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🏠 Advanced arbitraries

⚠️ Before diving into the topic of advanced arbitraries, it is highly recommended to have in mind the built-in arbitraries coming with fast-check.

This documentation covers the definition of new arbitraries. It can be an arbitrary derived from some existing ones or a totally new one.

Please do not hesitate to open issues to ask for new arbitraries.

Derive existing arbitraries

All generated arbitraries inherit from the same base class: Arbitrary.

It comes with two useful methods: filter(predicate: (t: T) => boolean): Arbitrary<T> and map<U>(mapper: (t: T) => U): Arbitrary<U>. These methods are used internally by the framework to derive some Arbitraries from existing ones.

Additionaly it comes with noShrink() which derives an existing Arbitrary<T> into the same Arbitrary<T> without the shrink option.

Filter values

filter(predicate: (t: T) => boolean): Arbitrary<T> can be used to filter undesirable values from the generated ones. It can be used as some kind of pre-requisite for the parameters required for your algorithm. For instance, you might need to generate two ordered integer values. One approach can be to use filter as follow:

const minMax = fc.tuple(fc.integer(), fc.integer())
                    .filter(t => t[0] < t[1]);

But be aware that using filter may highly impact the time required to generate a valid entry. In the previous example, half of the generated tuples will be rejected. It can nontheless be a very useful and powerful tool to derive your arbitraries quickly and easily.

Transform values

map<U>(mapper: (t: T) => U): Arbitrary<U> in its side does not filter any of the generated entries. It take one entry (generated or shrinked) and map it to another.

For instance the previous example could have been refactored as follow:

const minMax = fc.tuple(fc.integer(), fc.integer())
                    .map(t => t[0] < t[1] ? [t[0], t[1]] : [t[1], t[0]]);

Another example would be to derive fc.integer() and fc.array() to build fc.char() and fc.string():

const char = () => fc.integer(0x20, 0x7e).map(String.fromCharCode);
const string = () => fc.array(fc.char()).map(arr => arr.join(''));

Most of the built-in arbitraries use this trick to define themselves.

Transform arbitraries

chain<U>(fmapper: (t: T) => Arbitrary<U>): Arbitrary<U> (aka flatMap) It takes one entry from an Arbitrary and uses it to create a new Arbitrary based on that value.

⚠️ Be aware that the shrinker of such construct might not be able to shrink as much as possible

For example you can create arbitraries based on generated values:

// generate an array of strings, all having the same length.
const RandomFixedLengthStringArb: Arbitrary<string[]> =
      .chain(length => fc.array(fc.string(length, length)));

// generate an array of 2-element arrays containing integer pairs
const BoundedPairsArb: Arbitrary<[number, number][]> = fc.nat().chain(bound => fc.array(fc.integer().map((leftBound: number): [number, number] => [leftBound, leftBound+bound])));

// generate a random sized substring of a string
const StringAndSubstringArb: Arbitrary<[string, string]> = fc.string(3,100).chain(fulltext => fc.tuple(fc.nat(fulltext.length-1), fc.nat(fulltext.length-1)).map(indexes => [fulltext, fulltext.slice(indexes[0], indexes[1]) ]))

Remove the shrinker

Calling noShrink() on an Arbitrary<T> just remove the shrinker of the Arbitrary<T>. For instance, the following code will produce an Arbitrary<number> without shrinking operation.

const intNoShrink = fc.integer().noShrink();

Build your own

NOTE: Before writing your own arbitrary from scratch you should have a look to the examples provided in the repository. There are examples for: recursive structures, properties for automata or state machine and others.

You can also fully customize your arbitrary: not derive it from any of the buit-in arbitraries. What you have to do is to extend Arbitrary and implement generate(mrng: Random): Shrinkable<T>.

generate is responsable for the generation of one new random entity of type T (see signature above). In order to fulfill it in a deterministic way it receives a mrng: Random:

  • next(n: number): number: uniformly distributed n bits value (max value of n = 31)
  • nextBoolean(): boolean: uniformly distributed boolean value
  • nextInt(): number: uniformly distributed integer value
  • nextInt(from: number, to: number): number: uniformly distributed integer value between from (inclusive) and to (inclusive)
  • nextDouble(): number: uniformly distributed double value between 0.0 (included) and 1.0 (not included)

The generated value is responsible for its shrinking. Shrinking derives the item into smaller values and is optional.

Refer to built-in types for examples of fully custom arbitraries.

Advanced features of arbitraries

Biased arbitraries

Property based testing framework must be able to discover any kind of issues even very rare ones happening on some small values. For instance your algorithm might use magic numbers such as -1, 0 or others. Or fail when the input has duplicated values...

A common way to deal with those issues is:

  • Solution A: only generate small values - Issue: it fails to build large ones
  • Solution B: generate larger and larger entries - Issue: what if the failing case requires both large and small values

The choice made by fast-check is to bias the arbitrary 1 time over freq.

For fc.integer:

  • 1 over freq: arbitrary between smaller values
  • remaining: the full range arbitrary

For fc.array:

  • 1 over freq:
    • 1 over freq: small array with biased values
    • remaining: full range array with biased values
  • remaining: the full range arbitrary


A basic way to implement a property based testing framework is to define arbitraries with the following structure:

interface DummyArbitrary<Ts> {
    generate(mrng: Random): Ts;
    shrink(prev: Ts): Stream<Ts>;

Some frameworks actually use this approach. Unfortunately using this approach makes it impossible to shrink oneof. Indeed as soon as you have generated your value you do not know anymore who produced you.

Let's imagine you are using a oneof(integer(0, 10), integer(20, 30)) relying on the DummyArbitrary<number> above. As soon as you have generated a value - a number - you cannot call shrink anymore as you do not know if it has been produced by integer(0, 10) or integer(20, 30) - in this precise case you can easily infer the producer.

For this reason, the shrink method is not part of Arbitrary in fast-check but is part of the values instantiated by generate.


Any generated value having a key for fc.cloneMethod would be handled a bit differently during the execution. Indeed those values explicitly requires to be cloned before being transmitted again to the predicate.

Cloneable values can be seen as stateful values that would be altered as soon as we use them inside the predicate. For this precise reason they have to be recreated if they need to be used inside other runs of the predicate.

Example of usages:

  • fc.context: is a stateful instance that gathers all the logs for a given predicate execution. In order to provide only the logs linked to the run itself it has to be cloned between all the runs
  • stream structure

Example of a stream arbitrary:

const streamInt = fc.nat()
    .map(seed => {
        return Object.assign(
            new SeededRandomStream(seed),
            { [fc.cloneMethod]: () => new SeededRandomStream(seed) }
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