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Property-based testing for Ruby (ala QuickCheck)

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README.md

Propr

Property-based testing for Ruby (ala QuickCheck). Build Status

Introduction

The usual approach to testing software is to describe a set of test inputs and their corresponding expected outputs. The program is run with these inputs and the actual outputs are compared with the expected outputs to ensure the program behaves as expected. This methodology is simple to implement and automate, but suffers from problems like:

  • Writing test cases is tedious.
  • Non-obvious edge cases aren't tested.
  • Code coverage tools alone don't provide much assurance.

Property-based testing is an alternative, and complementary, approach in which the general relationships between program inputs and desired output are expressed, rather than enumerating particular inputs and outputs. The properties specify things like, "assuming the program is correct, when its run with any valid inputs, the inputs and the program output are related by f(input, output)". The test framework produces random (valid) inputs, searching for a counterexample.

Installation

There are a few things I'd like to fix before publishing this as a gem. Until then, you can install directly from the git repo using Bundler, with this in your Gemfile:

gem "propr", git: "git@github.com:kputnam/propr.git", branch: "rewrite"

You'll probably want to specify the current tag, also (eg, ..., tag: "v0.2.0")

Properties

The following example demonstrates testing a property with a specific input, then generalizing the test for any input.

describe Array do
  include Propr::RSpec

  describe "#+" do
    # Traditional unit test
    it "sums lengths" do
      xs = [100, 200, 300]
      ys = [400, 500]
      (xs + ys).length.should == xs.length + ys.length
    end

    # Property-based test
    property("sums lengths"){|xs, ys| (xs + ys).length == xs.length + ys.length }
      .check([100, 200, 300], [500, 200])
      .check{ sequence [Array.random { Integer.random }, Array.random { Integer.random }] }
  end
end

The following example is similar, but contains an error in specification

describe Array do
  include Propr::RSpec

  describe "#|" do
    # Traditional unit test
    it "sums lengths" do
      xs = [100, 200, 300]
      ys = [400, 500]
      (xs | ys).length.should == xs.length + ys.length
    end

    # Property-based test
    property("sums lengths"){|xs, ys| (xs | ys).length == xs.length + ys.length }
      .check([100, 200, 300], [400, 500])
      .check{ sequence [Array.random{Integer.random(min:0, max:50)}]*2 }
  end
end

When this specification is executed, the following error is reported.

$ rake spec
..F

Failures:

  1) Array#| sums lengths
     Failure/Error: raise Falsifiable.new(counterex, m.shrink(counterex), passed, skipped)
     Propr::Falsifiable:
       input:    [25, 24], [24, 27]
       shrunken: [], [0, 0]
       after: 49 passed, 0 skipped
     # ./lib/propr/rspec.rb:29:in `block in check'

Finished in 0.22829 seconds
3 examples, 1 failure

You may have figured out the error is that | removes duplicate elements from the result. We might not have caught the mistake by writing individual test cases. The output indicates Propr generated 49 sets of input before finding one that failed.

Now that a failing test case has been identified, you might write another check with those specific inputs to prevent regressions.

You could also print the initial random seed like this and when a test fails, explicitly set the random seed to regenerate the same inputs for the entire test suite:

$ cat spec/spec_helper.rb
RSpec.configure do |config|
  srand.tap{|seed| puts "Random seed is #{seed}"; srand seed }
end

$ rake spec
Random seed is 146211424375622429406889408197139382303
..F

Failures:

  1) Array#| sums lengths
     Failure/Error: raise Falsifiable.new(counterex, m.shrink(counterex), passed, skipped)
     Propr::Falsifiable:
       input:    [25, 24], [24, 27]
       shrunken: [], [0, 0]
       after: 49 passed, 0 skipped

Finished in 0.22829 seconds
3 examples, 1 failure

Now change spec_helper.rb to explicitly set the random seed:

$ cat spec/spec_helper.rb
RSpec.configure do |config|
  srand 146211424375622429406889408197139382303
  srand.tap{|seed| puts "Random seed is #{seed}"; srand seed }
end

$ rake spec
Random seed is 146211424375622429406889408197139382303

The remaining output should be identical every time you run the suite, so long as specs are in the same order each time.

