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mu
Alpha Chen 2 years ago
parent 600f60ab8e
commit 59dbb8262b
Signed by: alpha
SSH Key Fingerprint: SHA256:3fOT8fiYQG/aK9ntivV3Bqtg8AYQ7q4nV6ZgihOA20g
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639
lib/minitest/thesis.rb
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@ -2,10 +2,16 @@ require "digest"
require "minitest"
require_relative "thesis/directory_db"
require_relative "thesis/error"
require_relative "thesis/possibility"
require_relative "thesis/status"
require_relative "thesis/test_case"
require_relative "thesis/testing_state"
require_relative "thesis/version"
module Minitest
module Thesis
VERSION = "0.1.0"
class Test < Minitest::Test
# Runs a test. Usage is:
@ -72,161 +78,6 @@ module Minitest
end
end
# Represents a single generated test case, which consists of an underlying
# set of choices that produce possibilities.
class TestCase
# Returns a test case that makes this series of choices.
def self.for_choices(choices, print_results: false)
self.new(prefix: choices, random: nil, max_size: choices.length, print_results:)
end
attr_accessor :status
attr_reader :choices, :targeting_score
def initialize(prefix:, random:, max_size: Float::INFINITY, print_results: false)
@prefix, @random, @max_size, @print_results = prefix, random, max_size, print_results
@choices = []
@status = nil
@depth = 0
@targeting_score = nil
end
# Returns a number in the range [0, n]
def choice(n)
result = make_choice(n) { @random.rand(n) }
puts "choice(#{n}): #{result}" if should_print?
result
end
# Return True with probability `p`.
def weighted(p)
result = if p <= 0 then forced_choice(0)
elsif p >= 1 then forced_choice(1)
else make_choice(1) { (@random.rand <= p) ? 1 : 0 }
end
puts "weighted(#{p}): #{result}" if should_print?
result
end
# Inserts a fake choice into the choice sequence, as if some call to
# choice() had returned `n`. You almost never need this, but sometimes it
# can be a useful hint to the shrinker.
def forced_choice(n)
raise RangeError.new("Invalid choice #{n}") if n.bit_length > 64 || n.negative?
raise Frozen unless @status.nil?
mark_status(Status::OVERRUN) if @choices.length >= @max_size
choices << n
n
end
# Mark this test case as invalid.
def reject = mark_status(Status::INVALID)
# If this precondition is not met, abort the test and mark this test case as invalid.
def assume(precondition)
return if precondition
reject
end
# Set a score to maximize. Multiple calls to this function will override previous ones.
#
# The name and idea come from Löscher, Andreas, and Konstantinos Sagonas.
# "Targeted property-based testing." ISSTA. 2017, but the implementation
# is based on that found in Hypothesis, which is not that similar to
# anything described in the paper.
def target(score) = @targeting_score = score
# Return a possible value from `possibility`.
def any(possibility)
begin
@depth += 1
result = possibility.produce.(self)
ensure
@depth -= 1
end
puts "any(#{possibility}): #{result}" if should_print?
result
end
# Set the status and raise StopTest.
def mark_status(status)
raise Frozen unless self.status.nil?
@status = status
raise StopTest
end
private
def should_print? = @print_results && @depth.zero?
# Make a choice in [0, n], by calling rnd_method if randomness is needed.
def make_choice(n, &rnd_method)
raise RangeError.new("Invalid choice #{n}") if n.bit_length > 64 || n.negative?
raise Frozen unless @status.nil?
mark_status(Status::OVERRUN) if @choices.length >= @max_size
result = if @choices.length < @prefix.length
@prefix[@choices.length]
else
rnd_method.()
end
@choices << result
mark_status(Status::INVALID) if result > n
result
end
end
# Represents some range of values that might be used in a test, that can be
# requested from a `TestCase`. Pass one of these to TestCase.any to get a
# concrete value.
class Possibility
attr_reader :produce, :name
def initialize(name = "TODO", &produce)
@name = name
@produce = produce
end
def inspect = name
def to_s = name
# "Returns a `Possibility` where values come from applying `f` to some possible value for `self`."
def map(&f)
self.class.new("#{name}.map(TODO)") {|tc| f.call(tc.any(self)) }
end
# Returns a `Possibility` where values come from applying `f` (which
# should return a new `Possibility` to some possible value for `self`
# then returning a possible value from that.
def bind(&f)
self.class.new("#{name}.bind(TODO)") {|tc| tc.any(f.(tc.any(self))) }
end
# Returns a `Possibility` whose values are any possible value of `self`
# for which `f` returns True.
def satisfying(&f)
self.class.new("#{name}.select(TODO)") {|test_case|
3.times.first {
candidate = test_case.any(self)
candidate if f.(candidate)
} || test_case.reject
}
end
end
# Any integer in the range [m, n] is possible
def integers(m, n) = Possibility.new("integers(#{m}, #{n})") {|tc| m + tc.choice(n - m) }
@ -271,480 +122,6 @@ module Minitest
possibilities.map {|p| tc.any(p) }
}
end
# We cap the maximum amount of entropy a test case can use.
# This prevents cases where the generated test case size explodes
# by effectively rejection
BUFFER_SIZE = 8 * 1024
# Returns a cached version of a function that maps a choice sequence to the
# status of calling a test function on a test case populated with it. Is
# able to take advantage of the structure of the test function to predict
# the result even if exact sequence of choices has not been seen
# previously.
#
# You can safely omit implementing this at the cost of somewhat increased
# shrinking time.
class CachedTestFunction
def initialize(&test_function)
@test_function = test_function
# Tree nodes are either a point at which a choice occurs
# in which case they map the result of the choice to the
# tree node we are in after, or a Status object indicating
# mark_status was called at this point and all future
# choices are irrelevant.
#
# Note that a better implementation of this would use
# a Patricia trie, which implements long non-branching
# paths as an array inline. For simplicity we don't
# do that here.
@tree = {}
end
def call(choices)
node = @tree
begin
choices.each do |c|
node = node.fetch(c)
# mark_status was called, thus future choices
# will be ignored.
if node.is_a?(Status)
fail if node == Status::OVERRUN
return node
end
end
# If we never entered an unknown region of the tree
# or hit a Status value, then we know that another
# choice will be made next and the result will overrun.
return Status::OVERRUN
rescue KeyError
end
# We now have to actually call the test function to find out what
# happens.
test_case = TestCase.for_choices(choices)
@test_function.(test_case)
fail if test_case.status.nil?
# We enter the choices made in a tree.
node = @tree
*rest, last = test_case.choices
rest.each do |c|
node = if node.has_key?(c)
node[c]
else
node[c] = {}
end
end
unless last.nil?
node[last] = test_case.status == Status::OVERRUN ? {} : test_case.status
end
test_case.status
end
end
class TestingState
attr_reader :result, :valid_test_cases, :calls
def initialize(random:, test_function:, max_examples:)
@random, @_test_function, @max_examples = random, test_function, max_examples
@valid_test_cases = 0
@calls = 0
@test_is_trivial = false
end
def test_function(test_case)
begin
@_test_function.(test_case)
rescue StopTest
end
if test_case.status.nil?
test_case.status = Status::VALID
end
@calls += 1
if test_case.status >= Status::INVALID && test_case.choices.length.zero?
