Testing Pseudorandom Generators

  • Ronald T. Kneusel
Chapter

Abstract

Testing pseudorandom number generators is not quite as straightforward as it might seem. In this chapter we consider classical tests of randomness and apply them to the generators discussed in Chap.  2. Next we investigate two popular test suites: dieharder (based on the older DIEHARD) and TestU01, and one quick test program (ent). These test suites are the benchmarks against which researchers generally measure new algorithms.

References

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    Brown, Robert G., Dirk Eddelbuettel, and David Bauer. “Dieharder: A random number test suite.” Open Source software library, under development (2013). http://webhome.phy.duke.edu/~rgb/General/dieharder.php.
  2. 2.
    L’Ecuyer, Pierre, and Richard Simard. “TestU01: AC library for empirical testing of random number generators.” ACM Transactions on Mathematical Software (TOMS) 33, no. 4 (2007): 22. http://simul.iro.umontreal.ca/testu01/tu01.html.
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    Knuth, D.E. Seminumerical Algorithms. The Art of Computer Programming (2nd ed.), Vol. 2, Addison-Wesley, Reading, MA (1981).Google Scholar
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    Bassham III, Lawrence E., Andrew L. Rukhin, Juan Soto, James R. Nechvatal, Miles E. Smid, Elaine B. Barker, Stefan D. Leigh, et al. “Sp 800-22 rev. 1a. a statistical test suite for random and pseudorandom number generators for cryptographic applications.” (2010).Google Scholar
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    Marsaglia, George. “DIEHARD statistical tests.” (CDROM), Florida State University (1995).Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ronald T. Kneusel
    • 1
  1. 1.ThorntonUSA

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