Generators are sources of random values. Every randomized test needs a generator to produce its inputs.
These are good default generators for tests over types from OCaml and Base. They are designed to hit corner cases reasonably often, and also generate reasonably good coverage of common cases and arbitrary values.
val string : Base.string t
val bytes : Base.bytes t
val int32 : Base.int32 t
val int63 : Base.Int63.t t
val int64 : Base.int64 t
val nativeint : Base.nativeint t
val float : Base.float t
val sexp : Base.Sexp.t t
val option : 'a t -> 'a Base.option t
val array : 'a t -> 'a Base.array t
val lazy_t : 'a t -> 'a Base.Lazy.t t
val either : 'a t -> 'b t -> ('a, 'b) Base.Either.t t
val result : 'a t -> 'b t -> ('a, 'b) Base.Result.t t
This helper module type exists separately just to
open Bigarray in its scope.
val bigstring : (Base.char, Bigarray.int8_unsigned_elt, Bigarray.c_layout) Bigarray.Array1.t t
val float32_vec : (Base.float, Bigarray.float32_elt, Bigarray.fortran_layout) Bigarray.Array1.t t
val float64_vec : (Base.float, Bigarray.float64_elt, Bigarray.fortran_layout) Bigarray.Array1.t t
val float32_mat : (Base.float, Bigarray.float32_elt, Bigarray.fortran_layout) Bigarray.Array2.t t
val float64_mat : (Base.float, Bigarray.float64_elt, Bigarray.fortran_layout) Bigarray.Array2.t t
Generates random functions that use the given observer to perturb the pseudo-random state that is then used to generate the output value. The resulting functions are therefore deterministic, assuming the observer is deterministic.
val map_t_m : ('key, 'cmp) Base.Comparator.Module.t -> 'key t -> 'data t -> ('key, 'data, 'cmp) Base.Map.t t
val set_t_m : ('elt, 'cmp) Base.Comparator.Module.t -> 'elt t -> ('elt, 'cmp) Base.Set.t t
val map_tree_using_comparator : comparator:('key, 'cmp) Base.Comparator.t -> 'key t -> 'data t -> ('key, 'data, 'cmp) Base.Map.Using_comparator.Tree.t t
val set_tree_using_comparator : comparator:('elt, 'cmp) Base.Comparator.t -> 'elt t -> ('elt, 'cmp) Base.Set.Using_comparator.Tree.t t
Combining and Modifying Generators
Chooses among the given generators, weighted uniformly; then chooses a value from that generator.
include Base.Applicative.S with type 'a t := 'a t
module Applicative_infix : sig ... end
include Base.Monad.S with type 'a t := 'a t
t >>= f returns a computation that sequences the computations represented by two monad elements. The resulting computation first does
t to yield a value
v, and then runs the computation returned by
module Monad_infix : sig ... end
val return : 'a -> 'a t
return v returns the (trivial) computation that returns v.
ignore_m t is
map t ~f:(fun _ -> ()).
ignore_m used to be called
ignore, but we decided that was a bad name, because it shadowed the widely used
Caml.ignore. Some monads still do
let ignore = ignore_m for historical reasons.
all, but ensures that every monadic value in the list produces a unit value, all of which are discarded rather than being collected into a list.
module Let_syntax : sig ... end
These are convenient to have in scope when programming with a monad:
Size of Random Values
Base_quickcheck threads a size parameter through generators to limit the size of unbounded types. Users of Base_quickcheck often do not need to think about the size parameter; the default generators handle it sensibly. Generators of atomic types ignore it, generators of bounded-size containers like
either thread it through unchanged, and generators of unbounded-size containers like
set_t_m distribute the size they are given among their constituents.
The bindings below allow direct manipulation of the size parameter in cases where users want a custom treatment of sizes. There is no prescribed meaning of the size parameter for any given type other than that it must be non-negative. As a general guideline, however, the time and space used to generate a value should be proportional to the size parameter at most.
The size parameter should be treated as an upper bound but not as a lower bound, so for example a generator given a size parameter of 2 should have a chance to generate values of size 0 or 1 as well. If the size parameter is treated as a lower bound, then for example members of tuples will always be generated at the same size, and test cases for members of different size will not be covered.
