# Trait proptest::strategy::Strategy
[−]
[src]

pub trait Strategy: Debug { type Value: ValueTree; fn new_value(&self, runner: &mut TestRunner) -> Result<Self::Value, String>; fn prop_map<O: Debug, F: Fn(<Self::Value as ValueTree>::Value) -> O>(

self,

fun: F

) -> Map<Self, F>

where

Self: Sized, { ... } fn prop_flat_map<S: Strategy, F: Fn(<Self::Value as ValueTree>::Value) -> S>(

self,

fun: F

) -> Flatten<Map<Self, F>>

where

Self: Sized, { ... } fn prop_ind_flat_map<S: Strategy, F: Fn(<Self::Value as ValueTree>::Value) -> S>(

self,

fun: F

) -> IndFlatten<Map<Self, F>>

where

Self: Sized, { ... } fn prop_ind_flat_map2<S: Strategy, F: Fn(<Self::Value as ValueTree>::Value) -> S>(

self,

fun: F

) -> IndFlattenMap<Self, F>

where

Self: Sized, { ... } fn prop_filter<F: Fn(&<Self::Value as ValueTree>::Value) -> bool>(

self,

whence: String,

fun: F

) -> Filter<Self, F>

where

Self: Sized, { ... } fn prop_union(self, other: Self) -> Union<Self>

where

Self: Sized, { ... } fn prop_recursive<F: Fn(Arc<BoxedStrategy<<Self::Value as ValueTree>::Value>>) -> BoxedStrategy<<Self::Value as ValueTree>::Value>>(

self,

depth: u32,

desired_size: u32,

expected_branch_size: u32,

recurse: F

) -> Recursive<BoxedStrategy<<Self::Value as ValueTree>::Value>, F>

where

Self: Sized + 'static, { ... } fn boxed(self) -> BoxedStrategy<<Self::Value as ValueTree>::Value>

where

Self: Sized + 'static, { ... } fn no_shrink(self) -> NoShrink<Self>

where

Self: Sized, { ... } }

A strategy for producing arbitrary values of a given type.

`fmt::Debug`

is a hard requirement for all strategies currently due to
`prop_flat_map()`

. This constraint will be removed when specialisation
becomes stable.

## Associated Types

`type Value: ValueTree`

The value tree generated by this `Strategy`

.

This also implicitly describes the ultimate value type governed by the
`Strategy`

.

## Required Methods

`fn new_value(&self, runner: &mut TestRunner) -> Result<Self::Value, String>`

Generate a new value tree from the given runner.

This may fail if there are constraints on the generated value and the
generator is unable to produce anything that satisfies them. Any
failure is wrapped in `TestError::Abort`

.

## Provided Methods

`fn prop_map<O: Debug, F: Fn(<Self::Value as ValueTree>::Value) -> O>(`

self,

fun: F

) -> Map<Self, F> where

Self: Sized,

self,

fun: F

) -> Map<Self, F> where

Self: Sized,

Returns a strategy which produces values transformed by the function
`fun`

.

There is no need (or possibility, for that matter) to define how the output is to be shrunken. Shrinking continues to take place in terms of the source value.

`fn prop_flat_map<S: Strategy, F: Fn(<Self::Value as ValueTree>::Value) -> S>(`

self,

fun: F

) -> Flatten<Map<Self, F>> where

Self: Sized,

self,

fun: F

) -> Flatten<Map<Self, F>> where

Self: Sized,

Maps values produced by this strategy into new strategies and picks values from those strategies.

`fun`

is used to transform the values produced by this strategy into
other strategies. Values are then chosen from the derived strategies.
Shrinking proceeds by shrinking individual values as well as shrinking
the input used to generate the internal strategies.

## Shrinking

In the case of test failure, shrinking will not only shrink the output
from the combinator itself, but also the input, i.e., the strategy used
to generate the output itself. Doing this requires searching the new
derived strategy for a new failing input. The combinator will generate
up to `Config::cases`

values for this search.