@test_is_trivial = true
end
if test_case.status >= Status::VALID
@valid_test_cases += 1
unless test_case.targeting_score.nil?
relevant_info = [test_case.targeting_score, test_case.choices]
if @best_scoring.nil?
@best_scoring = relevant_info
else
best, _ = @best_scoring
if test_case.targeting_score > best
@best_scoring = relevant_info
end
end
end
end
if test_case.status == Status::INTERESTING && (
@result.nil? || ((sort_key(test_case.choices) <=> sort_key(@result)) == -1)
)
@result = test_case.choices
end
end
# If any test cases have had `target()` called on them, do a simple
# hill climbing algorithm to attempt to optimise that target score.
def target
return if !@result.nil? || @best_scoring.nil?
# Can we improve the score by changing choices[i] by `step`?
adjust = ->(i, step) do
fail if @best_scoring.nil?
score, choices = @best_scoring
return false if choices[i] + step < 0 || choices[i].bit_length >= 64
attempt = choices.dup
attempt[i] += step
test_case = TestCase.new(
prefix: attempt, random: @random, max_size: BUFFER_SIZE
)
test_function(test_case)
fail if test_case.status.nil?
test_case.status >= Status::VALID &&
!test_case.targeting_score.nil? &&
test_case.targeting_score > score
end
while keep_generating?
i = @random.rand(@best_scoring[1].length)
sign = 0
[1, -1].each do |k|
return unless keep_generating?
if adjust.(i, k)
sign = k
break
end
end
next if sign.zero?
k = 1
k *= 2 while keep_generating? && adjust.(i, sign * k)
while k.positive?
while keep_generating? && adjust.(i, sign * k)
end
k /= 2
end
end
end
def run
generate
target
shrink
end
def keep_generating?
!@test_is_trivial &&
result.nil? &&
@valid_test_cases < @max_examples &&
# We impose a limit on the maximum number of calls as
# well as the maximum number of valid examples. This is
# to avoid taking a prohibitively long time on tests which
# have hard or impossible to satisfy preconditions.
@calls < @max_examples * 10
end
# Run random generation until either we have found an interesting test
# case or hit the limit of how many test cases we should evaluate.
def generate
while keep_generating? && (@best_scoring.nil? || @valid_test_cases < @max_examples / 2)
test_function(TestCase.new(prefix: [], random: @random, max_size: BUFFER_SIZE))
end
end
# If we have found an interesting example, try shrinking it so that the
# choice sequence leading to our best example is shortlex smaller than
# the one we originally found. This improves the quality of the generated
# test case, as per our paper.
#
# https://drmaciver.github.io/papers/reduction-via-generation-preview.pdf
def shrink
# if not self.result:
# return
return if @result.nil? || @result.empty?
# Shrinking will typically try the same choice sequences over and over
# again, so we cache the test function in order to not end up
# reevaluating it in those cases. This also allows us to catch cases
# where we try something that is e.g. a prefix of something we've
# previously tried, which is guaranteed not to work.
cached = CachedTestFunction.new {|tc| test_function(tc) }
consider = ->(choices) do
return true if choices == @result
cached.(choices) == Status::INTERESTING
end
fail unless consider.(@result)
# We are going to perform a number of transformations to the current
# result, iterating until none of them make any progress - i.e. until
# we make it through an entire iteration of the loop without changing
# the result.
prev = nil
while prev != @result
prev = @result
# A note on weird loop order: We iterate backwards through the choice
# sequence rather than forwards, because later bits tend to depend on
# earlier bits so it's easier to make changes near the end and
# deleting bits at the end may allow us to make changes earlier on
# that we we'd have missed.
#
# Note that we do not restart the loop at the end when we find a
# successful shrink. This is because things we've already tried are
# less likely to work.
#
# If this guess is wrong, that's OK, this isn't a correctness
# problem, because if we made a successful reduction then we are not
# at a fixed point and will restart the loop at the end the next time
# round. In some cases this can result in performance issues, but the
# end result should still be fine.
# First try deleting each choice we made in chunks. We try longer
# chunks because this allows us to delete whole composite elements:
# e.g. deleting an element from a generated list requires us to
# delete both the choice of whether to include it and also the
# element itself, which may involve more than one choice. Some things
# will take more than 8 choices in the sequence. That's too bad, we
# may not be able to delete those. In Hypothesis proper we record the
# boundaries corresponding to `any` calls so that we can try deleting
# those, but that's pretty high overhead and also a bunch of slightly
# annoying code that it's not worth porting.
#
# We could instead do a quadratic amount of work to try all
# boundaries, but in general we don't want to do that because even a
# shrunk test case can involve a relatively large number of choices.
k = 8
while k.positive?
i = @result.length - k - 1
until i.negative?
if i >= @result.length
# Can happen if we successfully lowered the value at i - 1
i -= 1
next
end
attempt = @result[0...i] + (@result[i + k..] || [])
fail unless attempt.length < @result.length
unless consider.(attempt)
# This fixes a common problem that occurs
# when you have dependencies on some
# length parameter. e.g. draw a number
# between 0 and 10 and then draw that
# many elements. This can't delete
# everything that occurs that way, but
# it can delete some things and often
# will get us unstuck when nothing else
# does.
if i.positive? && attempt[i - 1].positive?
attempt[i - 1] -= 1
i += 1 if consider.(attempt)
end
i -= 1
end
end
k /= 2
end
# Attempts to replace some indices in the current result with new
# values. Useful for some purely lexicographic reductions that we are
# about to perform.
replace = ->(values) do
fail if @result.nil?
attempt = @result.dup
values.each do |i, v|
# The size of self.result can change during shrinking. If that
# happens, stop attempting to make use of these replacements
# because some other shrink pass is better to run now.
return false if i >= attempt.length
attempt[i] = v
end
consider.(attempt)
end
# Now we try replacing blocks of choices with zeroes. Note that
# unlike the above we skip k = 1 because we handle that in the next
# step. Often (but not always) a block of all zeroes is the shortlex
# smallest value that a region can be.
k = 8
while k > 1
i = @result.length - k
until i.negative?
if replace.((i...i+k).to_h {|i| [i, 0]})
# If we've succeeded then all of [i, i + k] is zero so we
# adjust i so that the next region does not overlap with this
# at all.
i -= k
else
# Otherwise we might still be able to zero some of these values
# but not the last one, so we just go back one.
i -= 1
end
end
k /= 2
end
# Now try replacing each choice with a smaller value by doing a
# binary search. This will replace n with 0 or n - 1 if possible, but
# will also more efficiently replace it with a smaller number than
# doing multiple subtractions would.
i = @result.length - 1
until i.negative?