Produces a generator that ignores the size parameter passed in by Base_quickcheck and instead uses the given
~size argument. Most often used with
size to reduce the size when dispatching to generators for subparts of a value.
For example, here is a use of
size to create a generator for optional lists. We are careful to generate
None even at non-zero sizes; see the note above about not using
size as a lower bound.
let optional_list generator = let open Let_syntax in match%bind both size bool with | (0, _) | (_, false) -> return None | k, _ -> let%map elements = with_size ~size:(k-1) (list generator) in Some elements
Produces a list of sizes that distribute the current size among list elements. The
max_length parameters can be used to bound the length of the result.
This is the distribution used by generators such as
list to divide up size among elements.
This function is designed so that elements of
list are always generated at strictly smaller size than the list itself. The technical invariant is: if
size_list is generated by
with_size ~size:n (sizes ~min_length ()), then:
(List.length size_list - min_length) + (List.sum (module Int) size_list) <= n
Produces values for which
false, retries with
size incremented by 1. This avoids
filter getting stuck if all values at a given size fail
f; see the note above about not using
size as a lower bound.
val filter_map : 'a t -> f:('a -> 'b Base.option) -> 'b t
Some x, produces
None, retries with
size incremented by 1, as with
Generating Recursive Values
Ties the recursive knot to produce generators for recursive types that have multiple clauses, separating base cases from recursive cases. At size 0, only base cases are produced; at size
n > 0, the base cases are produced at size
n along with the recursive cases at size
n-1. Raises if the list of base cases is empty or if the list of recursive cases is empty.
For example, here is a use of
recursive_union to create a generator for an expression datatype.
type exp = | Int of int | Bool of bool | If of exp * exp * exp | Add of exp * exp let exp_generator = recursive_union [ map int ~f:(fun i -> Int i); map bool ~f:(fun b -> Bool b); ] ~f:(fun exp -> let open Let_syntax in [ (let%map a = exp and b = exp and c = exp in If (a, b, c)); (let%map a = exp and b = exp in Add (a, b)); ])
recursive_union, without separate clauses or automatic size management. Useful for generating recursive types that don't fit the clause structure of
For example, here is a use of
fixed_point to create a generator for N-ary trees. No manual size management is needed, as
Generator.list guarantees to generate list elements at strictly smaller sizes than the list itself.
type tree = Node of tree list let tree_generator = fixed_point (fun tree -> map (list tree) ~f:(fun trees -> Node trees))
val of_lazy : 'a t Base.Lazy.t -> 'a t
t that forces the lazy argument as necessary. Can be used to tie (mutually) recursive knots.
Custom Random Distributions
val of_weighted_list : (Base.float * 'a) Base.list -> 'a t
Produces one of the given values, chosen with the corresponding weight. Weights must be non-negative and must have a strictly positive sum.
val weighted_union : (Base.float * 'a t) Base.list -> 'a t
Produces one of the given generators, chosen with the corresponding weight, then chooses a value from that generator. Weights must be non-negative and must have a strictly positive sum.
val weighted_recursive_union : (Base.float * 'a t) Base.list -> f:('a t -> (Base.float * 'a t) Base.list) -> 'a t
recursive_union, with explicit weights for each clause. Weights must be non-negative and the recursive case weights must have a strictly positive sum.
Produces an integer between 0 and an unspecified upper bound which is proportional to
size. This is a good generator to use for sizes of values like strings which have a variable number of fixed-size elements.
small_positive_or_zero_int but with a minimum of
Uniform Unbounded Distributions
These generators produce any value of the relevant integer type with uniform weight. The default generators for these types differ in that they give higher weight to corner cases, e.g.
val int32_uniform : Base.int32 t
val int63_uniform : Base.Int63.t t
val int64_uniform : Base.int64 t
val nativeint_uniform : Base.nativeint t
These generators produce any value between the given inclusive bounds, which must be given in nondecreasing order. Higher weight is given to corner cases, e.g. the bounds themselves.
