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 82 articles )

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  1. Fan, Chunlei; Wang, Chuanfu; Ding, Qun: A novel algorithm for detection and localization of periodic phenomena of chaotic binary sequences (2019)
  2. Lemire, Daniel; O’Neill, Melissa E.: Xorshift1024*, xorshift1024+, xorshift128+ and xoroshiro128+ fail statistical tests for linearity (2019)
  3. Owen, Art B.: Comment: unreasonable effectiveness of Monte Carlo (2019)
  4. Sulak, Fatih; Doğanaksoy, Ali; Uğuz, Muhiddin; Koçak, Onur: Periodic template tests: a family of statistical randomness tests for a collection of binary sequences (2019)
  5. Terenin, Alexander; Dong, Shawfeng; Draper, David: GPU-accelerated Gibbs sampling: a case study of the Horseshoe probit model (2019)
  6. Uğuz, Muhiddin; Doğanaksoy, Ali; Sulak, Fatih; Koçak, Onur: R-2 composition tests: a family of statistical randomness tests for a collection of binary sequences (2019)
  7. Bakiri, Mohammed; Guyeux, Christophe; Couchot, Jean-François; Oudjida, Abdelkrim Kamel: Survey on hardware implementation of random number generators on FPGA: theory and experimental analyses (2018)
  8. Contassot-Vivier, Sylvain; Couchot, Jean-François; Héam, Pierre-Cyrille: Gray codes generation algorithm and theoretical evaluation of random walks in (\mathsfN)-cubes (2018)
  9. Kneusel, Ronald T.: Random numbers and computers (2018)
  10. Lin, Y.; Wang, F.; Liu, B.: Random number generators for large-scale parallel Monte Carlo simulations on FPGA (2018)
  11. Liu, Lingfeng; Liu, Bocheng; Hu, Hanping; Miao, Suoxia: Reducing the dynamical degradation by bi-coupling digital chaotic maps (2018)
  12. Saito, Asaki; Yamaguchi, Akihiro: Pseudorandom number generator based on the Bernoulli map on cubic algebraic integers (2018)
  13. Savvidy, George; Savvidy, Konstantin: Exponential decay of correlations functions in MIXMAX generator of pseudorandom numbers (2018)
  14. Aljahdali, Asia; Mascagni, Michael: Feistel-inspired scrambling improves the quality of linear congruential generators (2017)
  15. Barash, Lev Yu.; Weigel, Martin; Borovský, Michal; Janke, Wolfhard; Shchur, Lev N.: GPU accelerated population annealing algorithm (2017)
  16. Beebe, Nelson H. F.: The mathematical-function computation handbook. Programming using the MathCW portable software library (2017)
  17. Cho, Kenichiro; Miyano, Takaya: Design and test of pseudorandom number generator using a star network of Lorenz oscillators (2017)
  18. Contassot-Vivier, Sylvain; Couchot, Jean-François; Guyeux, Christophe; Heam, Pierre-Cyrille: Random walk in a (\mathsfN)-cube without Hamiltonian cycle to chaotic pseudorandom number generation: theoretical and practical considerations (2017)
  19. Liu, Lingfeng; Lin, Jun; Miao, Suoxia; Liu, Bocheng: A double perturbation method for reducing dynamical degradation of the digital Baker map (2017)
  20. Liu, Yunqi; Luo, Yuling; Song, Shuxiang; Cao, Lvchen; Liu, Junxiu; Harkin, Jim: Counteracting dynamical degradation of digital chaotic Chebyshev map via perturbation (2017)

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