# Attempt to replace
bin_search_down(0, @result[i]) {|v| replace.({i => v}) }
i -= 1
end
# NB from here on this is just showing off cool shrinker tricks and
# you probably don't need to worry about it and can skip these bits
# unless they're easy and you want bragging rights for how much
# better you are at shrinking than the local QuickCheck equivalent.
# Try sorting out of order ranges of choices, as `sort(x) <= x`, so
# this is always a lexicographic reduction.
k = 8
# while k > 1:
while k > 1
(@result.length - k - 1).downto(0).each do |i|
consider.(@result[0...i] + @result[i...i+k].sort + @result[i+k..])
end
k /= 2
end
# Try adjusting nearby pairs of integers by redistributing value
# between them. This is useful for tests that depend on the sum of
# some generated values.
[2, 1].each do |k|
(@result.length - k - 1).downto(0).each do |i|
j = i + k
# This check is necessary because the previous changes might have
# shrunk the size of result, but also it's tedious to write tests
# for this so I didn't.
if j < @result.length
# Try swapping out of order pairs
if @result[i] > @result[j]
replace.({j => @result[i], i => @result[j]})
end
# j could be out of range if the previous swap succeeded.
if j < @result.length && @result[i].positive?
prev_i = @result[i]
prev_j = @result[j]
bin_search_down(0, prev_i) {|v|
replace.({i => v, j => prev_j + (prev_i - v)})
}
end
end
end
end
end
end
private
# Returns a key that can be used for the shrinking order of test cases.
def sort_key(choices) = [choices.length, choices]
# Returns n in [lo, hi] such that f(n) is True, where it is assumed and
# will not be checked that f(hi) is True.
#
# Will return `lo` if `f(lo)` is True, otherwise the only guarantee that is
# made is that `f(n - 1)` is False and `f(n)` is True. In particular this
# does *not* guarantee to find the smallest value, only a locally minimal
# one.
def bin_search_down(low, high, &f)
return low if f.(low)
while low + 1 < high
mid = low + (high - low) / 2
if f.(mid)
high = mid
else
low = mid
end
end
high
end
end
class DirectoryDb
def initialize(dir)
@dir = dir
Dir.mkdir(@dir)
rescue SystemCallError => e
raise unless e.errno == Errno::EEXIST::Errno
end
def [](key)
f = file(key)
return nil unless File.exist?(f)
File.read(f)
end
def []=(key, value)
File.write(file(key), value)
end
private
def file(key)
File.join(@dir, Digest::SHA1.hexdigest(key)[0...10])
end
end
class Error< StandardError; end
# Attempted to make choices on a test case that has been completed.
class Frozen < Error; end
# Raised when a test should stop executing early.
class StopTest < Error; end
# Raised when a test has no valid examples.
class Unsatisfiable < Error; end
class Status < Struct.new(:value)
# Test case didn't have enough data to complete
OVERRUN = self.new(0)
# Test case contained something that prevented completion
INVALID = self.new(1)
# Test case completed just fine but was boring
VALID = self.new(2)
# Test case completed and was interesting
INTERESTING = self.new(3)
include Comparable
def <=>(other)
value <=> other.value
end
end
end
end
end

27
lib/minitest/thesis/directory_db.rb
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@ -0,0 +1,27 @@
module Minitest::Thesis
class DirectoryDb
def initialize(dir)
@dir = dir
Dir.mkdir(@dir)
rescue SystemCallError => e
raise unless e.errno == Errno::EEXIST::Errno
end
def [](key)
f = file(key)
return nil unless File.exist?(f)
File.read(f)
end
def []=(key, value)
File.write(file(key), value)
end
private
def file(key)
File.join(@dir, Digest::SHA1.hexdigest(key)[0...10])
end
end
end

12
lib/minitest/thesis/error.rb
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@ -0,0 +1,12 @@
module Minitest::Thesis
Error = Class.new(StandardError)
# Attempted to make choices on a test case that has been completed.
Frozen = Class.new(Error)
# Raised when a test should stop executing early.
StopTest = Class.new(Error)
# Raised when a test has no valid examples.
Unsatisfiable = Class.new(Error)
end

39
lib/minitest/thesis/possibility.rb
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@ -0,0 +1,39 @@
module Minitest::Thesis
# Represents some range of values that might be used in a test, that can be
# requested from a `TestCase`. Pass one of these to TestCase.any to get a
# concrete value.
class Possibility
attr_reader :produce, :name
def initialize(name = "TODO", &produce)
@name = name
@produce = produce
end
def inspect = name
def to_s = name
# "Returns a `Possibility` where values come from applying `f` to some possible value for `self`."
def map(&f)
self.class.new("#{name}.map(TODO)") {|tc| f.call(tc.any(self)) }
end
# Returns a `Possibility` where values come from applying `f` (which
# should return a new `Possibility` to some possible value for `self`
# then returning a possible value from that.
def bind(&f)
self.class.new("#{name}.bind(TODO)") {|tc| tc.any(f.(tc.any(self))) }
end
# Returns a `Possibility` whose values are any possible value of `self`
# for which `f` returns True.
def satisfying(&f)
self.class.new("#{name}.select(TODO)") {|test_case|
3.times.first {
candidate = test_case.any(self)
candidate if f.(candidate)
} || test_case.reject
}
end
end
end

21
lib/minitest/thesis/status.rb
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module Minitest::Thesis
class Status < Struct.new(:value)
# Test case didn't have enough data to complete
OVERRUN = self.new(0)
# Test case contained something that prevented completion
INVALID = self.new(1)
# Test case completed just fine but was boring
VALID = self.new(2)
# Test case completed and was interesting
INTERESTING = self.new(3)
include Comparable
def <=>(other)
value <=> other.value
end
end
end

121
lib/minitest/thesis/test_case.rb
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@ -0,0 +1,121 @@
require_relative "error"
require_relative "status"
module Minitest::Thesis
# Represents a single generated test case, which consists of an underlying
# set of choices that produce possibilities.
class TestCase
# Returns a test case that makes this series of choices.
def self.for_choices(choices, print_results: false)
self.new(prefix: choices, random: nil, max_size: choices.length, print_results:)
end
attr_accessor :status
attr_reader :choices, :targeting_score
def initialize(prefix:, random:, max_size: Float::INFINITY, print_results: false)
@prefix, @random, @max_size, @print_results = prefix, random, max_size, print_results
@choices = []
@status = nil
@depth = 0
@targeting_score = nil
end
# Returns a number in the range [0, n]
def choice(n)
result = make_choice(n) { @random.rand(n) }
puts "choice(#{n}): #{result}" if should_print?
result
end
# Return True with probability `p`.
def weighted(p)
result = if p <= 0 then forced_choice(0)
elsif p >= 1 then forced_choice(1)
else make_choice(1) { (@random.rand <= p) ? 1 : 0 }
end
puts "weighted(#{p}): #{result}" if should_print?
result
end
# Inserts a fake choice into the choice sequence, as if some call to
# choice() had returned `n`. You almost never need this, but sometimes it
# can be a useful hint to the shrinker.
def forced_choice(n)
raise RangeError.new("Invalid choice #{n}") if n.bit_length > 64 || n.negative?
raise Frozen unless @status.nil?
mark_status(Status::OVERRUN) if @choices.length >= @max_size
choices << n
n
end
# Mark this test case as invalid.
def reject = mark_status(Status::INVALID)
# If this precondition is not met, abort the test and mark this test case as invalid.
def assume(precondition)
return if precondition
reject
end
# Set a score to maximize. Multiple calls to this function will override previous ones.
#
# The name and idea come from Löscher, Andreas, and Konstantinos Sagonas.
# "Targeted property-based testing." ISSTA. 2017, but the implementation
# is based on that found in Hypothesis, which is not that similar to
# anything described in the paper.
def target(score) = @targeting_score = score
# Return a possible value from `possibility`.
def any(possibility)
begin
@depth += 1
result = possibility.produce.(self)
ensure
@depth -= 1
end
puts "any(#{possibility}): #{result}" if should_print?
result
end
# Set the status and raise StopTest.
def mark_status(status)
raise Frozen unless self.status.nil?
