Class: MiniTest::Unit::TestCase

Inherits:
Object
  • Object
show all
Includes:
Assertions
Defined in:
lib/minitest/unit.rb,
lib/minitest/benchmark.rb

Overview

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

See MiniTest::Assertions

Direct Known Subclasses

Spec

Constant Summary collapse

PASSTHROUGH_EXCEPTIONS = 
[NoMemoryError, SignalException,
Interrupt, SystemExit]
SUPPORTS_INFO_SIGNAL =

:nodoc:





Instance Attribute Summary collapse




Class Method Summary
collapse





Instance Method Summary
collapse




Methods included from Assertions


#_assertions, #_assertions=, #assert, #assert_block, #assert_empty, #assert_equal, #assert_in_delta, #assert_in_epsilon, #assert_includes, #assert_instance_of, #assert_kind_of, #assert_match, #assert_nil, #assert_operator, #assert_output, #assert_raises, #assert_respond_to, #assert_same, #assert_send, #assert_silent, #assert_throws, #capture_io, #exception_details, #flunk, #message, #mu_pp, #pass, #refute, #refute_empty, #refute_equal, #refute_in_delta, #refute_in_epsilon, #refute_includes, #refute_instance_of, #refute_kind_of, #refute_match, #refute_nil, #refute_operator, #refute_respond_to, #refute_same, #skip




Constructor Details





#initialize(name) ⇒ TestCase







:nodoc:















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# File 'lib/minitest/unit.rb', line 864

def initialize name # :nodoc:
  @__name__ = name
  @__io__ = nil
  @passed = nil
end













Instance Attribute Details






#__name__ ⇒ Object (readonly)







:nodoc:















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# File 'lib/minitest/unit.rb', line 822

def __name__
  @__name__
end













Class Method Details





.bench_exp(min, max, base = 10) ⇒ Object







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]
















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# 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) ⇒ Object







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


bench_linear(20, 40, 10) # => [20, 30, 40]
















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# 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 ⇒ Object







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.















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# File 'lib/minitest/benchmark.rb', line 63

def self.bench_range
  bench_exp 1, 10_000
end












.benchmark_methods ⇒ Object







Returns the benchmark methods (methods that start with bench_) for that class.















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# File 'lib/minitest/benchmark.rb', line 45

def self.benchmark_methods # :nodoc:
  public_instance_methods(true).grep(/^bench_/).map { |m| m.to_s }.sort
end












.benchmark_suites ⇒ Object







Returns all test suites that have benchmark methods.















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# File 'lib/minitest/benchmark.rb', line 52

def self.benchmark_suites
  TestCase.test_suites.reject { |s| s.benchmark_methods.empty? }
end












.inherited(klass) ⇒ Object







:nodoc:















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# File 'lib/minitest/unit.rb', line 885

def self.inherited klass # :nodoc:
  @@test_suites[klass] = true
end












.reset ⇒ Object







:nodoc:















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# File 'lib/minitest/unit.rb', line 879

def self.reset # :nodoc:
  @@test_suites = {}
end












.test_methods ⇒ Object







:nodoc:















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# File 'lib/minitest/unit.rb', line 902

def self.test_methods # :nodoc:
  methods = public_instance_methods(true).grep(/^test/).map { |m| m.to_s }

  case self.test_order
  when :random then
    max = methods.size
    methods.sort.sort_by { rand max }
  when :alpha, :sorted then
    methods.sort
  else
    raise "Unknown test_order: #{self.test_order.inspect}"
  end
end












.test_order ⇒ Object







Defines test order and is subclassable. Defaults to :random but can be overridden to return :alpha if your tests are order dependent (read: weak).















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# File 'lib/minitest/unit.rb', line 894

def self.test_order
  :random
end












.test_suites ⇒ Object







:nodoc:















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# File 'lib/minitest/unit.rb', line 898

def self.test_suites # :nodoc:
  @@test_suites.keys.sort_by { |ts| ts.name.to_s }
end













Instance Method Details





#assert_performance(validation, &work) ⇒ Object







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 |x|
    @obj.algorithm
  end
end
















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# 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) ⇒ Object







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 |x|
    @obj.algorithm
  end
end
















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# 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) ⇒ Object







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 |x|
    @obj.algorithm
  end
end
















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# 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) ⇒ Object







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 |x|
    @obj.algorithm
  end
end
















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# 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) ⇒ Object







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
















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# 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) ⇒ Object







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


See: en.wikipedia.org/wiki/Coefficient_of_determination















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# 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) ⇒ Object







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















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# 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) ⇒ Object







Fits the functional form: a + bx.


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


See: mathworld.wolfram.com/LeastSquaresFitting.html















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# 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) ⇒ Object







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















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# 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 ⇒ Object










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# File 'lib/minitest/unit.rb', line 870

def io
  @__io__ = true
  MiniTest::Unit.output
end












#io? ⇒ Boolean













Returns:


    

  • 
(Boolean)

    • 










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# File 'lib/minitest/unit.rb', line 875

def io?
  @__io__
end












#passed? ⇒ Boolean







Returns true if the test passed.








Returns:


    

  • 
(Boolean)

    • 










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# File 'lib/minitest/unit.rb', line 919

def passed?
  @passed
end












#run(runner) ⇒ Object







Runs the tests reporting the status to runner















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# File 'lib/minitest/unit.rb', line 832

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.__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.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 ⇒ Object







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















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# File 'lib/minitest/unit.rb', line 926

def setup; end












#sigma(enum, &block) ⇒ Object







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
















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# 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 ⇒ Object







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















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# File 'lib/minitest/unit.rb', line 931

def teardown; end












#validation_for_fit(msg, threshold) ⇒ Object







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















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# 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