Class MiniTest::Unit::TestCase
In: lib/minitest/benchmark.rb
lib/minitest/unit.rb
Parent: Object

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

See MiniTest::Assertions

Methods

Included Modules

LifecycleHooks Deprecated::Hooks Guard MiniTest::Assertions

External Aliases

__send__ -> run_test

Public Class methods

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]

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

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

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.

Returns all test suites that have benchmark methods.

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.

Public Instance methods

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

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

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

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

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

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

See: en.wikipedia.org/wiki/Coefficient_of_determination

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

Fits the functional form: a + bx.

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

See: mathworld.wolfram.com/LeastSquaresFitting.html

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

Return the output IO object

Have we hooked up the IO yet?

Returns true if the test passed.

Runs the tests reporting the status to runner

Runs before every test. Use this to set up before each test run.

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

Runs after every test. Use this to clean up after each test run.

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

[Validate]