randomprng

Randomprng is a term commonly used to refer to a pseudo-random number generator (PRNG) in software development. A randomprng produces sequences of numbers that appear random but are generated by a deterministic algorithm from an initial seed. Because the sequence is fully determined by the seed, it is repeatable, which is useful for debugging, simulations, and reproducible research. Unless a randomprng is explicitly described as cryptographically secure, it should not be used for security-sensitive tasks.

A randomprng typically works by updating an internal state with a mathematical function and then deriving output values from that state. The quality of a randomprng is judged by how uniformly its outputs are distributed, how independent successive values seem, and the length of its period—the number of values before the sequence begins to repeat. Good PRNG design also minimizes correlations between successive outputs and provides a straightforward seeding mechanism.

Common algorithms used in randomprng implementations include linear congruential generators, Mersenne Twister, Xorshift, PCG, and ChaCha-based generators. Some are favored for speed and low memory usage, while others emphasize statistical properties. Modern libraries often offer a default randomprng alongside options to reseed with true entropy for improved unpredictability in non-cryptographic contexts.

Usage of a randomprng generally involves seeding with a value or entropy source, then requesting integers or floating-point values in specified ranges. Reproducibility is achieved by using the same seed. In multi-threaded environments, separate instances or thread-safe designs help avoid unintended correlations between streams.

In practice, randomprngs are suitable for simulations, randomized algorithms, and games, but for cryptographic keys or security-sensitive operations, a cryptographically secure PRNG (CSPRNG) is required.