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The ever-increasing need for random numbers is clear in many areas ofcomputer science, from neural networks to optimization. As such, most commonprogramming language provide easy access to Pseudorandom Number Generators.However, these generators are not all made equal, and empirical verificationhas previously shown some to be flawed in key ways. Because of the constantchanges in programming languages, we perform the same empirical benchmarkingusing large batteries of statistcal tests on a wide array of PRNGs, andidentify that while some languages have improved significantly over the years,there are still cases where the default PRNG fails to deliver sufficientlyrandom results.

Benchmarking the Status of Default Pseudorandom Number Generators in Common Programming Languages