Parent

Included Modules

MiniTest::Unit::TestCase

Subclass TestCase to create your own tests. Typically you’ll want a TestCase subclass per implementation class.

See MiniTest::Assertions

Public Class Methods

add_setup_hook(arg=nil, &block) click to toggle source

Adds a block of code that will be executed before every TestCase is run. Equivalent to setup, but usable multiple times and without re-opening any classes.

All of the setup hooks will run in order after the setup method, if one is defined.

The argument can be any object that responds to call or a block. That means that this call,

MiniTest::TestCase.add_setup_hook { puts "foo" }

… is equivalent to:

module MyTestSetup
  def call
    puts "foo"
  end
end

MiniTest::TestCase.add_setup_hook MyTestSetup

The blocks passed to add_setup_hook take an optional parameter that will be the TestCase instance that is executing the block.

# File lib/minitest/unit.rb, line 1111
def self.add_setup_hook arg=nil, &block
  hook = arg || block
  @setup_hooks << hook
end
add_teardown_hook(arg=nil, &block) click to toggle source

Adds a block of code that will be executed after every TestCase is run. Equivalent to teardown, but usable multiple times and without re-opening any classes.

All of the teardown hooks will run in reverse order after the teardown method, if one is defined.

The argument can be any object that responds to call or a block. That means that this call,

MiniTest::TestCase.add_teardown_hook { puts "foo" }

… is equivalent to:

module MyTestTeardown
  def call
    puts "foo"
  end
end

MiniTest::TestCase.add_teardown_hook MyTestTeardown

The blocks passed to add_teardown_hook take an optional parameter that will be the TestCase instance that is executing the block.

# File lib/minitest/unit.rb, line 1160
def self.add_teardown_hook arg=nil, &block
  hook = arg || block
  @teardown_hooks << hook
end
bench_exp(min, max, base = 10) click to toggle source

Returns a set of ranges stepped exponentially from min to max by powers of base. Eg:

bench_exp(2, 16, 2) # => [2, 4, 8, 16]
# File lib/minitest/benchmark.rb, line 22
def self.bench_exp min, max, base = 10
  min = (Math.log10(min) / Math.log10(base)).to_i
  max = (Math.log10(max) / Math.log10(base)).to_i

  (min..max).map { |m| base ** m }.to_a
end
bench_linear(min, max, step = 10) click to toggle source

Returns a set of ranges stepped linearly from min to max by step. Eg:

bench_linear(20, 40, 10) # => [20, 30, 40]
# File lib/minitest/benchmark.rb, line 35
def self.bench_linear min, max, step = 10
  (min..max).step(step).to_a
rescue LocalJumpError # 1.8.6
  r = []; (min..max).step(step) { |n| r << n }; r
end
bench_range() click to toggle source

Specifies the ranges used for benchmarking for that class. Defaults to exponential growth from 1 to 10k by powers of 10. Override if you need different ranges for your benchmarks.

See also: ::bench_exp and ::bench_linear.

# File lib/minitest/benchmark.rb, line 63
def self.bench_range
  bench_exp 1, 10_000
end
benchmark_suites() click to toggle source

Returns all test suites that have benchmark methods.

# File lib/minitest/benchmark.rb, line 52
def self.benchmark_suites
  TestCase.test_suites.reject { |s| s.benchmark_methods.empty? }
end
i_suck_and_my_tests_are_order_dependent!() click to toggle source

Call this at the top of your tests when you absolutely positively need to have ordered tests. In doing so, you’re admitting that you suck and your tests are weak.

# File lib/minitest/unit.rb, line 1027
def self.i_suck_and_my_tests_are_order_dependent!
  class << self
    define_method :test_order do :alpha end
  end
end

Public Instance Methods

assert_performance(validation, &work) click to toggle source

Runs the given work, gathering the times of each run. Range and times are then passed to a given validation proc. Outputs the benchmark name and times in tab-separated format, making it easy to paste into a spreadsheet for graphing or further analysis.

Ranges are specified by ::bench_range.