@status = status
raise StopTest
end
private
def should_print? = @print_results && @depth.zero?
# Make a choice in [0, n], by calling rnd_method if randomness is needed.
def make_choice(n, &rnd_method)
raise RangeError.new("Invalid choice #{n}") if n.bit_length > 64 || n.negative?
raise Frozen unless @status.nil?
mark_status(Status::OVERRUN) if @choices.length >= @max_size
result = if @choices.length < @prefix.length
@prefix[@choices.length]
else
rnd_method.()
end
@choices << result
mark_status(Status::INVALID) if result > n
result
end
end
end

418
lib/minitest/thesis/testing_state.rb
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@ -0,0 +1,418 @@
module Minitest::Thesis
# We cap the maximum amount of entropy a test case can use.
# This prevents cases where the generated test case size explodes
# by effectively rejection
BUFFER_SIZE = 8 * 1024
class TestingState
attr_reader :result, :valid_test_cases, :calls
def initialize(random:, test_function:, max_examples:)
@random, @_test_function, @max_examples = random, test_function, max_examples
@valid_test_cases = 0
@calls = 0
@test_is_trivial = false
end
def test_function(test_case)
begin
@_test_function.(test_case)
rescue StopTest
end
if test_case.status.nil?
test_case.status = Status::VALID
end
@calls += 1
if test_case.status >= Status::INVALID && test_case.choices.length.zero?
@test_is_trivial = true
end
if test_case.status >= Status::VALID
@valid_test_cases += 1
unless test_case.targeting_score.nil?
relevant_info = [test_case.targeting_score, test_case.choices]
if @best_scoring.nil?
@best_scoring = relevant_info
else
best, _ = @best_scoring
if test_case.targeting_score > best
@best_scoring = relevant_info
end
end
end
end
if test_case.status == Status::INTERESTING && (
@result.nil? || ((sort_key(test_case.choices) <=> sort_key(@result)) == -1)
)
@result = test_case.choices
end
end
# If any test cases have had `target()` called on them, do a simple
# hill climbing algorithm to attempt to optimise that target score.
def target
return if !@result.nil? || @best_scoring.nil?
# Can we improve the score by changing choices[i] by `step`?
adjust = ->(i, step) do
fail if @best_scoring.nil?
score, choices = @best_scoring
return false if choices[i] + step < 0 || choices[i].bit_length >= 64
attempt = choices.dup
attempt[i] += step
test_case = TestCase.new(
prefix: attempt, random: @random, max_size: BUFFER_SIZE
)
test_function(test_case)
fail if test_case.status.nil?
test_case.status >= Status::VALID &&
!test_case.targeting_score.nil? &&
test_case.targeting_score > score
end
while keep_generating?
i = @random.rand(@best_scoring[1].length)
sign = 0
[1, -1].each do |k|
return unless keep_generating?
if adjust.(i, k)
sign = k
break
end
end
next if sign.zero?
k = 1
k *= 2 while keep_generating? && adjust.(i, sign * k)
while k.positive?
while keep_generating? && adjust.(i, sign * k)
end
k /= 2
end
end
end
def run
generate
target
shrink
end
def keep_generating?
!@test_is_trivial &&
result.nil? &&
@valid_test_cases < @max_examples &&
# We impose a limit on the maximum number of calls as
# well as the maximum number of valid examples. This is
# to avoid taking a prohibitively long time on tests which
# have hard or impossible to satisfy preconditions.
@calls < @max_examples * 10
end
# Run random generation until either we have found an interesting test
# case or hit the limit of how many test cases we should evaluate.
def generate
while keep_generating? && (@best_scoring.nil? || @valid_test_cases < @max_examples / 2)
test_function(TestCase.new(prefix: [], random: @random, max_size: BUFFER_SIZE))
end
end
# If we have found an interesting example, try shrinking it so that the
# choice sequence leading to our best example is shortlex smaller than
# the one we originally found. This improves the quality of the generated
# test case, as per our paper.
#
# https://drmaciver.github.io/papers/reduction-via-generation-preview.pdf
def shrink
# if not self.result:
# return
return if @result.nil? || @result.empty?
# Shrinking will typically try the same choice sequences over and over
# again, so we cache the test function in order to not end up
# reevaluating it in those cases. This also allows us to catch cases
# where we try something that is e.g. a prefix of something we've
# previously tried, which is guaranteed not to work.
cached = CachedTestFunction.new {|tc| test_function(tc) }
consider = ->(choices) do
return true if choices == @result
cached.(choices) == Status::INTERESTING
end
fail unless consider.(@result)
# We are going to perform a number of transformations to the current
# result, iterating until none of them make any progress - i.e. until
# we make it through an entire iteration of the loop without changing
# the result.
prev = nil
while prev != @result
prev = @result
# A note on weird loop order: We iterate backwards through the choice
# sequence rather than forwards, because later bits tend to depend on
# earlier bits so it's easier to make changes near the end and
# deleting bits at the end may allow us to make changes earlier on
# that we we'd have missed.
#
# Note that we do not restart the loop at the end when we find a
# successful shrink. This is because things we've already tried are
# less likely to work.
#
# If this guess is wrong, that's OK, this isn't a correctness
# problem, because if we made a successful reduction then we are not
# at a fixed point and will restart the loop at the end the next time
# round. In some cases this can result in performance issues, but the
# end result should still be fine.
# First try deleting each choice we made in chunks. We try longer
# chunks because this allows us to delete whole composite elements:
# e.g. deleting an element from a generated list requires us to
# delete both the choice of whether to include it and also the
# element itself, which may involve more than one choice. Some things
# will take more than 8 choices in the sequence. That's too bad, we
# may not be able to delete those. In Hypothesis proper we record the
# boundaries corresponding to `any` calls so that we can try deleting
# those, but that's pretty high overhead and also a bunch of slightly
# annoying code that it's not worth porting.
#
# We could instead do a quadratic amount of work to try all
# boundaries, but in general we don't want to do that because even a
# shrunk test case can involve a relatively large number of choices.
k = 8
while k.positive?
i = @result.length - k - 1
until i.negative?
if i >= @result.length
# Can happen if we successfully lowered the value at i - 1
i -= 1
next
end
attempt = @result[0...i] + (@result[i + k..] || [])
fail unless attempt.length < @result.length
unless consider.(attempt)
# This fixes a common problem that occurs
# when you have dependencies on some
# length parameter. e.g. draw a number
# between 0 and 10 and then draw that
# many elements. This can't delete
# everything that occurs that way, but
# it can delete some things and often
# will get us unstuck when nothing else
# does.
if i.positive? && attempt[i - 1].positive?
attempt[i - 1] -= 1
i += 1 if consider.(attempt)
end
i -= 1
end
end
k /= 2
end
# Attempts to replace some indices in the current result with new
# values. Useful for some purely lexicographic reductions that we are
# about to perform.
replace = ->(values) do
fail if @result.nil?
