Filtering
Sometimes, you have a case where your input values have some sort of “irregular” constraint on them. For example, an integer needing to be even, or two values needing to be nonequal.
In general, the ideal solution is to find a way to take a seed value and
then use prop_map
to transform it into the desired, irregular domain. For
example, to generate even integers, use something like
#![allow(unused)] fn main() { extern crate proptest; use proptest::prelude::*; prop_compose! { // Generate arbitrary integers up to half the maximum desired value, // then multiply them by 2, thus producing only even integers in the // desired range. fn even_integer(max: i32)(base in 0..max/2) > i32 { base * 2 } } }
For the cases where this is not viable, it is possible to filter strategies. Proptest actually divides filters into two categories:

“Local” filters apply to a single strategy. If a value is rejected, a new value is drawn from that strategy only.

“Global” filters apply to the whole test case. If the test case is rejected, the whole thing is regenerated.
The distinction is somewhat arbitrary, since something like a “global filter” could be created by just putting a “local filter” around the whole input strategy. In practise, the distinction is as to what code performs the rejection.
A local filter is created with the prop_filter
combinator. Besides a
function indicating whether to accept the value, it also takes a value of
type &'static str
, String
, .., which it uses to record where/why the
rejection happened.
extern crate proptest; use proptest::prelude::*; proptest! { #[test] fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this fn some_test( v in (0..1000u32) .prop_filter("Values must not divisible by 7 xor 11", v !((0 == v % 7) ^ (0 == v % 11))) ) { assert_eq!(0 == v % 7, 0 == v % 11); } } fn main() { some_test(); }
Global filtering results when a test itself returns
Err(TestCaseError::Reject)
. The
prop_assume!
macro provides an easy way to do this.
extern crate proptest; use proptest::prelude::*; fn frob(a: i32, b: i32) > (i32, i32) { let d = (a  b).abs(); (a / d, b / d) } proptest! { #[test] fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this fn test_frob(a in 1000..1000, b in 1000..1000) { // Input illegal if a==b. // Equivalent to // if (a == b) { return Err(TestCaseError::Reject(...)); } prop_assume!(a != b); let (a2, b2) = frob(a, b); assert!(a2.abs() <= a.abs()); assert!(b2.abs() <= b.abs()); } } fn main() { test_frob(); }
While useful, filtering has a lot of disadvantages:

Since it is simply rejection sampling, it will slow down generation of test cases since values need to be generated additional times to satisfy the filter. In the case where a filter always returns false, a test could theoretically never generate a result.

Proptest tracks how many local and global rejections have happened, and aborts if they exceed a certain number. This prevents a test taking an extremely long time due to rejections, but means not all filters are viable in the default configuration. The limits for local and global rejections are different; by default, proptest allows a large number of local rejections but a fairly small number of global rejections, on the premise that the former are cheap but potentially common (having been built into the strategy) but the latter are expensive but rare (being an edge case in the particular test).

Shrinking and filtering do not play well together. When shrinking, if a value winds up being rejected, there is no pass/fail information to continue shrinking properly. Instead, proptest treats such a rejection the same way it handles a shrink that results in a passing test: by backing away from simplification with a call to
complicate()
. Thus encountering a filter rejection during shrinking prevents shrinking from continuing to any simpler values, even if there are some that would be accepted by the filter.