Class | MiniTest::Unit::TestCase |
In: |
lib/minitest/benchmark.rb
lib/minitest/unit.rb |
Parent: | Object |
__send__ | -> | run_test |
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::Unit::TestCase.add_setup_hook { puts "foo" }
… is equivalent to:
module MyTestSetup def self.call puts "foo" end end MiniTest::Unit::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.
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::Unit::TestCase.add_teardown_hook { puts "foo" }
… is equivalent to:
module MyTestTeardown def self.call puts "foo" end end MiniTest::Unit::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.
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.
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.
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
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
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