Minitest-Proptest allows tests to be expressed in terms of universal properties, and will generate test cases to try to disprove them the cases listed automatically. This library is heavily inspired by QuickCheck and Hypothesis.

Tests should be written to express a universal property in the simplest manner achievable. Properties can be expressed via property within any context where an assertion is allowed.

Property tests should aim to express universal properties of the code under test - the framework will generate test cases to try to find counter-examples to properties listed.

Tests should usually follow the following sequence:

1. Allocate arbitrary primitives
2. Build necessary data structures from the primitives
3. Provide predicates to filter out inappropriate data generation
4. Express assertions and return a boolean

• All Minitest assertions are available within a property. They will conveniently return a boolean.
• The block provided to the property method is an implicit assertion - the assertion will fail if the return value is not truthy.

A trivial example can be seen below:

class PropertyTest < Minitest::Test
  def test_average_list
    # Allocate a list of Integers
    xs = arbitrary Array, UInt8

    # The list must contain at least one value
    where { !xs.empty? }

    # Calculate the list's average
    average = xs.reduce(&:+) / xs.length.to_f

    # Conclude the block with the core assertion of universal property:
    xs.min <= average <= xs.max
  end
end

This can also be expressed with assert:

class PropertyTest < Minitest::Test
  def test_average_list
    # Allocate a list of Integers
    xs = arbitrary Array, UInt8

    # The list must contain at least one value
    where { !xs.empty? }

    # Calculate the list's average
    average = xs.reduce(&:+) / xs.length.to_f

    # Conclude the block with the core assertion of universal property:
    assert xs.min <= average <= xs.max
  end
end

Using Minitest assertions is particularly useful when a long chain of logic would otherwise be necessary to express a success case.

In order for tests and shrinking to work as expected, the two following tips will set a test writer up for success:

• Allocate everything you might need up front
• Allocate everything you need in the same place

Allocating everything that will be needed up front avoids the possibility of predicating allocation on other allocations. This breaks the shrinking logic and must be avoided.

Allocating everything in the same place makes it easier to parse counter-examples when they're found.

The following types are provided by Minitest-Proptest:

• Unbounded Integer
• Unsigned Integers
  • UInt8
  • UInt16
  • UInt32
  • UInt64
• Signed Integers
  • Int8
  • Int16
  • Int32
  • Int64
• Floating Point
  • Float32
  • Float64
  • Float - identical to Float64
• Complex numbers
• Rational numbers
• Text types
  • Char - any single character 0x00-0xff
  • ASCIIChar - any character 0x00-0x7f
  • String - arbitrary length string of Chars
• Bool
• Polymorphic types
  • Array a - array of arbitrary length of another type
  • Hash a b - hash of arbitrary size from type a to type b
  • Range a - range between two values whose classes implement <=>

Generators provide the machinery through which arbitrary values are provided, scored, and shrunk when a counterexample is found. It is typically only relatively primitive data which require generators, while non-primitive objects are generally constructed from arbitrary data.

Writing high quality generators for primitive types can make or break property tests' ability to provide reliable results. While a large number of built-in primitive generators are provided, cases will arise for which additional generators are required to improve tests' clarity.

Generators are written with the generator_for method, which is available globally. The simplest case is to create a generator for a type which generates data directly.

• The sized method provides a random Integer from 0 to the provided value, inclusive. This provides direct access to entropy for the purpose of primitive generation.

  BoxedUInt8 = Struct.new(:value)
  generator_for(BoxedUInt8) do
    i = sized(0xff)
    BoxedUInt8.new(i)
  end
  
  # ...
  boxed = arbitrary BoxedUInt8

• The one_of method will produce a random selection of a set of values. The set may be given as any collection which implements to_a. This provides indirect access to entropy via the selection of value.

  Dice = Struct.new(:value)
  generator_for(Dice) do
    Dice.new(one_of(1..6))
  end
  
  # ...
  one_d_six = arbitrary Dice

Finding counter-examples is made more helpful when the counter-examples are able to be meaningfully reduced to an easier failure case about which to reason. Built-in generators which provide numeric values will tend towards zero as they are shrunk (n.b. Float types will consider NaN and Infinity as having an equal score to 0), and types which are list-like will try to drop elements while shrinking the elements' respective values. The default behavior of any custom generator will be to convert the value to an integer via to_i and take its absolute value.

If a scoring and shrink function are provided to the generator, any failure condition will be retried in forms which are generated according to the functions in question until the score closest to zero is obtained.

The BoxedUInt8 definition can be rewritten for shrinking with the following shrink and score functions:

BoxedUInt8 = Struct.new(:value)

generator_for(BoxedUInt8) do
  i = sized(0xff)
  BoxedUInt8.new(i)
end.with_shrink_function do |i|
  candidates = []
  y = i.value

  until y.zero?
    candidates << BoxedUInt8.new(i.value - y)
    candidates << BoxedUInt8.new(y)
    y = (y / 2.0).to_i
  end

  candidates
end.with_score_function do |i|
  i.value
end

In cases where a type might be inhabited by various other types, the block provided to generator_for can take arguments. This block will be treated as equivalent to a curried constructor, e.g.

Twople = Struct.new(:fst, :snd) do
generator_for(Twople) do |fst, snd|
  Twople.new(fst, snd)
end.with_shrink_function do |ffst, fsnd, t|
  f_candidates = ffst.call(t.fst)
  s_candidates = fsnd.call(t.snd)

  f_candidates.reduce([]) do |candidates, fst|
    candidates + s_candidates.map { |snd| Twople.new(fst, snd) }
  end
end.with_score_function do |ffst, fsnd, t|
  ffst.call(t.fst) + fsnd.call(t.snd)
end

# ...
tuple = arbitrary Twople, Int8, Int8

Note that the functions for shrinking and scoring should be parameterized to accept functions for shrinking or scoring the contained data. These can be used to further refine the generation of minimal counterexamples.

In cases where a given datatype can be arbitrarily sized and it's useful to generate varying sizes of data for tests, functions for appending and generating an empty/initial state of the datatype with the with_append and with_empty methods respectively.

The append and empty functions for generating Arrays can serve as a reasonable example:

generator_for(Array) do |x|
  [x]
end.with_append(0, 0x10) do |xs, ys|
  xs + ys
end.with_empty { [] }

Note that with_append requires a minimum and maximum size which is acceptable to generate to be supplied.

Additional examples of generators are available in lib/minitest/proptest/gen.rb

Minitest-Proptest is designed to work with Minitest 5.0 or greater. Non-EOL Rubies are tested to work; older rubies may work as well.

Minitest-Proptest is released under the MIT License

Whether the contribution concerns code, documentation, bug reports, or something else entirely, contributions are welcome and appreciated!

If the contribution is relating to a security concern, please see SECURITY.md.

For all other contributions, please see CONTRIBUTING.md. In short:

• Fork the project.
• Add tests for any new functionality/to verify that bugs have been fixed.
• Send a pull request on GitHub.

The Minitest-Proptest project is governed by a Code of Conduct.