic hash function based on circular shifts with variable parameters (2016)
  12. Savvidy, Konstantin; Savvidy, George: Spectrum and entropy of C-systems MIXMAX random number generator (2016)
  13. Self, Julian; Mackey, Michael C.: Random numbers from a delay equation (2016)
  14. Sýs, Marek; Matyáš, Vashek: Randomness testing: result interpretation and speed (2016)
  15. Vigna, Sebastiano: An experimental exploration of Marsaglia’s xorshift generators, scrambled (2016)
  16. Balint, Adrian; Belov, Anton; Järvisalo, Matti; Sinz, Carsten: Overview and analysis of the SAT challenge 2012 solver competition (2015) ioport
  17. Schaathun, Hans Georg: Evaluation of splittable pseudo-random generators (2015)
  18. Demchik, Vadim: Pseudorandom numbers generation for Monte Carlo simulations on GPUs: OpenCL approach (2014)
  19. Karl, Andrew T.; Eubank, Randy; Milovanovic, Jelena; Reiser, Mark; Young, Dennis: Using rngstreams for parallel random number generation in C++ and R (2014)
  20. L’Ecuyer, Pierre; Simard, Richard: On the lattice structure of a special class of multiple recursive random number generators (2014)

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