attempt = @result.dup
values.each do |i, v|
# The size of self.result can change during shrinking. If that
# happens, stop attempting to make use of these replacements
# because some other shrink pass is better to run now.
return false if i >= attempt.length
attempt[i] = v
end
consider.(attempt)
end
# Now we try replacing blocks of choices with zeroes. Note that
# unlike the above we skip k = 1 because we handle that in the next
# step. Often (but not always) a block of all zeroes is the shortlex
# smallest value that a region can be.
k = 8
while k > 1
i = @result.length - k
until i.negative?
if replace.((i...i+k).to_h {|i| [i, 0]})
# If we've succeeded then all of [i, i + k] is zero so we
# adjust i so that the next region does not overlap with this
# at all.
i -= k
else
# Otherwise we might still be able to zero some of these values
# but not the last one, so we just go back one.
i -= 1
end
end
k /= 2
end
# Now try replacing each choice with a smaller value by doing a
# binary search. This will replace n with 0 or n - 1 if possible, but
# will also more efficiently replace it with a smaller number than
# doing multiple subtractions would.
i = @result.length - 1
until i.negative?
# Attempt to replace
bin_search_down(0, @result[i]) {|v| replace.({i => v}) }
i -= 1
end
# NB from here on this is just showing off cool shrinker tricks and
# you probably don't need to worry about it and can skip these bits
# unless they're easy and you want bragging rights for how much
# better you are at shrinking than the local QuickCheck equivalent.
# Try sorting out of order ranges of choices, as `sort(x) <= x`, so
# this is always a lexicographic reduction.
k = 8
# while k > 1:
while k > 1
(@result.length - k - 1).downto(0).each do |i|
consider.(@result[0...i] + @result[i...i+k].sort + @result[i+k..])
end
k /= 2
end
# Try adjusting nearby pairs of integers by redistributing value
# between them. This is useful for tests that depend on the sum of
# some generated values.
[2, 1].each do |k|
(@result.length - k - 1).downto(0).each do |i|
j = i + k
# This check is necessary because the previous changes might have
# shrunk the size of result, but also it's tedious to write tests
# for this so I didn't.
if j < @result.length
# Try swapping out of order pairs
if @result[i] > @result[j]
replace.({j => @result[i], i => @result[j]})
end
# j could be out of range if the previous swap succeeded.
if j < @result.length && @result[i].positive?
prev_i = @result[i]
prev_j = @result[j]
bin_search_down(0, prev_i) {|v|
replace.({i => v, j => prev_j + (prev_i - v)})
}
end
end
end
end
end
end
private
# Returns a key that can be used for the shrinking order of test cases.
def sort_key(choices) = [choices.length, choices]
# Returns n in [lo, hi] such that f(n) is True, where it is assumed and
# will not be checked that f(hi) is True.
#
# Will return `lo` if `f(lo)` is True, otherwise the only guarantee that is
# made is that `f(n - 1)` is False and `f(n)` is True. In particular this
# does *not* guarantee to find the smallest value, only a locally minimal
# one.
def bin_search_down(low, high, &f)
return low if f.(low)
while low + 1 < high
mid = low + (high - low) / 2
if f.(mid)
high = mid
else
low = mid
end
end
high
end
end
# Returns a cached version of a function that maps a choice sequence to the
# status of calling a test function on a test case populated with it. Is
# able to take advantage of the structure of the test function to predict
# the result even if exact sequence of choices has not been seen
# previously.
#
# You can safely omit implementing this at the cost of somewhat increased
# shrinking time.
class CachedTestFunction
def initialize(&test_function)
@test_function = test_function
# Tree nodes are either a point at which a choice occurs
# in which case they map the result of the choice to the
# tree node we are in after, or a Status object indicating
# mark_status was called at this point and all future
# choices are irrelevant.
#
# Note that a better implementation of this would use
# a Patricia trie, which implements long non-branching
# paths as an array inline. For simplicity we don't
# do that here.
@tree = {}
end
def call(choices)
node = @tree
begin
choices.each do |c|
node = node.fetch(c)
# mark_status was called, thus future choices
# will be ignored.
if node.is_a?(Status)
fail if node == Status::OVERRUN
return node
end
end
# If we never entered an unknown region of the tree
# or hit a Status value, then we know that another
# choice will be made next and the result will overrun.
return Status::OVERRUN
rescue KeyError
end
# We now have to actually call the test function to find out what
# happens.
test_case = TestCase.for_choices(choices)
@test_function.(test_case)
fail if test_case.status.nil?
# We enter the choices made in a tree.
node = @tree
*rest, last = test_case.choices
rest.each do |c|
node = if node.has_key?(c)
node[c]
else
node[c] = {}
end
end
unless last.nil?
node[last] = test_case.status == Status::OVERRUN ? {} : test_case.status
end
test_case.status
end
end
end

3
lib/minitest/thesis/version.rb
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@ -0,0 +1,3 @@
module Minitest::Thesis
VERSION = "0.1.0"
end

764
test/minitest/thesis_test.rb
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@ -1,442 +1,469 @@
require "test_helper"
class Minitest::ThesisTest < Minitest::Thesis::Test
class Failure < StandardError; end
def test_finds_small_list
(0...10).each do |seed|
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("finds_small_list", database: {}, random: Random.new(seed)) do |test_case|
ls = test_case.any(lists(integers(0, 10_000)))
assert ls.sum <= 1_000
module Minitest::Thesis
class ThesisTest < Minitest::Thesis::Test
class Failure < StandardError; end
def test_finds_small_list
(0...10).each do |seed|
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("finds_small_list", database: {}, random: Random.new(seed)) do |test_case|
ls = test_case.any(lists(integers(0, 10_000)))
assert ls.sum <= 1_000
end
end
end
assert_equal <<~OUT, out
any(lists(integers(0, 10000))): [1001]
OUT
end
end
assert_equal <<~OUT, out
any(lists(integers(0, 10000))): [1001]
OUT
# Minithesis can't really handle shrinking arbitrary monadic bind, but length
# parameters are a common case of monadic bind that it has a little bit of
# special casing for. This test ensures that that special casing works.
#
# The problem is that if you generate a list by drawing a length and then
# drawing that many elements, you can end up with something like ``[1001, 0,
# 0]`` then deleting those zeroes in the middle is a pain. minithesis will
# solve this by first sorting those elements, so that we have ``[0, 0,
# 1001]``, and then lowering the length by two, turning it into ``[1001]`` as
# desired.
def test_finds_small_list_even_with_bad_lists
bad_list = Possibility.new("bad_list") {|tc|
n = tc.choice(10)
Array.new(n) { tc.choice(10_000) }
}
(0...10).each do |seed|
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("finds_small_list_even_with_bad_lists", database: {}, random: Random.new(seed)) do |test_case|
ls = test_case.any(bad_list)
assert ls.sum <= 1_000
end
end
end
assert_equal <<~OUT, out
any(bad_list): [1001]