Eg:

def bench_algorithm
  validation = proc { |x, y| ... }
  assert_performance validation do |n|
    @obj.algorithm(n)
  end
end
# File lib/minitest/benchmark.rb, line 85
def assert_performance validation, &work
  range = self.class.bench_range

  io.print "#{__name__}"

  times = []

  range.each do |x|
    GC.start
    t0 = Time.now
    instance_exec(x, &work)
    t = Time.now - t0

    io.print "\t%9.6f" % t
    times << t
  end
  io.puts

  validation[range, times]
end
assert_performance_constant(threshold = 0.99, &work) click to toggle source

Runs the given work and asserts that the times gathered fit to match a constant rate (eg, linear slope == 0) within a given threshold. Note: because we’re testing for a slope of 0, R^2 is not a good determining factor for the fit, so the threshold is applied against the slope itself. As such, you probably want to tighten it from the default.

See www.graphpad.com/curvefit/goodness_of_fit.htm for more details.

Fit is calculated by fit_linear.

Ranges are specified by ::bench_range.

Eg:

def bench_algorithm
  assert_performance_constant 0.9999 do |n|
    @obj.algorithm(n)
  end
end
# File lib/minitest/benchmark.rb, line 129
def assert_performance_constant threshold = 0.99, &work
  validation = proc do |range, times|
    a, b, rr = fit_linear range, times
    assert_in_delta 0, b, 1 - threshold
    [a, b, rr]
  end

  assert_performance validation, &work
end
assert_performance_exponential(threshold = 0.99, &work) click to toggle source

Runs the given work and asserts that the times gathered fit to match a exponential curve within a given error threshold.

Fit is calculated by fit_exponential.

Ranges are specified by ::bench_range.

Eg:

def bench_algorithm
  assert_performance_exponential 0.9999 do |n|
    @obj.algorithm(n)
  end
end
# File lib/minitest/benchmark.rb, line 155
def assert_performance_exponential threshold = 0.99, &work
  assert_performance validation_for_fit(:exponential, threshold), &work
end
assert_performance_linear(threshold = 0.99, &work) click to toggle source

Runs the given work and asserts that the times gathered fit to match a straight line within a given error threshold.

Fit is calculated by fit_linear.

Ranges are specified by ::bench_range.

Eg:

def bench_algorithm
  assert_performance_linear 0.9999 do |n|
    @obj.algorithm(n)
  end
end
# File lib/minitest/benchmark.rb, line 175
def assert_performance_linear threshold = 0.99, &work
  assert_performance validation_for_fit(:linear, threshold), &work
end
assert_performance_power(threshold = 0.99, &work) click to toggle source

Runs the given work and asserts that the times gathered curve fit to match a power curve within a given error threshold.

Fit is calculated by fit_power.

Ranges are specified by ::bench_range.

Eg:

def bench_algorithm
  assert_performance_power 0.9999 do |x|
    @obj.algorithm
  end
end
# File lib/minitest/benchmark.rb, line 195
def assert_performance_power threshold = 0.99, &work
  assert_performance validation_for_fit(:power, threshold), &work
end
fit_error(xys) click to toggle source

Takes an array of x/y pairs and calculates the general R^2 value.

See: en.wikipedia.org/wiki/Coefficient_of_determination

# File lib/minitest/benchmark.rb, line 204
def fit_error xys
  y_bar  = sigma(xys) { |x, y| y } / xys.size.to_f
  ss_tot = sigma(xys) { |x, y| (y    - y_bar) ** 2 }
  ss_err = sigma(xys) { |x, y| (yield(x) - y) ** 2 }

  1 - (ss_err / ss_tot)
end
fit_exponential(xs, ys) click to toggle source

To fit a functional form: y = ae^(bx).

Takes x and y values and returns [a, b, r^2].

See: mathworld.wolfram.com/LeastSquaresFittingExponential.html

# File lib/minitest/benchmark.rb, line 219
def fit_exponential xs, ys
  n     = xs.size
  xys   = xs.zip(ys)
  sxlny = sigma(xys) { |x,y| x * Math.log(y) }
  slny  = sigma(xys) { |x,y| Math.log(y)     }
  sx2   = sigma(xys) { |x,y| x * x           }
  sx    = sigma xs

  c = n * sx2 - sx ** 2
  a = (slny * sx2 - sx * sxlny) / c
  b = ( n * sxlny - sx * slny ) / c

  return Math.exp(a), b, fit_error(xys) { |x| Math.exp(a + b * x) }
end
fit_linear(xs, ys) click to toggle source

Fits the functional form: a + bx.

