Automatic mutation testing of R packages. Mutation in the sense of
mutating inputs (parameters) to function calls. autotest primarily
works by scraping documented examples for all functions, and mutating
the parameters input to those functions.
The easiest way to install this package is via the associated
r-universe.
As shown there, simply enable the universe with
options (repos = c (
ropenscireviewtools = "https://ropensci-review-tools.r-universe.dev",
CRAN = "https://cloud.r-project.org"
))And then install the usual way with,
install.packages ("autotest")Alternatively, the package can be installed by running one of the following lines:
# install.packages("remotes")
remotes::install_git ("https://git.sr.ht/~mpadge/autotest")
remotes::install_bitbucket ("mpadge/autotest")
remotes::install_gitlab ("mpadge/autotest")
remotes::install_github ("ropensci-review-tools/autotest")The package can then be loaded the usual way:
library (autotest)The simply way to use the package is
x <- autotest_package ("<package>")The main argument to the autotest_package()
function
can either be the name of an installed package, or a path to a local
directory containing the source for a package. The result is a
data.frame of errors, warnings, and other diagnostic messages issued
during package auotest-ing. The function has an additional parameter,
functions, to restrict tests to specified functions only.
By default,
autotest_package()
returns a list of all tests applied to a package without actually
running them. To implement those tests, set the parameter test to
TRUE. Results are only returned for tests in which functions do not
behave as expected, whether through triggering errors, warnings, or
other behaviour as described below. The ideal behaviour of
autotest_package() is to return nothing (or strictly, NULL),
indicating that all tests passed successfully. See the main package
vignette for
an introductory tour of the package.
The package includes a function which lists all tests currently implemented.
autotest_types ()
#> # A tibble: 27 × 8
#> type test_name fn_name parameter parameter_type operation content test
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl>
#> 1 dummy rect_as_other <NA> <NA> rectangular Convert … "check… TRUE
#> 2 dummy rect_compare_… <NA> <NA> rectangular Convert … "expec… TRUE
#> 3 dummy rect_compare_… <NA> <NA> rectangular Convert … "expec… TRUE
#> 4 dummy rect_compare_… <NA> <NA> rectangular Convert … "expec… TRUE
#> 5 dummy extend_rect_c… <NA> <NA> rectangular Extend e… "(Shou… TRUE
#> 6 dummy replace_rect_… <NA> <NA> rectangular Replace … "(Shou… TRUE
#> 7 dummy vector_to_lis… <NA> <NA> vector Convert … "(Shou… TRUE
#> 8 dummy vector_custom… <NA> <NA> vector Custom c… "(Shou… TRUE
#> 9 dummy double_is_int <NA> <NA> numeric Check wh… "int p… TRUE
#> 10 dummy trivial_noise <NA> <NA> numeric Add triv… "(Shou… TRUE
#> # ℹ 17 more rowsThat functions returns a tibble
describing 27 unique tests. The default behaviour of
autotest_package()
with test = FALSE uses these test types to identify which tests will
be applied to each parameter and function. The table returned from
autotest_types()
can be used to selectively switch tests off by setting values in the
test column to FALSE, as demonstrated below.
The package works by scraping documented examples from all .Rd help
files, and using those to identify the types of all parameters to all
functions. Usage therefore first requires that the usage of all
parameters be demonstrated in example code.
As described above, tests can also be selectively applied to particular
functions through the parameters functions, used to nominate functions
to include in tests, or exclude, used to nominate functions to exclude
from tests. The following code illustrates.
x <- autotest_package (package = "stats", functions = "var", test = FALSE)
#>
#> ── autotesting stats ──
#>
#> ✔ [1 / 6]: var
#> ✔ [2 / 6]: cor
#> ✔ [3 / 6]: cor
#> ✔ [4 / 6]: cov
#> ✔ [5 / 6]: cov
#> ✔ [6 / 6]: cor
print (x)
#> # A tibble: 170 × 9
#> type test_name fn_name parameter parameter_type operation content test
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl>
#> 1 warning par_is_demo… var use <NA> Check th… Exampl… TRUE
#> 2 warning par_is_demo… cov y <NA> Check th… Exampl… TRUE
#> 3 dummy trivial_noi… var x numeric Add triv… (Shoul… TRUE
#> 4 dummy vector_cust… var x vector Custom c… (Shoul… TRUE
#> 5 dummy vector_to_l… var x vector Convert … (Shoul… TRUE
#> 6 dummy negate_logi… var na.rm single logical Negate d… (Funct… TRUE
#> 7 dummy subst_int_f… var na.rm single logical Substitu… (Funct… TRUE
#> 8 dummy subst_char_… var na.rm single logical Substitu… should… TRUE
#> 9 dummy single_par_… var na.rm single logical Length 2… Should… TRUE
#> 10 dummy return_succ… var (return … (return objec… Check th… <NA> TRUE
#> # ℹ 160 more rows
#> # ℹ 1 more variable: yaml_hash <chr>Testing the var function also tests cor and cov, because these are
all documented within a single .Rd help file. Typing ?var shows that
the help topic is cor, and that the examples include the three
functions, var, cor, and cov. That result details the 170 tests
which would be applied to the var function from the stats package.