OUT
end
end
end
# Minithesis can't really handle shrinking arbitrary monadic bind, but length
# parameters are a common case of monadic bind that it has a little bit of
# special casing for. This test ensures that that special casing works.
#
# The problem is that if you generate a list by drawing a length and then
# drawing that many elements, you can end up with something like ``[1001, 0,
# 0]`` then deleting those zeroes in the middle is a pain. minithesis will
# solve this by first sorting those elements, so that we have ``[0, 0,
# 1001]``, and then lowering the length by two, turning it into ``[1001]`` as
# desired.
def test_finds_small_list_even_with_bad_lists
bad_list = Possibility.new("bad_list") {|tc|
n = tc.choice(10)
Array.new(n) { tc.choice(10_000) }
}
(0...10).each do |seed|
def test_reduces_additive_pairs
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("finds_small_list_even_with_bad_lists", database: {}, random: Random.new(seed)) do |test_case|
ls = test_case.any(bad_list)
assert ls.sum <= 1_000
run_test("reduces_additive_pairs", database: {}, max_examples: 10_000) do |test_case|
m = test_case.choice(1000)
n = test_case.choice(1000)
assert m + n <= 1000
end
end
end
assert_equal <<~OUT, out
any(bad_list): [1001]
choice(1000): 1
choice(1000): 1000
OUT
end
end
def test_reduces_additive_pairs
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("reduces_additive_pairs", database: {}, max_examples: 10_000) do |test_case|
m = test_case.choice(1000)
n = test_case.choice(1000)
assert m + n <= 1000
end
end
end
assert_equal <<~OUT, out
choice(1000): 1
choice(1000): 1000
OUT
end
def test_reuses_results_from_the_database
Dir.mktmpdir do |tmpdir|
db = DirectoryDb.new(tmpdir)
count = 0
def test_reuses_results_from_the_database
Dir.mktmpdir do |tmpdir|
db = DirectoryDb.new(tmpdir)
count = 0
run = -> {
assert_raises(Minitest::Assertion) do
run_test("reuses_results_from_the_database", database: db, quiet: true) do |test_case|
count += 1
assert test_case.choice(10_000) < 10
run = -> {
assert_raises(Minitest::Assertion) do
run_test("reuses_results_from_the_database", database: db, quiet: true) do |test_case|
count += 1
assert test_case.choice(10_000) < 10
end
end
end
}
run.()
}
assert_equal 1, Dir.children(tmpdir).length
prev_count = count
run.()
run.()
assert_equal 1, Dir.children(tmpdir).length
prev_count = count
assert_equal 1, Dir.children(tmpdir).length
assert_equal prev_count + 2, count
end
end
run.()
def test_test_cases_satisfy_preconditions
run_test("test_cases_satisfy_preconditions", database: {}) do |test_case|
n = test_case.choice(10)
test_case.assume(n != 0)
refute_equal 0, n
assert_equal 1, Dir.children(tmpdir).length
assert_equal prev_count + 2, count
end
end
end
def test_error_on_too_strict_precondition
assert_raises(Unsatisfiable) do
run_test("error_on_too_strict_precondition", database: {}) do |test_case|
def test_test_cases_satisfy_preconditions
run_test("test_cases_satisfy_preconditions", database: {}) do |test_case|
n = test_case.choice(10)
test_case.reject
test_case.assume(n != 0)
refute_equal 0, n
end
end
end
def test_error_on_unbounded_test_function
orig_buffer_size = BUFFER_SIZE
suppress_warnings do
Minitest::Thesis.const_set(:BUFFER_SIZE, 10)
end
assert_raises(Unsatisfiable) do
run_test("error_on_unbounded_test_function", database: {}, max_examples: 5) do |test_case|
loop do
def test_error_on_too_strict_precondition
assert_raises(Unsatisfiable) do
run_test("error_on_too_strict_precondition", database: {}) do |test_case|
test_case.choice(10)
test_case.reject
end
end
end
ensure
suppress_warnings do
Minitest::Thesis.const_set(:BUFFER_SIZE, orig_buffer_size)
end
end
def test_function_cache
tf = ->(tc) do
tc.mark_status(Status::INTERESTING) if tc.choice(1_000) >= 200
tc.reject if tc.choice(1).zero?
end
state = TestingState.new(random: Random.new(0), test_function: tf, max_examples: 100)
cache = CachedTestFunction.new {|tc| state.test_function(tc) }
def test_error_on_unbounded_test_function
orig_buffer_size = BUFFER_SIZE
suppress_warnings do
Minitest::Thesis.const_set(:BUFFER_SIZE, 10)
end
assert_equal Status::VALID, cache.([1, 1])
assert_equal Status::OVERRUN, cache.([1])
assert_equal Status::INTERESTING, cache.([1_000])
assert_equal Status::INTERESTING, cache.([1_000])
assert_equal Status::INTERESTING, cache.([1_000, 1])
assert_raises(Unsatisfiable) do
run_test("error_on_unbounded_test_function", database: {}, max_examples: 5) do |test_case|
loop do
test_case.choice(10)
end
end
end
ensure
suppress_warnings do
Minitest::Thesis.const_set(:BUFFER_SIZE, orig_buffer_size)
end
end
assert_equal 2, state.calls
end
def test_function_cache
tf = ->(tc) do
tc.mark_status(Status::INTERESTING) if tc.choice(1_000) >= 200
tc.reject if tc.choice(1).zero?
end
# Targeting has a number of places it checks for whether we've exceeded the
# generation limits. This makes sure we've checked them all.
def test_max_examples_is_not_exceeded
(1...100).each do |max_examples|
calls = 0
state = TestingState.new(random: Random.new(0), test_function: tf, max_examples: 100)
cache = CachedTestFunction.new {|tc| state.test_function(tc) }
run_test(
"max_examples_is_not_exceeded",
database: {},
random: Random.new(0),
max_examples:,
) do |tc|
m = 10000
n = tc.choice(m)
calls += 1
tc.target(n * (m - n))
end
assert_equal Status::VALID, cache.([1, 1])
assert_equal Status::OVERRUN, cache.([1])
assert_equal Status::INTERESTING, cache.([1_000])
assert_equal Status::INTERESTING, cache.([1_000])
assert_equal Status::INTERESTING, cache.([1_000, 1])
assert_equal max_examples, calls
assert_equal 2, state.calls
end
end
# Targeting has a number of places it checks for whether we've exceeded the
# generation limits. This makes sure we've checked them all.
def test_max_examples_is_not_exceeded
(1...100).each do |max_examples|
calls = 0
# Targeting has a number of places it checks for whether we've exceeded the
# generation limits. This makes sure we've checked them all.
def test_finds_a_local_maximum
(0...100).each do |seed|
assert_raises(Minitest::Assertion) do
run_test(
"finds_a_local_maximum",
"max_examples_is_not_exceeded",
database: {},
random: Random.new(seed),
max_examples: 200,
quiet: true
random: Random.new(0),
max_examples:,
) do |tc|
m = tc.choice(1000)
n = tc.choice(1000)
score = -((m - 500) ** 2 + (n - 500) ** 2)
tc.target(score)
assert m != 500 || n != 500
m = 10000
n = tc.choice(m)
calls += 1
tc.target(n * (m - n))
end
assert_equal max_examples, calls
end
end
end
def test_can_target_a_score_upwards_to_interesting
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("can_target_a_score_upwards_to_interesting", database: {}, max_examples: 1000) do |test_case|
n = test_case.choice(1000)
m = test_case.choice(1000)
score = n + m
test_case.target(score)