As a result, nested `prop_flat_map`

/`Flatten`

combinators risk
exponential run time on this search for new failing values. To ensure
that test failures occur within a reasonable amount of time, all of
these combinators share a single "flat map regen" counter, and will
stop generating new values if it exceeds `Config::max_flat_map_regens`

.

## Example

Generate two integers, where the second is always less than the first, without using filtering:

#[macro_use] extern crate proptest; use proptest::prelude::*; proptest! { #[test] fn test_two( // Pick integers in the 1..65536 range, and derive a strategy // which emits a tuple of that integer and another one which is // some value less than it. (a, b) in (1..65536).prop_flat_map(|a| (Just(a), 0..a)) ) { prop_assert!(b < a); } }

## Choosing the right flat-map

`Strategy`

has three "flat-map" combinators. They look very similar at
first, and can be used to produce superficially identical test results.
For example, the following three expressions all produce inputs which
are 2-tuples `(a,b)`

where the `b`

component is less than `a`

.

use proptest::prelude::*; let flat_map = (1..10).prop_flat_map(|a| (Just(a), 0..a)); let ind_flat_map = (1..10).prop_ind_flat_map(|a| (Just(a), 0..a)); let ind_flat_map2 = (1..10).prop_ind_flat_map2(|a| 0..a);

The three do differ however in terms of how they shrink.

For `flat_map`

, both `a`

and `b`

will shrink, and the invariant that
`b < a`

is maintained. This is a "dependent" or "higher-order" strategy
in that it remembers that the strategy for choosing `b`

is dependent on
the value chosen for `a`

.

For `ind_flat_map`

, the invariant `b < a`

is maintained, but only
because `a`

does not shrink. This is due to the fact that the
dependency between the strategies is not tracked; `a`

is simply seen as
a constant.

Finally, for `ind_flat_map2`

, the invariant `b < a`

is *not*
maintained, because `a`

can shrink independently of `b`

, again because
the dependency between the two variables is not tracked, but in this
case the derivation of `a`

is still exposed to the shrinking system.

The use-cases for the independent flat-map variants is pretty narrow.
For the majority of cases where invariants need to be maintained and
you want all components to shrink, `prop_flat_map`

is the way to go.
`prop_ind_flat_map`

makes the most sense when the input to the map
function is not exposed in the output and shrinking across strategies
is not expected to be useful. `prop_ind_flat_map2`

is useful for using
related values as starting points while not constraining them to that
relation.

`fn prop_ind_flat_map<S: Strategy, F: Fn(<Self::Value as ValueTree>::Value) -> S>(`

self,

fun: F

) -> IndFlatten<Map<Self, F>> where

Self: Sized,

self,

fun: F

) -> IndFlatten<Map<Self, F>> where

Self: Sized,

Maps values produced by this strategy into new strategies and picks values from those strategies while considering the new strategies to be independent.

This is very similar to `prop_flat_map()`

, but shrinking will *not*
attempt to shrink the input that produces the derived strategies. This
is appropriate for when the derived strategies already fully shrink in
the desired way.

In most cases, you want `prop_flat_map()`

.

See `prop_flat_map()`

for a more detailed explanation on how the
three flat-map combinators differ.

`fn prop_ind_flat_map2<S: Strategy, F: Fn(<Self::Value as ValueTree>::Value) -> S>(`

self,

fun: F

) -> IndFlattenMap<Self, F> where

Self: Sized,

self,

fun: F

) -> IndFlattenMap<Self, F> where

Self: Sized,

Similar to `prop_ind_flat_map()`

, but produces 2-tuples with the input
generated from `self`

in slot 0 and the derived strategy in slot 1.

See `prop_flat_map()`

for a more detailed explanation on how the
three flat-map combinators differ differ.

`fn prop_filter<F: Fn(&<Self::Value as ValueTree>::Value) -> bool>(`

self,

whence: String,

fun: F

) -> Filter<Self, F> where

Self: Sized,

self,

whence: String,

fun: F

) -> Filter<Self, F> where

Self: Sized,

Returns a strategy which only produces values accepted by `fun`

.

This results in a very naïve form of rejection sampling and should only
be used if (a) relatively few values will actually be rejected; (b) it
isn't easy to express what you want by using another strategy and/or
`map()`

.

