TestU01

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 112 articles )

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  1. Haramoto, Hiroshi: Study on upper limit of sample size for a two-level test in NIST SP800-22 (2021)
  2. Kaur, Rajwinder; Singh, Butta: A hybrid algorithm for robust image steganography (2021)
  3. Gevorkyan, M. N.; Demidova, A. V.; Korol’kova, A. V.; Kulyabov, D. S.: A practical approach to testing random number generators in computer algebra systems (2020)
  4. Gevorkyan, M. N.; Korolkova, A. V.; Kulyabov, D. S.; Sevast’yanov, L. A.: A modular extension for a computer algebra system (2020)
  5. L’Ecuyer, Pierre; Wambergue, Paul; Bourceret, Erwan: Spectral analysis of the MIXMAX random number generators (2020)
  6. Liu, Hongjun; Kadir, Abdurahman; Xu, Chengbo: Color image encryption with cipher feedback and coupling chaotic map (2020)
  7. Liu, Yu; Qin, Zheng; Liao, Xiaofeng; Wu, Jiahui: A chaotic image encryption scheme based on Hénon-Chebyshev modulation map and genetic operations (2020)
  8. Lorek, Paweł; Łoś, Grzegorz; Gotfryd, Karol; Zagórski, Filip: On testing pseudorandom generators via statistical tests based on the arcsine law (2020)
  9. Rainer, Benjamin; Pilz, Jürgen; Deutschmann, Martin: Assessing the statistical quality of RNGs (2020)
  10. Bhattacharjee, Kamalika; Das, Sukanta: Random number generation using decimal cellular automata (2019)
  11. Fan, Chunlei; Wang, Chuanfu; Ding, Qun: A novel algorithm for detection and localization of periodic phenomena of chaotic binary sequences (2019)
  12. Flores-Vergara, A.; García-Guerrero, E. E.; Inzunza-González, E.; López-Bonilla, O. R.; Rodríguez-Orozco, Eduardo; Cárdenas-Valdez, Jose R.; Tlelo-Cuautle, E.: Implementing a chaotic cryptosystem in a 64-bit embedded system by using multiple-precision arithmetic (2019)
  13. Haramoto, Hiroshi; Matsumoto, Makoto: Checking the quality of approximation of (p)-values in statistical tests for random number generators by using a three-level test (2019)
  14. Harase, Shin: Conversion of mersenne twister to double-precision floating-point numbers (2019)
  15. Huang, Xuan; Liu, Lingfeng; Li, Xiangjun; Yu, Minrong; Wu, Zijie: A new two-dimensional mutual coupled logistic map and its application for pseudorandom number generator (2019)
  16. Lemire, Daniel; O’Neill, Melissa E.: Xorshift1024*, xorshift1024+, xorshift128+ and xoroshiro128+ fail statistical tests for linearity (2019)
  17. Li, Bo; Liao, Xiaofeng; Jiang, Yan: A novel image encryption scheme based on improved random number generator and its implementation (2019)
  18. Martirosyan, Narek; Savvidy, Konstantin; Savvidy, George: Spectral test of the MIXMAX random number generators (2019)
  19. Owen, Art B.: Comment: unreasonable effectiveness of Monte Carlo (2019)
  20. Sulak, Fatih; Doğanaksoy, Ali; Uğuz, Muhiddin; Koçak, Onur: Periodic template tests: a family of statistical randomness tests for a collection of binary sequences (2019)

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