assert score < 2000
# Targeting has a number of places it checks for whether we've exceeded the
# generation limits. This makes sure we've checked them all.
def test_finds_a_local_maximum
(0...100).each do |seed|
assert_raises(Minitest::Assertion) do
run_test(
"finds_a_local_maximum",
database: {},
random: Random.new(seed),
max_examples: 200,
quiet: true
) do |tc|
m = tc.choice(1000)
n = tc.choice(1000)
score = -((m - 500) ** 2 + (n - 500) ** 2)
tc.target(score)
assert m != 500 || n != 500
end
end
end
end
assert_equal <<~OUT, out
choice(1000): 1000
choice(1000): 1000
OUT
end
def test_can_target_a_score_upwards_without_failing
max_score = 0
def test_can_target_a_score_upwards_to_interesting
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("can_target_a_score_upwards_to_interesting", database: {}, max_examples: 1000) do |test_case|
n = test_case.choice(1000)
m = test_case.choice(1000)
score = n + m
test_case.target(score)
assert score < 2000
end
end
end
run_test("can_target_a_score_upwards_without_failing", database: {}, max_examples: 1000) do |test_case|
n = test_case.choice(1000)
m = test_case.choice(1000)
score = n + m
test_case.target(score)
max_score = [score, max_score].max
assert_equal <<~OUT, out
choice(1000): 1000
choice(1000): 1000
OUT
end
assert_equal 2000, max_score
end
def test_can_target_a_score_upwards_without_failing
max_score = 0
def test_targeting_when_most_do_not_benefit
big = 10_000
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("targeting_when_most_do_not_benefit", database: {}, max_examples: 1000) do |test_case|
test_case.choice(1000)
test_case.choice(1000)
score = test_case.choice(big)
test_case.target(score)
assert score < big
end
run_test("can_target_a_score_upwards_without_failing", database: {}, max_examples: 1000) do |test_case|
n = test_case.choice(1000)
m = test_case.choice(1000)
score = n + m
test_case.target(score)
max_score = [score, max_score].max
end
assert_equal 2000, max_score
end
assert_equal <<~OUT, out
choice(1000): 0
choice(1000): 0
choice(#{big}): #{big}
OUT
end
def test_targeting_when_most_do_not_benefit
big = 10_000
def test_can_target_a_score_downwards
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("can_target_a_score_downwards", database: {}, max_examples: 1000) do |test_case|
n = test_case.choice(1000)
m = test_case.choice(1000)
score = n + m
test_case.target(-score)
assert score.positive?
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("targeting_when_most_do_not_benefit", database: {}, max_examples: 1000) do |test_case|
test_case.choice(1000)
test_case.choice(1000)
score = test_case.choice(big)
test_case.target(score)
assert score < big
end
end
end
assert_equal <<~OUT, out
choice(1000): 0
choice(1000): 0
choice(#{big}): #{big}
OUT
end
assert_equal <<~OUT, out
choice(1000): 0
choice(1000): 0
OUT
end
def test_can_target_a_score_downwards
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("can_target_a_score_downwards", database: {}, max_examples: 1000) do |test_case|
n = test_case.choice(1000)
m = test_case.choice(1000)
score = n + m
test_case.target(-score)
assert score.positive?
end
end
end
def test_prints_a_top_level_weighted
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("prints_a_top_level_weighted", database: {}, max_examples: 1000) do |test_case|
assert test_case.weighted(0.5).nonzero?
assert_equal <<~OUT, out
choice(1000): 0
choice(1000): 0
OUT
end
def test_prints_a_top_level_weighted
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("prints_a_top_level_weighted", database: {}, max_examples: 1000) do |test_case|
assert test_case.weighted(0.5).nonzero?
end
end
end
assert_equal <<~OUT, out
weighted(0.5): 0
OUT
end
assert_equal <<~OUT, out
weighted(0.5): 0
OUT
end
def test_errors_when_using_frozen
tc = TestCase.for_choices([0])
tc.status = Status::VALID
def test_errors_when_using_frozen
tc = TestCase.for_choices([0])
tc.status = Status::VALID
assert_raises(Frozen) do
tc.mark_status(Status::INTERESTING)
end
assert_raises(Frozen) do
tc.mark_status(Status::INTERESTING)
end
assert_raises(Frozen) do
tc.choice(10)
end
assert_raises(Frozen) do
tc.choice(10)
assert_raises(Frozen) do
tc.forced_choice(10)
end
end
assert_raises(Frozen) do
tc.forced_choice(10)
def test_errors_on_too_large_choice
tc = TestCase.for_choices([0])
assert_raises(RangeError) do
tc.choice(2 ** 64)
end
end
end
def test_errors_on_too_large_choice
tc = TestCase.for_choices([0])
assert_raises(RangeError) do
tc.choice(2 ** 64)
def test_can_choose_full_64_bits
run_test("can_choose_full_64_bits", database: {}) do |tc|
tc.choice(2 ** 64 - 1)
end
end
end
def test_can_choose_full_64_bits
run_test("can_choose_full_64_bits", database: {}) do |tc|
tc.choice(2 ** 64 - 1)
def test_mapped_possibility
run_test("mapped_possibility", database: {}) do |tc|
n = tc.any(integers(0, 5).map {|n| n * 2 })
assert n.even?
end
end
end
def test_mapped_possibility
run_test("mapped_possibility", database: {}) do |tc|
n = tc.any(integers(0, 5).map {|n| n * 2 })
assert n.even?
def test_selected_possibility
run_test("selected_possibility", database: {}) do |tc|
n = tc.any(
integers(0, 5)
.satisfying(&:even?)
)
assert n.even?
end
end
end
def test_selected_possibility
run_test("selected_possibility", database: {}) do |tc|
n = tc.any(
integers(0, 5)
.satisfying(&:even?)
)
assert n.even?
def test_bound_possibility
run_test("bound_possibility", database: {}) do |tc|
m, n = tc.any(
integers(0, 5).bind {|m| tuples(just(m), integers(m, m + 10)) }
)
assert (m..m+10).cover?(n)
end
end
end
def test_bound_possibility
run_test("bound_possibility", database: {}) do |tc|
m, n = tc.any(
integers(0, 5).bind {|m| tuples(just(m), integers(m, m + 10)) }
)
assert (m..m+10).cover?(n)
def test_cannot_witness_nothing
assert_raises(Unsatisfiable) do
run_test("cannot_witness_nothing", database: {}) do |tc|
tc.any(nothing)
end
end
end
end
def test_cannot_witness_nothing
assert_raises(Unsatisfiable) do
run_test("cannot_witness_nothing", database: {}) do |tc|
tc.any(nothing)
def test_cannot_witness_empty_mix_of
assert_raises(Unsatisfiable) do
run_test("cannot_witness_empty_mix_of", database: {}) do |tc|
tc.any(mix_of)
end
end
end
end
def test_cannot_witness_empty_mix_of
assert_raises(Unsatisfiable) do
run_test("cannot_witness_empty_mix_of", database: {}) do |tc|
tc.any(mix_of)
def test_can_draw_mixture
run_test("can_draw_mixture", database: {}) do |tc|
m = tc.any(mix_of(integers(-5, 0), integers(2, 5)))
assert (-5..5).cover?(m)
refute_equal 1, m
end
end
end
def test_can_draw_mixture
run_test("can_draw_mixture", database: {}) do |tc|
m = tc.any(mix_of(integers(-5, 0), integers(2, 5)))
assert (-5..5).cover?(m)