There are a lot of downsides to this form of filtering. It slows
testing down, since values must be generated but then discarded.
Proptest only allows a limited number of rejects this way (across the
entire `TestRunner`

). Rejection can interfere with shrinking;
particularly, complex filters may largely or entirely prevent shrinking
from substantially altering the original value.

Local rejection sampling is still preferable to rejecting the entire
input to a test (via `TestCaseError::Reject`

), however, and the default
number of local rejections allowed is much higher than the number of
whole-input rejections.

`whence`

is used to record where and why the rejection occurred.

`fn prop_union(self, other: Self) -> Union<Self> where`

Self: Sized,

Self: Sized,

Returns a strategy which picks uniformly from `self`

and `other`

.

When shrinking, if a value from `other`

was originally chosen but that
value can be shrunken no further, it switches to a value from `self`

and starts shrinking that.

Be aware that chaining `prop_union`

calls will result in a very
right-skewed distribution. If this is not what you want, you can call
the `.or()`

method on the `Union`

to add more values to the same union,
or directly call `Union::new()`

.

Both `self`

and `other`

must be of the same type. To combine
heterogeneous strategies, call the `boxed()`

method on both `self`

and
`other`

to erase the type differences before calling `prop_union()`

.

`fn prop_recursive<F: Fn(Arc<BoxedStrategy<<Self::Value as ValueTree>::Value>>) -> BoxedStrategy<<Self::Value as ValueTree>::Value>>(`

self,

depth: u32,

desired_size: u32,

expected_branch_size: u32,

recurse: F

) -> Recursive<BoxedStrategy<<Self::Value as ValueTree>::Value>, F> where

Self: Sized + 'static,

self,

depth: u32,

desired_size: u32,

expected_branch_size: u32,

recurse: F

) -> Recursive<BoxedStrategy<<Self::Value as ValueTree>::Value>, F> where

Self: Sized + 'static,

Generate a recursive structure with `self`

items as leaves.

`recurse`

is applied to various strategies that produce the same type
as `self`

with nesting depth *n* to create a strategy that produces the
same type with nesting depth *n+1*. Generated structures will have a
depth between 0 and `depth`

and will usually have up to `desired_size`

total elements, though they may have more. `expected_branch_size`

gives
the expected maximum size for any collection which may contain
recursive elements and is used to control branch probability to achieve
the desired size. Passing a too small value can result in trees vastly
larger than desired.

Note that `depth`

only counts branches; i.e., `depth = 0`

is a single
leaf, and `depth = 1`

is a leaf or a branch containing only leaves.

In practise, generated values usually have a lower depth than `depth`

(but `depth`

is a hard limit) and almost always under
`expected_branch_size`

(though it is not a hard limit) since the
underlying code underestimates probabilities.

## Example

use std::collections::HashMap; #[macro_use] extern crate proptest; use proptest::prelude::*; /// Define our own JSON AST type #[derive(Debug, Clone)] enum JsonNode { Null, Bool(bool), Number(f64), String(String), Array(Vec<JsonNode>), Map(HashMap<String, JsonNode>), } // Define a strategy for generating leaf nodes of the AST let json_leaf = prop_oneof![ Just(JsonNode::Null), prop::bool::ANY.prop_map(JsonNode::Bool), prop::num::f64::ANY.prop_map(JsonNode::Number), ".*".prop_map(JsonNode::String), ]; // Now define a strategy for a whole tree let json_tree = json_leaf.prop_recursive( 4, // No more than 4 branch levels deep 64, // Target around 64 total elements 16, // Each collection is up to 16 elements long |element| prop_oneof![ // NB `element` is an `Arc` and we'll need to reference it twice, // so we clone it the first time. prop::collection::vec(element.clone(), 0..16) .prop_map(JsonNode::Array), prop::collection::hash_map(".*", element, 0..16) .prop_map(JsonNode::Map) ].boxed());

`fn boxed(self) -> BoxedStrategy<<Self::Value as ValueTree>::Value> where`

Self: Sized + 'static,

Self: Sized + 'static,

Erases the type of this `Strategy`

so it can be passed around as a
simple trait object.