These 170 tests yield the following results when actually applied:
y <- autotest_package (package = "stats", functions = "var", test = TRUE)
#> ── autotesting stats ──
#>
#> ✔ [1 / 6]: var
#> ✔ [2 / 6]: cor
#> ✔ [3 / 6]: cor
#> ✔ [4 / 6]: cov
#> ✔ [5 / 6]: cov
#> ✔ [6 / 6]: cor
print (y)
#> # A tibble: 25 × 9
#> type test_name fn_name parameter parameter_type operation content test
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl>
#> 1 warning par_is_d… var use <NA> Check th… "Examp… TRUE
#> 2 warning par_is_d… cov y <NA> Check th… "Examp… TRUE
#> 3 diagnostic vector_t… var x vector Convert … "Funct… TRUE
#> 4 diagnostic subst_in… var na.rm single logical Substitu… "(Func… TRUE
#> 5 diagnostic vector_t… var x vector Convert … "Funct… TRUE
#> 6 diagnostic vector_t… var y vector Convert … "Funct… TRUE
#> 7 diagnostic single_c… cor use single charac… upper-ca… "is ca… TRUE
#> 8 diagnostic single_c… cor method single charac… upper-ca… "is ca… TRUE
#> 9 diagnostic vector_c… cor x vector Custom c… "Funct… TRUE
#> 10 diagnostic vector_c… cor x vector Custom c… "Funct… TRUE
#> # ℹ 15 more rows
#> # ℹ 1 more variable: yaml_hash <chr>And only 25 of the original 170 tests produced unexpected behaviour. There were in fact only 5 kinds of tests which produced these 25 results:
unique (y$operation)
#> [1] "Check that parameter usage is demonstrated"
#> [2] "Convert vector input to list-columns"
#> [3] "Substitute integer values for logical parameter"
#> [4] "upper-case character parameter"
#> [5] "Custom class definitions for vector input"One of these involves conversion of a vector to a list-column
representation (via I(as.list(<vec>))). Relatively few packages accept
this kind of input, even though doing so is relatively straightforward.
The following lines demonstrate how these tests can be switched off when
autotest-ing a package. The autotest_types() function, used above to
extract information on all types of tests, also accepts a single
argument listing the test_name entries of any tests which are to be
switched off.
types <- autotest_types (notest = "vector_to_list_col")
y <- autotest_package (
package = "stats", functions = "var",
test = TRUE, test_data = types
)
#> ── autotesting stats ──
#>
#> ✔ [1 / 6]: var
#> ✔ [2 / 6]: cor
#> ✔ [3 / 6]: cor
#> ✔ [4 / 6]: cov
#> ✔ [5 / 6]: cov
#> ✔ [6 / 6]: cor
print (y)
#> # A tibble: 22 × 9
#> type test_name fn_name parameter parameter_type operation content test
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl>
#> 1 warning par_is_d… var use <NA> Check th… Exampl… TRUE
#> 2 warning par_is_d… cov y <NA> Check th… Exampl… TRUE
#> 3 diagnostic subst_in… var na.rm single logical Substitu… (Funct… TRUE
#> 4 diagnostic single_c… cor use single charac… upper-ca… is cas… TRUE
#> 5 diagnostic single_c… cor method single charac… upper-ca… is cas… TRUE
#> 6 diagnostic vector_c… cor x vector Custom c… Functi… TRUE
#> 7 diagnostic vector_c… cor x vector Custom c… Functi… TRUE
#> 8 diagnostic single_c… cor method single charac… upper-ca… is cas… TRUE
#> 9 diagnostic single_c… cor use single charac… upper-ca… is cas… TRUE
#> 10 diagnostic single_c… cor use single charac… upper-ca… is cas… TRUE
#> # ℹ 12 more rows
#> # ℹ 1 more variable: yaml_hash <chr>Those tests are still returned from autotest_package(), but with
test = FALSE to indicate they were not run, and a type of “no_test”
rather than the previous “diagnostic”.
Not yet, but that should be possible soon. In the meantime, there are
testthat expectations, listed in the
main package
functions,
which enable autotest to be used in a package’s test suite.
- The
great-expectationsframework for python, described in this medium article. QuickCheckfor Haskellmutatefor rubymutantfor mutation of R code itself
Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
All contributions to this project are gratefully acknowledged using the
allcontributors
package following the
all-contributors specification.
Contributions of any kind are welcome!
mpadge |
helske |
maelle |
simpar1471 |
noamross |
njtierney |
JeffreyRStevens |
bbolker |
mattfidler |
kieranjmartin |
statnmap |
vgherard |
christophsax |
joelnitta |
santikka |
gilbertocamara |
schneiderpy |