refute_equal 1, m
# This test is very hard to trigger without targeting, and targeting will
# tend to overshoot the score, so we will see multiple interesting test cases
# before shrinking.
def test_target_and_reduce
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("target_and_reduce", database: {}) do |tc|
m = tc.choice(100_000)
tc.target(m)
assert m <= 99_900
end
end
end
assert_equal <<~OUT, out
choice(100000): 99901
OUT
end
end
# This test is very hard to trigger without targeting, and targeting will
# tend to overshoot the score, so we will see multiple interesting test cases
# before shrinking.
def test_target_and_reduce
out, _ = capture_io do
assert_raises(Minitest::Assertion) do
run_test("target_and_reduce", database: {}) do |tc|
m = tc.choice(100_000)
tc.target(m)
assert m <= 99_900
def test_impossible_weighted
assert_raises(Failure) do
run_test("impossible_weighted", database: {}, quiet: true) do |tc|
tc.choice(1)
10.times do
assert false unless tc.weighted(0.0).zero?
end
raise Failure if tc.choice(1).zero?
end
end
end
assert_equal <<~OUT, out
choice(100000): 99901
OUT
end
def test_impossible_weighted
assert_raises(Failure) do
run_test("impossible_weighted", database: {}, quiet: true) do |tc|
tc.choice(1)
10.times do
assert false unless tc.weighted(0.0).zero?
def test_guaranteed_weighted
assert_raises(Failure) do
run_test("guaranteed_weighted", database: {}, quiet: true) do |tc|
if tc.weighted(1.0).nonzero?
tc.choice(1)
raise Failure
else
assert false
end
end
raise Failure if tc.choice(1).zero?
end
end
end
def test_guaranteed_weighted
assert_raises(Failure) do
run_test("guaranteed_weighted", database: {}, quiet: true) do |tc|
if tc.weighted(1.0).nonzero?
tc.choice(1)
raise Failure
else
assert false
end
def test_size_bounds_on_list
run_test("size_bounds_on_list", database: {}) do |tc|
ls = tc.any(lists(integers(0, 10), min_size: 1, max_size: 3))
assert (1..3).cover?(ls.length)
end
end
end
def test_size_bounds_on_list
run_test("size_bounds_on_list", database: {}) do |tc|
ls = tc.any(lists(integers(0, 10), min_size: 1, max_size: 3))
assert (1..3).cover?(ls.length)
def test_forced_choice_bounds
assert_raises(RangeError) do
run_test("forced_choice_bounds", database: {}) do |tc|
tc.forced_choice(2 ** 64)
end
end
end
end
def test_forced_choice_bounds
assert_raises(RangeError) do
run_test("forced_choice_bounds", database: {}) do |tc|
tc.forced_choice(2 ** 64)
def test_failure_from_hypothesis_1
assert_raises(Failure) do
run_test("failure_from_hypothesis_1", database: {}, random: Random.new(100), max_examples: 1000, quiet: true) do |tc|
n1 = tc.weighted(0.0)
if n1.zero?
n2 = tc.choice(511)
if n2 == 112
n3 = tc.choice(511)
if n3 == 124
raise Failure
elsif n3 == 93
raise Failure
else
tc.mark_status(Status::INVALID)
end
elsif n2 == 93
raise Failure
else
tc.mark_status(Status::INVALID)
end
end
end
end
end
end
def test_failure_from_hypothesis_1
assert_raises(Failure) do
run_test("failure_from_hypothesis_1", database: {}, random: Random.new(100), max_examples: 1000, quiet: true) do |tc|
n1 = tc.weighted(0.0)
if n1.zero?
n2 = tc.choice(511)
if n2 == 112
n3 = tc.choice(511)
if n3 == 124
def test_failure_from_hypothesis_2
assert_raises(Failure) do
run_test("failure_from_hypothesis_2", database: {}, random: Random.new(0), max_examples: 1000, quiet: true) do |tc|
n1 = tc.choice(6)
if n1 == 6
n2 = tc.weighted(0.0)
if n2.zero?
raise Failure
elsif n3 == 93
end
elsif n1 == 4
n3 = tc.choice(0)
if n3 == 0
raise Failure
else
tc.mark_status(Status::INVALID)
end
elsif n2 == 93
elsif n1 == 2
raise Failure
else
tc.mark_status(Status::INVALID)
@ -444,82 +471,57 @@ class Minitest::ThesisTest < Minitest::Thesis::Test
end
end
end
end
def test_failure_from_hypothesis_2
assert_raises(Failure) do
run_test("failure_from_hypothesis_2", database: {}, random: Random.new(0), max_examples: 1000, quiet: true) do |tc|
n1 = tc.choice(6)
if n1 == 6
n2 = tc.weighted(0.0)
if n2.zero?
raise Failure
end
elsif n1 == 4
n3 = tc.choice(0)
if n3 == 0
raise Failure
def test_refactoring_cache
old = ->(node, choices, status) {
choices.each.with_index do |c, i|
if i + 1 < choices.length || status == Status::OVERRUN
node = if node.has_key?(c)
node[c]
else
node[c] = {}
end
else
tc.mark_status(Status::INVALID)
node[c] = status
end
elsif n1 == 2
raise Failure
else
tc.mark_status(Status::INVALID)
end
end
end
end
}
def test_refactoring_cache
old = ->(node, choices, status) {
choices.each.with_index do |c, i|
if i + 1 < choices.length || status == Status::OVERRUN
new = ->(node, choices, status) {
*rest, last = choices
rest.each do |c|
node = if node.has_key?(c)
node[c]
else
node[c] = {}
end
else
node[c] = status
end
end
}
new = ->(node, choices, status) {
*rest, last = choices
rest.each do |c|
node = if node.has_key?(c)
node[c]
else
node[c] = {}
end
end
unless last.nil?
node[last] = status == Status::OVERRUN ? {} : status
end
}
unless last.nil?
node[last] = status == Status::OVERRUN ? {} : status
end
}
run_test("refactoring_cache", database: {}) do |tc|
old_tree = {}
new_tree = {}
run_test("refactoring_cache", database: {}) do |tc|
old_tree = {}
new_tree = {}
choices = tc.any(lists(integers(0, 10)))
status = Status.new(tc.choice(4))
choices = tc.any(lists(integers(0, 10)))
status = Status.new(tc.choice(4))
old.(old_tree, choices, status)
new.(new_tree, choices, status)
old.(old_tree, choices, status)
new.(new_tree, choices, status)
assert_equal old_tree, new_tree
assert_equal old_tree, new_tree
end
end
end
private
private
def suppress_warnings
original_verbosity = $VERBOSE
$VERBOSE = nil
yield
$VERBOSE = original_verbosity
def suppress_warnings
original_verbosity = $VERBOSE
$VERBOSE = nil
yield
$VERBOSE = original_verbosity
end
end
end

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