Property based testing in R, inspired by QuickCheck. This package builds on the property based testing framework provided by `hedgehog`

and is designed to seamlessly integrate with `testthat`

.

You can install the released version of `quickcheck`

from CRAN with:

And the development version from GitHub with:

The following example uses `quickcheck`

to test the properties of the base R `+`

function. Here is an introduction to the concept of property based testing, and an explanation of the mathematical properties of addition can be found here.

```
library(testthat)
library(quickcheck)
test_that("0 is the additive identity of +", {
for_all(
a = numeric_(len = 1),
property = function(a) expect_equal(a, a + 0)
)
})
#> Test passed π
test_that("+ is commutative", {
for_all(
a = numeric_(len = 1),
b = numeric_(len = 1),
property = function(a, b) expect_equal(a + b, b + a)
)
})
#> Test passed πΈ
test_that("+ is associative", {
for_all(
a = numeric_(len = 1),
b = numeric_(len = 1),
c = numeric_(len = 1),
property = function(a, b, c) expect_equal(a + (b + c), (a + b) + c)
)
})
#> Test passed π
```

Here we test the properties of the `distinct`

function from the `dplyr`

package.

```
library(dplyr, warn.conflicts = FALSE)
test_that("distinct does nothing with a single row", {
for_all(
a = any_tibble(rows = 1L),
property = function(a) {
distinct(a) %>% expect_equal(a)
}
)
})
#> Test passed π
test_that("distinct returns single row if rows are repeated", {
for_all(
a = any_tibble(rows = 1L),
property = function(a) {
bind_rows(a, a) %>%
distinct() %>%
expect_equal(a)
}
)
})
#> Test passed π
test_that("distinct does nothing if rows are unique", {
for_all(
a = tibble_of(integer_positive(), rows = 1L, cols = 1L),
b = tibble_of(integer_negative(), rows = 1L, cols = 1L),
property = function(a, b) {
unique_rows <- bind_rows(a, b)
distinct(unique_rows) %>% expect_equal(unique_rows)
}
)
})
#> Test passed π
```

Many generators are provided with `quickcheck`

. Here are a few examples.

```
integer_(len = 10) %>% show_example()
#> [1] -833 5111 -8831 -3495 -1899 1051 9964 2473 9557 -2465
character_alphanumeric(len = 10) %>% show_example()
#> [1] "y5Ph" "8" "B8" "3vOcYf" "qr" "o"
#> [7] "5rW2nHdrA" "88" "umU" "vJpqr"
posixct_(len = 10, any_na = TRUE) %>% show_example()
#> [1] "1652-02-25 11:34:40 LMT" "1683-08-15 05:26:47 LMT"
#> [3] "2339-08-19 19:19:07 PDT" "0244-05-09 12:26:30 LMT"
#> [5] "0756-11-24 03:23:10 LMT" "0660-04-16 21:21:08 LMT"
#> [7] "2993-05-14 04:45:47 PDT" NA
#> [9] "1301-04-09 00:40:00 LMT" NA
```

```
list_(a = constant(NULL), b = any_undefined()) %>% show_example()
#> $a
#> NULL
#>
#> $b
#> [1] -Inf
flat_list_of(logical_(), len = 3) %>% show_example()
#> [[1]]
#> [1] TRUE
#>
#> [[2]]
#> [1] TRUE
#>
#> [[3]]
#> [1] TRUE
```

```
tibble_(a = date_(), b = hms_(), rows = 5) %>% show_example()
#> # A tibble: 5 x 2
#> a b
#> <date> <time>
#> 1 1271-08-16 22:32:16.108893
#> 2 2788-05-31 20:37:31.119791
#> 3 1246-05-10 09:14:29.411623
#> 4 2434-06-08 16:01:39.498445
#> 5 1074-10-19 04:07:18.552658
tibble_of(double_bounded(-10, 10), rows = 3, cols = 3) %>% show_example()
#> # A tibble: 3 x 3
#> ...1 ...2 ...3
#> <dbl> <dbl> <dbl>
#> 1 0 2.55 5.81
#> 2 4.42 8.87 -5.43
#> 3 9.45 7.02 -3.97
any_tibble(rows = 3, cols = 3) %>% show_example()
#> # A tibble: 3 x 3
#> ...1 ...2 ...3
#> <list> <list> <date>
#> 1 <named list [2]> <time [2]> 1628-11-24
#> 2 <named list [2]> <time [7]> 2989-06-25
#> 3 <named list [2]> <fct [4]> 2175-02-14
```

`quickcheck`

is meant to work with `hedgehog`

, not replace it. `hedgehog`

generators can be used by wrapping them in `from_hedgehog`

.

```
library(hedgehog)
is_even <-
function(a) a %% 2 == 0
gen_powers_of_two <-
gen.element(1:10) %>% gen.with(function(a) 2^a)
test_that("is_even returns TRUE for powers of two", {
for_all(
a = from_hedgehog(gen_powers_of_two),
property = function(a) is_even(a) %>% expect_true()
)
})
#> Test passed π
```

Any `hedgehog`

generator can be used with `quickcheck`

but they canβt be composed together to build another generator. For example this will work:

```
test_that("powers of two and integers are both numeric values", {
for_all(
a = from_hedgehog(gen_powers_of_two),
b = integer_(),
property = function(a, b) {
c(a, b) %>%
is.numeric() %>%
expect_true()
}
)
})
#> Test passed π
```

But this will cause an error:

```
test_that("composing hedgehog with quickcheck generators fails", {
tibble_of(from_hedgehog(gen_powers_of_two)) %>% expect_error()
})
#> Test passed π₯
```

A `quickcheck`

generator can also be converted to a `hedgehog`

generator which can then be used with other `hedgehog`

functions.

```
gen_powers_of_two <-
integer_bounded(1L, 10L, len = 1L) %>%
as_hedgehog() %>%
gen.with(function(a) 2^a)
test_that("is_even returns TRUE for powers of two", {
for_all(
a = from_hedgehog(gen_powers_of_two),
property = function(a) is_even(a) %>% expect_true()
)
})
#> Test passed π
```

Fuzz testing is a special case of property based testing in which the only property being tested is that the code doesnβt fail with a range of inputs. Here is an example of how to do fuzz testing with `quickcheck`

. Letβs say we want to test that the `purrr::map`

function wonβt fail with any vector as input.

```
test_that("map won't fail with any vector as input", {
for_all(
a = any_vector(),
property = function(a) purrr::map(a, identity) %>% expect_silent()
)
})
#> Test passed π
```

Repeat tests can be used to repeatedly test that a property holds true for many calls of a function. These are different from regular property based tests because they donβt require generators. The function `repeat_test`

will call a function many times to ensure the expectation passes in all cases. This kind of test can be useful for testing functions with randomness.