val int32_inclusive : Base.int32 -> Base.int32 -> Base.int32 t
val int63_inclusive : Base.Int63.t -> Base.Int63.t -> Base.Int63.t t
val int64_inclusive : Base.int64 -> Base.int64 -> Base.int64 t
val nativeint_inclusive : Base.nativeint -> Base.nativeint -> Base.nativeint t
Uniform Bounded Distributions
These generators produce any value between the given inclusive bounds, which must be given in nondecreasing order. All values are given equal weight.
val int32_uniform_inclusive : Base.int32 -> Base.int32 -> Base.int32 t
val int63_uniform_inclusive : Base.Int63.t -> Base.Int63.t -> Base.Int63.t t
val int64_uniform_inclusive : Base.int64 -> Base.int64 -> Base.int64 t
val nativeint_uniform_inclusive : Base.nativeint -> Base.nativeint -> Base.nativeint t
Uniform in Log Space Distributions
These generators produce any value between the given inclusive, non-negative bounds, choosing bit-length in that range uniformly and then uniformly among values with that bit-length between the bounds. The bounds must be given in nondecreasing order.
val int32_log_uniform_inclusive : Base.int32 -> Base.int32 -> Base.int32 t
val int63_log_uniform_inclusive : Base.Int63.t -> Base.Int63.t -> Base.Int63.t t
val int64_log_uniform_inclusive : Base.int64 -> Base.int64 -> Base.int64 t
val nativeint_log_uniform_inclusive : Base.nativeint -> Base.nativeint -> Base.nativeint t
Log Space Distributions
*_log_uniform_inclusive bindings above, but giving additional weight to corner cases, e.g. the given bounds.
val int32_log_inclusive : Base.int32 -> Base.int32 -> Base.int32 t
val int63_log_inclusive : Base.Int63.t -> Base.Int63.t -> Base.Int63.t t
val int64_log_inclusive : Base.int64 -> Base.int64 -> Base.int64 t
val nativeint_log_inclusive : Base.nativeint -> Base.nativeint -> Base.nativeint t
Floating Point Distributions
val float_inclusive : Base.float -> Base.float -> Base.float t
Generates values between the given bounds, inclusive, which must be finite and in nondecreasing order. Weighted toward boundary values.
val float_uniform_exclusive : Base.float -> Base.float -> Base.float t
Generates values between the given bounds, exclusive, which must be finite and in increasing order, with at least one float value between them. Weighted approximately uniformly across the resulting range, rounding error notwithstanding.
val float_without_nan : Base.float t
val float_finite : Base.float t
val float_strictly_positive : Base.float t
val float_strictly_negative : Base.float t
val float_positive_or_zero : Base.float t
val float_negative_or_zero : Base.float t
val float_of_class : Base.Float.Class.t -> Base.float t
val string_non_empty : Base.string t
val string_with_length : length:Base.int -> Base.string t
val string_of : Base.char t -> Base.string t
val string_non_empty_of : Base.char t -> Base.string t
val string_with_length_of : Base.char t -> length:Base.int -> Base.string t
val sexp_of : Base.string t -> Base.Sexp.t t
Produces s-expressions whose atoms are chosen from the given string distribution.
Randomly drops elements from a list. The length of each result is chosen uniformly between 0 and the length of the input, inclusive.
Produces permutations of the given list, weighted uniformly.
These functions provide direct access to the pseudo-random state threaded through Base_quickcheck generators. Most users should not need these functions.
Passes in additional "salt" used to perturb the pseudo-random state used to generate random values. Generators' output is intended to be deterministic for any initial pseudorandom state, so
perturb can be used to generate a new generator with the same distribution that nonetheless produces different values from the original for any given pseudo-random state.
val create : (size:Base.int -> random:Splittable_random.State.t -> 'a) -> 'a t
Creates a generator that calls the given function with the current size parameter and pseudorandom state.
val generate : 'a t -> size:Base.int -> random:Splittable_random.State.t -> 'a
Generates a random value using the given size and pseudorandom state. Useful when using
create and dispatching to other existing generators.
module Debug : sig ... end