Takes x and y values and returns [a, b, r^2].

See: mathworld.wolfram.com/LeastSquaresFitting.html

# File lib/minitest/benchmark.rb, line 241
def fit_linear xs, ys
  n   = xs.size
  xys = xs.zip(ys)
  sx  = sigma xs
  sy  = sigma ys
  sx2 = sigma(xs)  { |x|   x ** 2 }
  sxy = sigma(xys) { |x,y| x * y  }

  c = n * sx2 - sx**2
  a = (sy * sx2 - sx * sxy) / c
  b = ( n * sxy - sx * sy ) / c

  return a, b, fit_error(xys) { |x| a + b * x }
end
fit_power(xs, ys) click to toggle source

To fit a functional form: y = ax^b.

Takes x and y values and returns [a, b, r^2].

See: mathworld.wolfram.com/LeastSquaresFittingPowerLaw.html

# File lib/minitest/benchmark.rb, line 263
def fit_power xs, ys
  n       = xs.size
  xys     = xs.zip(ys)
  slnxlny = sigma(xys) { |x, y| Math.log(x) * Math.log(y) }
  slnx    = sigma(xs)  { |x   | Math.log(x)               }
  slny    = sigma(ys)  { |   y| Math.log(y)               }
  slnx2   = sigma(xs)  { |x   | Math.log(x) ** 2          }

  b = (n * slnxlny - slnx * slny) / (n * slnx2 - slnx ** 2);
  a = (slny - b * slnx) / n

  return Math.exp(a), b, fit_error(xys) { |x| (Math.exp(a) * (x ** b)) }
end
io() click to toggle source
# File lib/minitest/unit.rb, line 1007
def io
  @__io__ = true
  MiniTest::Unit.output
end
io?() click to toggle source
# File lib/minitest/unit.rb, line 1012
def io?
  @__io__
end
passed?() click to toggle source

Returns true if the test passed.

# File lib/minitest/unit.rb, line 1064
def passed?
  @passed
end
run(runner) click to toggle source

Runs the tests reporting the status to runner

# File lib/minitest/unit.rb, line 962
def run runner
  trap "INFO" do
    time = runner.start_time ? Time.now - runner.start_time : 0
    warn "%s#%s %.2fs" % [self.class, self.__name__, time]
    runner.status $stderr
  end if SUPPORTS_INFO_SIGNAL

  result = ""
  begin
    @passed = nil
    self.setup
    self.run_setup_hooks
    self.__send__ self.__name__
    result = "." unless io?
    @passed = true
  rescue *PASSTHROUGH_EXCEPTIONS
    raise
  rescue Exception => e
    @passed = false
    result = runner.puke self.class, self.__name__, e
  ensure
    begin
      self.run_teardown_hooks
      self.teardown
    rescue *PASSTHROUGH_EXCEPTIONS
      raise
    rescue Exception => e
      result = runner.puke self.class, self.__name__, e
    end
    trap 'INFO', 'DEFAULT' if SUPPORTS_INFO_SIGNAL
  end
  result
end
setup() click to toggle source

Runs before every test. Use this to refactor test initialization.

# File lib/minitest/unit.rb, line 1071
def setup; end
sigma(enum, &block) click to toggle source

Enumerates over enum mapping block if given, returning the sum of the result. Eg:

sigma([1, 2, 3])                # => 1 + 2 + 3 => 7
sigma([1, 2, 3]) { |n| n ** 2 } # => 1 + 4 + 9 => 14
# File lib/minitest/benchmark.rb, line 284
def sigma enum, &block
  enum = enum.map(&block) if block
  enum.inject { |sum, n| sum + n }
end
teardown() click to toggle source

Runs after every test. Use this to refactor test cleanup.

# File lib/minitest/unit.rb, line 1076
def teardown; end
validation_for_fit(msg, threshold) click to toggle source

Returns a proc that calls the specified fit method and asserts that the error is within a tolerable threshold.

# File lib/minitest/benchmark.rb, line 293
def validation_for_fit msg, threshold
  proc do |range, times|
    a, b, rr = send "fit_#{msg}", range, times
    assert_operator rr, :>=, threshold
    [a, b, rr]
  end
end

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