`fn no_shrink(self) -> NoShrink<Self> where`

Self: Sized,

Self: Sized,

Wraps this strategy to prevent values from being subject to shrinking.

Suppressing shrinking is useful when testing things like linear
approximation functions. Ordinarily, proptest will tend to shrink the
input to the function until the result is just barely outside the
acceptable range whereas the original input may have produced a result
far outside of it. Since this makes it harder to see what the actual
problem is, making the input `NoShrink`

allows learning about inputs
that produce more incorrect results.

## Implementors

`impl<S: Strategy + ?Sized> Strategy for Box<S>`

`impl<'a, S: Strategy + ?Sized> Strategy for &'a S`

`impl<'a, S: Strategy + ?Sized> Strategy for &'a mut S`

`impl<S: Strategy + ?Sized> Strategy for Rc<S>`

`impl<S: Strategy + ?Sized> Strategy for Arc<S>`

`impl<S: Strategy, O: Debug, F: Fn(<S::Value as ValueTree>::Value) -> O> Strategy for Map<S, F>`

`impl<S: Strategy> Strategy for Flatten<S> where`

<S::Value as ValueTree>::Value: Strategy,`impl<S: Strategy> Strategy for IndFlatten<S> where`

<S::Value as ValueTree>::Value: Strategy,`impl<S: Strategy, R: Strategy, F: Fn(<S::Value as ValueTree>::Value) -> R> Strategy for IndFlattenMap<S, F>`

`impl<S: Strategy, F: Fn(&<S::Value as ValueTree>::Value) -> bool> Strategy for Filter<S, F>`

`impl<T: Strategy> Strategy for Union<T>`

`impl<T: Debug + 'static, F: Fn(Arc<BoxedStrategy<T>>) -> BoxedStrategy<T>> Strategy for Recursive<BoxedStrategy<T>, F>`