Just Plain Functions

Properties are basically just functions, they should return true or false.

p = Propr::Property.new("name", lambda{|a,b| a + b == b + a })

You can invoke a property using #check. Like lambdas and procs, you can also invoke them using #call or #[].

p.check(3, 4)     #=> true
p.check("x", "y") #=> false

But you can also invoke them by yielding a function that generates random inputs.

m = Propr::Random
p.check { m.eval(m.sequence [Integer.random, Float.random]) }  #=> true
p.check { m.eval(m.sequence [String.random , String.random]) } #=> false

When invoked with a block, check will run p with 100 random inputs by default, but you can also pass an argument to check indicating how many examples p should be tested against.

Using Propr + Test Frameworks

Mixing in a module magically defines the property singleton method, so you can use it to generate test cases.

describe "foo" do
  include Propr::RSpec

  # This defines three test cases, one per each `check`
  property("length"){|a| a.length >= 0 }
    check("abc").
    check("xyz").
    check{ String.random }
end

Note your property should still return true or false. You should not use #should or #assert, because the test generator will generate the assertion for you. This also reduces visual clutter.

Alternatively, to use Propr with all specification, you can add this to your spec_helper.rb

RSpec.configure do |config|
  include Propr::RSpec
end

Property DSL

The code block inside property { ... } has an extended scope that defines a few helpful methods:

  • guard: Skip this iteration unless all the given conditions are met. This can be used, for instance, to define a property only on even integers.
    property{|x| guard(x.even?); x & 1 == 0 }

  • error?: True if the code block throws an exception of the given type.
    property{|x| error? { x / 0 }}

  • m: Short alias for Propr::Random, used to generate random data as described below.
    property{|x| m.eval(m.sequence([m.unit 0] * x)).length == x }

Check DSL

The code block inside check { ... } should return a generator value. The code block's scope is extended with a few combinators to compose generators.

  • unit: Create a generator that returns the given value. For instance, to yield 3 as an argument to the property,
    check { unit(3) }

  • bind: Chain the value yielded by one generator into another. For instance, to yield two integers as arguments to a property,
    check { bind(Integer.random){|a| bind(Integer.random){|b| unit([a,b]) }}}

  • guard: Short-circuit the chain if the given condition is false. The entire chain will be re-run until the guard passes. For instance, to generate two distinct numbers,
    check { bind(Integer.random){|a| bind(Integer.random){|b| guard(a != b){ unit([a,b]) }}}}

  • join: Remove one level of generator nesting. If you have a generator x that yields a number generator, then join x is a number generator. For instance, to yield either a number or a string,
    check { join([Integer.random, String.random].random) }

  • sequence: Convert a list of generator values to a list generator. For instance, to yield three integers to a property,
    check { sequence [Integer.random]*3 }

Generating Random Values

Propr defines a random method that returns a generator for most standard Ruby types. You can run the generator using the Propr::Random.eval method.

>> m = Propr::Random
=> ...

>> m.eval(Boolean.random)
=> false

Boolean

>> m.eval Boolean.random
=> true

Date

>> m.eval(Date.random(min: Date.today - 10, max: Date.today + 10)).to_s
=> "2012-03-01"

Options

  • min: minimum value, defaults to 0001-01-01
  • max: maximum value, defaults to 9999-12-31
  • center: defaults to the midpoint between min and max

Time

>> m.eval Time.random(min: Time.now, max: Time.now + 3600)
=> 2012-02-20 13:47:57 -0600

Options

  • min: minimum value, defaults to 1000-01-01 00:00:00 UTC
  • max: maximum value, defaults to 9999-12-31 12:59:59 UTC
  • center: defaults to the midpoint between min and max

String

>> m.eval String.random(min: 5, max: 10, charset: :lower)
=> "rqyhw"

Options

  • min: minimum size, defaults to 0
  • max: maximum size, defaults to 10
  • center: defaults to the midpoint between min and max
  • charset: regular expression character class, defaults to /[[:print]]/

Numbers

Integer.random

>> m.eval Integer.random(min: -500, max: 500)
=> -382

Options

  • min: minimum value, defaults to Integer::MIN
  • max: maximum value, defaults to Integer::MAX
  • center: defaults to the midpoint between min and max.

