Package 'fuzzr'

Summarize fuzz test results as a data frame

Description

Summarize fuzz test results as a data frame

Usage

## S3 method for class 'fuzz_results'
as.data.frame(x, ..., delim = "; ")

Arguments

x	Object returned by fuzz_function.
...	Additional arguments to be passed to or from methods.
delim	The delimiter to use for fields like messages or warnings in which there may be multiple results.

Value

A data frame with the following columns:

fuzz_input

The name of the fuzz test performed.

output

Delimited outputs to the command line from the process, if applicable.

messages

Delimited messages, if applicable.

warnings

Delimited warnings, if applicable.

errors

Error returned, if applicable.

value_classes

Delimited classes of the object returned by the function, if applicable

results_index

Index of x from which the summary was produced.

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Fuzz-test a function

Description

Evaluate how a function responds to unexpected or non-standard inputs.

Usage

fuzz_function(fun, arg_name, ..., tests = test_all(), check_args = TRUE,
  progress = interactive())

p_fuzz_function(fun, .l, check_args = TRUE, progress = interactive())

Arguments

fun	A function.
arg_name	Quoted name of the argument to fuzz test.
...	Other non-dynamic arguments to pass to fun. These will be repeated for every one of the tests.
tests	Which fuzz tests to run. Accepts a named list of inputs, defaulting to test_all.
check_args	Check if arg_name and any arguments passed as ... are accepted by fun. Set to FALSE if you need to pass arguments to a function that accepts arguments via ....
progress	Show a progress bar while running tests?
.l	A named list of tests.

Details

fuzz_function provides a simple interface to fuzz test a single argument of a function by passing the function, name of the argument, static values of other required arguments, and a named list of test values.

p_fuzz_function takes a nested list of arguments paired with lists of tests to run on each argument, and will evaluate every combination of argument and provided test.

Value

A fuzz_results object.

Note

The user will be asked to confirm before proceeding if the combinations of potential tests exceeds 500,000.

See Also

fuzz_results and as.data.frame.fuzz_results to access fuzz test results.

Examples

# Evaluate the 'formula' argument of lm, passing additional required variables
fr <- fuzz_function(lm, "formula", data = iris)

# When evaluating a function that takes ..., set check_args to FALSE
fr <- fuzz_function(paste, "x", check_args = FALSE)

# Pass tests to multiple arguments via a named list
test_args <- list(
   data = test_df(),
   subset = test_all(),
   # Specify custom tests with a new named list
   formula = list(all_vars = Sepal.Length ~ ., one_var = mpg ~ .))
fr <- p_fuzz_function(lm, test_args)

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Access individual fuzz test results

Description

Access individual fuzz test results

Usage

fuzz_value(fr, index = NULL, ...)

fuzz_call(fr, index = NULL, ...)

Arguments

fr	fuzz_results object
index	The test index (by position) to access. Same as the results_index in the data frame returned by as.data.frame.fuzz_results.
...	Additional arguments must be named regex patterns that will be used to match against test names. The names of the patterns must match the function argument name(s) whose test names you wish to match.

Functions

• fuzz_value: Access the object returned by the fuzz test

• fuzz_call: Access the call used for the fuzz test

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Fuzz-Test R Functions

Description

Test function arguments with a wide array of inputs, and produce reports summarizing messages, warnings, errors, and returned values.

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Fuzz test inputs

Description

Each test_all returns a named list that concatenates all the available tests specified below.

Usage

test_all()

test_char()

test_int()

test_dbl()

test_lgl()

test_fctr()

test_date()

test_raw()

test_df()

test_null()

Functions

• test_char: Character vectors

  • char_empty: character(0)

  • char_single: "a"

  • char_single_blank: ""

  • char_multiple: c("a", "b", "c")

  • char_multiple_blank: c("a", "b", "c", "")

  • char_with_na: c("a", "b", NA)

  • char_single_na: NA_character_

  • char_all_na: c(NA_character_, NA_character_, NA_character_)

• test_int: Integer vectors

  • int_empty: integer(0)

  • int_single: 1L

  • int_multiple: 1:3

  • int_with_na: c(1L, 2L, NA)

  • int_single_na: NA_integer_

  • int_all_na: c(NA_integer_, NA_integer_, NA_integer_)

• test_dbl: Double vectors

  • dbl_empty: numeric(0)

  • dbl_single: 1.5

  • dbl_mutliple: c(1.5, 2.5, 3.5)

  • dbl_with_na: c(1.5, 2.5, NA)

  • dbl_single_na: NA_real_

  • dbl_all_na: c(NA_real_, NA_real_, NA_real_)

• test_lgl: Logical vectors

  • lgl_empty: logical(0)

  • lgl_single: TRUE

  • lgl_mutliple: c(TRUE, FALSE, FALSE)

  • lgl_with_na: c(TRUE, NA, FALSE)

  • lgl_single_na: NA

  • lgl_all_na: c(NA, NA, NA)

• test_fctr: Factor vectors

  • fctr_empty: structure(integer(0), .Label = character(0), class = "factor")

  • fctr_single: structure(1L, .Label = "a", class = "factor")

  • fctr_multiple: structure(1:3, .Label = c("a", "b", "c"), class = "factor")

  • fctr_with_na: structure(c(1L, 2L, NA), .Label = c("a", "b"), class = "factor")

  • fctr_missing_levels: structure(1:3, .Label = c("a", "b", "c", "d"), class = "factor")

  • fctr_single_na: structure(NA_integer_, .Label = character(0), class = "factor")

  • fctr_all_na: structure(c(NA_integer_, NA_integer_, NA_integer_), .Label = character(0), class = "factor")

• test_date: Date vectors

  • date_single: as.Date("2001-01-01")

  • date_multiple: as.Date(c("2001-01-01", "1950-05-05"))

  • date_with_na: as.Date(c("2001-01-01", NA, "1950-05-05"))

  • date_single_na: as.Date(NA_integer_, origin = "1971-01-01")

  • date_all_na: as.Date(rep(NA_integer_, 3), origin = "1971-01-01")

• test_raw: Raw vectors

  • raw_empty: raw(0)

  • raw_char: as.raw(0x62),

  • raw_na: charToRaw(NA_character_)

• test_df: Data frames

  • df_complete: datasets::iris

  • df_empty: data.frame(NULL)

  • df_one_row: datasets::iris[1, ]

  • df_one_col: datasets::iris[ ,1]

  • df_with_na: iris with several NAs added to each column.

• test_null: Null value

  • null_value: NULL

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