# Random Number Generation

To understand randomized testing, we need to take a brief digression into random number generation.

Most languages provide the facility to generate random numbers. In truth, these generators are usually not truly random (in the sense that they are completely unpredictable) but in fact are pseudorandom: the sequence of numbers they generate pass good statistical tests to ensure there is no discernible pattern in them, but the sequence itself is a deterministic function of an initial seed value. (Recall that the prefix pseudo is from the Greek pseudēs meaning "false".) Java and Python both provide pseudorandom number generators (PRNGs). So does OCaml in the standard library's Random module.

## An Experiment

Start a new session of utop and enter the following:

# Random.int 100;;
# Random.int 100;;
# Random.int 100;;


Write down the responses that you get. Each is a pseudorandom integer $$i$$ such that $$0 \leq i \lt 100$$.

Now quit utop and start another new session. Enter the same phrases as above again. You will get the same responses as last time. Unless your OCaml installation is different from the VM's, they will be: 44, 85, 82. Chances are that everyone in the class will get those same numbers. Not exactly unpredictable, eh?

## Pseudorandom Generators

Although for purposes of security and cryptography a PRNG leads to terrible vulnerabilities, for other purposes—including testing and simulation—PRNGs are just fine. In fact their predictability can even be useful: given the same initial seed, a PRNG will always produce the same sequence of pseudorandom numbers, leading to the ability to repeat a particular sequence of tests or a particular simulation.

The way a PRNG works in general is that it initializes a state that it keeps internally from the initial seed. From then on, each time the PRNG generates a new value, it imperatively updates that state. The Random module in fact makes it possible to manipulate that state in limited ways. For example, you can

• get the current state with Random.get_state,
• duplicate the current state with Random.State.copy,
• request a random int generated from a particular state with Random.State.int, and
• initialize the state yourself. The functions Random.self_init and Random.State.make_self_init will choose a "random" seed to initialize the state. They do so by sampling from a special Unix file named /dev/urandom, which is meant to provide as close to true randomness as a computer can.

## Repeating the Experiment

Start a new session of utop. Enter the following:

# Random.self_init ();;
# Random.int 100;;
# Random.int 100;;
# Random.int 100;;


Now do that a second time (it doesn't matter whether you exit utop or not in between). You will notice that you get a different sequence of values.