Float.random

>> m.eval Float.random(min: -500, max: 500)
=> 48.252030464134364

Options

  • min: minimum value, defaults to -Float::MAX
  • max: maximum value, defaults to Float::MAX
  • center: defaults to the midpoint between min and max.

Rational.random

>> m.eval m.bind(m.sequence [Integer.random]*2){|a,b| unit Rational(a,b) }
=> (300421843/443649464)

Not implemented, as there isn't a nice way to ensure a min works. Instead, generate two numeric values and combine them:

BigDecimal.random

>> m.eval(BigDecimal.random(min: 10, max: 20)).to_s("F")
=> "14.934854011762374703280016489856414847259220844969789892"

Options

  • min: minimum value, defaults to -Float::MAX
  • max: maximum value, defaults to Float::MAX
  • center: defaults to the midpoint between min and max

Bignum.random

>> m.eval Integer.random(min: Integer::MAX, max: Integer::MAX * 2)
=> 2015151263

There's no constructor specifically for Bignum. You can use Integer.random and specify min: Integer::MAX + 1 and some even larger max value. Ruby will automatically handle Integer overflow by coercing to Bignum.

Complex.random

>> m.eval(m.bind(m.sequence [Float.random(min:-10, max:10)]*2){|a,b| m.unit Complex(a,b) })
=> (9.806161068637833+7.523520738439842i)

Not implemented, as there's no simple way to implement min and max, nor the types of the components. Instead, generate two numeric values and combine them:

Collections

The class method random returns a generator to construct a collection of elements, while the #random instance method returns a generator which returns an element from the collection.

Array.random

Expects a block parameter that yields a generator for elements.

>> m.eval Array.random(min:4, max:4) { String.random(min:4, max:4) }
=> ["2n #", "UZ1d", "0vF,", "cV_{"]

Options

  • min: minimum size, defaults to 0
  • max: maximum size, defaults to 10
  • center: defaults to the midpoint between min and max

Hash.random

Expects a block parameter that yields generator of [key, value] pairs.

>> m.eval Hash.random(min:2, max:4) { m.sequence [Integer.random, m.unit(nil)] }
=> {564854752=>nil, -1065292239=>nil, 830081146=>nil}

Options

  • min: minimum size, defaults to 0
  • max: maximum size, defaults to 10
  • center: defaults to the midpoint between min and max

Hash.random_vals

Expects a hash whose keys are ordinary values, and whose values are generators.

>> m.eval Hash.random_vals(a: String.random, b: Integer.random)
=> {:a=>"Fi?p`g", :b=>4551738453396095365} 

Doesn't accept any options.

Set.random

Expects a block parameter that yields a generator for elements.

>> m.eval Set.random(min:4, max:4) { String.random(min:4, max:4) }
=> #<Set: {"2n #", "UZ1d", "0vF,", "cV_{"}>

Options

  • min: minimum size, defaults to 0
  • max: maximum size, defaults to 10
  • center: defaults to the midpoint between min and max

Range.random

Expects either a block parameter or one or both of min and max.

>> m.eval Range.random(min: 0, max: 100)
=> 81..58

>> m.eval Range.random { Integer.random(min: 0, max: 100) }
=> 9..80

Options

  • min: minimum element
  • max: maximum element
  • inclusive?: defaults to true, meaning Range includes max element

Elements from a collection

The #random instance method is defined on the above types. It takes no parameters.

>> m.eval([1,2,3,4,5].random)
=> 4

>> m.eval({a: 1, b: 2, c: 3, d: 4}.random)
=> [:b, 2]

>> m.eval((0..100).random)
=> 12

>> m.eval(Set.new([1,2,3,4]).random)
=> 4

Search Space Attenuation

The m.eval method has a second parameter that serves to exponentially reduce the domain for generators, specified with min: and max: parameters. The scale value may range from 0 to 1, where 1 causes no change.