`impl<T: Clone + Debug> Strategy for Just<T>`

`impl<T: Strategy> Strategy for NoShrink<T>`

`impl Strategy for proptest::bool::Any`

`impl Strategy for Weighted`

`impl Strategy for proptest::num::i8::Any`

`impl Strategy for Range<i8>`

`impl Strategy for RangeFrom<i8>`

`impl Strategy for RangeTo<i8>`

`impl Strategy for proptest::num::i16::Any`

`impl Strategy for Range<i16>`

`impl Strategy for RangeFrom<i16>`

`impl Strategy for RangeTo<i16>`

`impl Strategy for proptest::num::i32::Any`

`impl Strategy for Range<i32>`

`impl Strategy for RangeFrom<i32>`

`impl Strategy for RangeTo<i32>`

`impl Strategy for proptest::num::i64::Any`

`impl Strategy for Range<i64>`

`impl Strategy for RangeFrom<i64>`

`impl Strategy for RangeTo<i64>`

`impl Strategy for proptest::num::isize::Any`

`impl Strategy for Range<isize>`

`impl Strategy for RangeFrom<isize>`

`impl Strategy for RangeTo<isize>`

`impl Strategy for proptest::num::u8::Any`

`impl Strategy for Range<u8>`

`impl Strategy for RangeFrom<u8>`

`impl Strategy for RangeTo<u8>`

`impl Strategy for proptest::num::u16::Any`

`impl Strategy for Range<u16>`

`impl Strategy for RangeFrom<u16>`

`impl Strategy for RangeTo<u16>`

`impl Strategy for proptest::num::u32::Any`

`impl Strategy for Range<u32>`

`impl Strategy for RangeFrom<u32>`

`impl Strategy for RangeTo<u32>`

`impl Strategy for proptest::num::u64::Any`

`impl Strategy for Range<u64>`

`impl Strategy for RangeFrom<u64>`

`impl Strategy for RangeTo<u64>`

`impl Strategy for proptest::num::usize::Any`

`impl Strategy for Range<usize>`

`impl Strategy for RangeFrom<usize>`

`impl Strategy for RangeTo<usize>`

`impl Strategy for proptest::num::f32::Any`

`impl Strategy for Range<f32>`

`impl Strategy for RangeFrom<f32>`

`impl Strategy for RangeTo<f32>`

`impl Strategy for proptest::num::f64::Any`

`impl Strategy for Range<f64>`

`impl Strategy for RangeFrom<f64>`

`impl Strategy for RangeTo<f64>`

`impl<T: BitSetLike> Strategy for BitSetStrategy<T>`

`impl<A: Strategy> Strategy for (A,)`

`impl<A: Strategy, B: Strategy> Strategy for (A, B)`

`impl<A: Strategy, B: Strategy, C: Strategy> Strategy for (A, B, C)`

`impl<A: Strategy, B: Strategy, C: Strategy, D: Strategy> Strategy for (A, B, C, D)`

`impl<A: Strategy, B: Strategy, C: Strategy, D: Strategy, E: Strategy> Strategy for (A, B, C, D, E)`

`impl<A: Strategy, B: Strategy, C: Strategy, D: Strategy, E: Strategy, F: Strategy> Strategy for (A, B, C, D, E, F)`

`impl<A: Strategy, B: Strategy, C: Strategy, D: Strategy, E: Strategy, F: Strategy, G: Strategy> Strategy for (A, B, C, D, E, F, G)`

`impl<A: Strategy, B: Strategy, C: Strategy, D: Strategy, E: Strategy, F: Strategy, G: Strategy, H: Strategy> Strategy for (A, B, C, D, E, F, G, H)`

`impl<A: Strategy, B: Strategy, C: Strategy, D: Strategy, E: Strategy, F: Strategy, G: Strategy, H: Strategy, I: Strategy> Strategy for (A, B, C, D, E, F, G, H, I)`

`impl<A: Strategy, B: Strategy, C: Strategy, D: Strategy, E: Strategy, F: Strategy, G: Strategy, H: Strategy, I: Strategy, J: Strategy> Strategy for (A, B, C, D, E, F, G, H, I, J)`

`impl<S: Strategy> Strategy for [S; 1]`

`impl<S: Strategy> Strategy for [S; 2]`

`impl<S: Strategy> Strategy for [S; 3]`

`impl<S: Strategy> Strategy for [S; 4]`

`impl<S: Strategy> Strategy for [S; 5]`

`impl<S: Strategy> Strategy for [S; 6]`

`impl<S: Strategy> Strategy for [S; 7]`

`impl<S: Strategy> Strategy for [S; 8]`

`impl<S: Strategy> Strategy for [S; 9]`

`impl<S: Strategy> Strategy for [S; 10]`

`impl<S: Strategy> Strategy for [S; 11]`

`impl<S: Strategy> Strategy for [S; 12]`

`impl<S: Strategy> Strategy for [S; 13]`

`impl<S: Strategy> Strategy for [S; 14]`

`impl<S: Strategy> Strategy for [S; 15]`

`impl<S: Strategy> Strategy for [S; 16]`

`impl<S: Strategy> Strategy for [S; 17]`

`impl<S: Strategy> Strategy for [S; 18]`

`impl<S: Strategy> Strategy for [S; 19]`

`impl<S: Strategy> Strategy for [S; 20]`

`impl<S: Strategy> Strategy for [S; 21]`

`impl<S: Strategy> Strategy for [S; 22]`

`impl<S: Strategy> Strategy for [S; 23]`

`impl<S: Strategy> Strategy for [S; 24]`

`impl<S: Strategy> Strategy for [S; 25]`

`impl<S: Strategy> Strategy for [S; 26]`

`impl<S: Strategy> Strategy for [S; 27]`

`impl<S: Strategy> Strategy for [S; 28]`

`impl<S: Strategy> Strategy for [S; 29]`

`impl<S: Strategy> Strategy for [S; 30]`

`impl<S: Strategy> Strategy for [S; 31]`

`impl<S: Strategy> Strategy for [S; 32]`

`impl<T: Strategy> Strategy for VecStrategy<T>`

`impl<'a> Strategy for CharStrategy<'a>`

`impl Strategy for str`