When scale is 0, the domain is reduced to a single value, which is specified by the center: parameter. Usually this defaults to the midpoint between min: and max:. Any value between min: and max: can be given for center:, in addition to the three symbolic values, :min, :mid, and :max.

Scale values beteween 0 and 1 adjust the domain exponentially, so a domain with 10,000 elements when scale = 1 will have 1,000 elements when scale = 0.5 and only 100 when scale = 0.25.

With scale = 0, the domain contains at most 10000^0 = 1 elements:

>> m.eval Integer.random(min: 0, max: 10000, center: :min), 0
== m.eval Integer.random(min: 0, max: 0)

>> m.eval Integer.random(min: 0, max: 10000, center: :mid), 0
== m.eval Integer.random(min: 5000, max: 5000)

>> m.eval Integer.random(min: 0, max: 10000, center: :max), 0
== m.eval Integer.random(min: 10000, max: 10000)

With scale = 0.25, the domain contains at most 10000^0.25 = 10 elements:

>> m.eval Integer.random(min: 0, max: 10000, center: :min), 0.25
== m.eval Integer.random(min: 0, max: 9)

>> m.eval Integer.random(min: 0, max: 10000, center: :mid), 0.25
== m.eval Integer.random(min: 4996, max: 5004)

>> m.eval Integer.random(min: 0, max: 10000, center: :max), 0.25
== m.eval Integer.random(min: 9991, max: 10000)

With scale = 0.50, the domain contains at most 10000^0.5 = 100 elements:

>> m.eval Integer.random(min: 0, max: 10000, center: :min), 0.5
== m.eval Integer.random(min: 0, max: 99)

>> m.eval Integer.random(min: 0, max: 10000, center: :mid), 0.5
== m.eval Integer.random(min: 4951, max: 5048)

>> m.eval Integer.random(min: 0, max: 10000, center: :max), 0.5
== m.eval Integer.random(min: 9901, max: 10000)

With scale = 0.75, the domain contains at most 10000^0.75 = 1000 elements:

>> m.eval Integer.random(min: 0, max: 10000, center: :min), 0.75
== m.eval Integer.random(min: 0, max: 998)

>> m.eval Integer.random(min: 0, max: 10000, center: :mid), 0.75
== m.eval Integer.random(min: 4507, max: 5499)

>> m.eval Integer.random(min: 0, max: 10000, center: :max), 0.75
== m.eval Integer.random(min: 9002, max: 10000)

Deepening of the Search Space

By default, the test framework adapters increase the scale linearly (causing an exponential increase of the domain size) each time the property is tested.

That is, when running 100 iterations, scale values will be 0.00, 0.01, 0.02, 0.03, 0.04, etc. This is intended to test the simplest counterexamples first, and increase the complexity of generated inputs exponentially.

Simplification of Counterexamples

Once a random input has been classified as a counterexample, Propr will search for a simpler counterexample. This is done by iteratively calling #shrink on each successively smaller counterexample.

$ cat shrink.example
require "rspec"
require "propr"

RSpec.configure do |config|
  include Propr::RSpec

  srand 146211424375622429406889408197139382303
  srand.tap{|seed| puts "Random seed is #{seed}"; srand seed }
end

describe Float do
  property("assoc"){|x,y,z| (x + y) + z == x + (y + z) }
    .check(-382863.98514407175, 224121.21177705095, 276118.77134001954)
end

$ rspec shrink.example
Random seed is 146211424375622429406889408197139382303
F

Failures:

  1) Float assoc
     Propr::Falsifiable:
       input:    -382863.98514407175, 224121.21177705095, 276118.77134001954
       shrunken: -0.007740960460133677, 0.011895728563551701, 3.9765678826328424e-05
       after: 0 passed, 0 skipped
     # ./lib/propr/rspec.rb:36:in `block in check'

Finished in 10.52 seconds
1 example, 1 failure

Notice the output shows a "simpler" counterexample than the inputs we explicitly tested. This becomes useful when testing with more complex data like trees, where it can be difficult to understand which aspect of the counterexample is relevant.

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