diff --git a/test/test_helper.rb b/test/test_helper.rb
new file mode 100644
index 0000000..1a69950
--- /dev/null
+++ b/test/test_helper.rb
@@ -0,0 +1,1208 @@
+require "minitest/autorun"
+
+module Minitest
+
+  class Property < Test
+
+    # Runs a test. Usage is:
+    #
+    # run_test do |test_case|
+    #   n = test_case.choice(1000)
+    # end
+    #
+    # The block takes a `TestCase` argument, and should raise an exception to
+    # indicate a test failure. It will either run silently or print drawn
+    # values and then fail with an exception if minithesis finds some test case
+    # that fails.
+    #
+    # Arguments:
+    # * max_examples: the maximum number of valid test cases to run for.
+    #   Note that under some circumstances the test may run fewer test
+    #   cases than this.
+    # * random: An instance of random.Random that will be used for all
+    #   nondeterministic choices.
+    # * database: A Hash-like object in which results will be cached and resumed
+    #   from, ensuring that if a test is run twice it fails in the same way.
+    # * quiet: Will not print anything on failure if True.
+    def run_test(
+      name,
+      max_examples: 100,
+      random: Random.new,
+      database: DirectoryDb.new(".minitest-thesis-cache"),
+      quiet: false,
+      &test
+    )
+      mark_failures_interesting = ->(test_case) do
+        test.(test_case)
+      rescue Exception
+        raise unless test_case.status.nil?
+
+        test_case.mark_status(Status::INTERESTING)
+      end
+
+      state = TestingState.new(random:, test_function: mark_failures_interesting, max_examples:)
+
+      prev_failure = database[name]
+
+      unless prev_failure.nil?
+        choices = prev_failure.unpack("Q>*")
+        state.test_function(TestCase.for_choices(choices))
+      end
+
+      if state.result.nil?
+        state.run
+      end
+
+      if state.valid_test_cases.zero?
+        raise Unsatisfiable
+      end
+
+      if state.result.nil?
+        database.delete(name)
+      else
+        database[name] = state.result.pack("Q>*")
+      end
+
+      unless state.result.nil?
+        test.(TestCase.for_choices(state.result, print_results: !quiet))
+      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)
+        if p.zero? || p.negative?
+          result = forced_choice(0)
+        elsif p >= 1
+          result = forced_choice(1)
+        else
+          result = 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(produce, name: "TODO")
+        @produce = produce
+        @name = name
+      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(
+          ->(test_case) { f.(test_case.any(self)) },
+          name: "#{name}.map(TODO)",
+        )
+      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)
+        produce = ->(test_case) { test_case.any(f.(test_case.any(self))) }
+        self.class.new(produce, name: "#{name}.bind(TODO)")
+      end
+
+      # Returns a `Possibility` whose values are any possible value of `self`
+      # for which `f` returns True.
+      def satisfying(&f)
+        produce = ->(test_case) {
+          3.times do
+            candidate = test_case.any(self)
+            return candidate if f.(candidate)
+          end
+          test_case.reject
+        }
+
+        self.class.new(produce, name: "#{name}.select(TODO)")
+      end
+    end
+
+    # Any integer in the range [m, n] is possible
+    def integers(m, n) = Possibility.new(->(tc) { m + tc.choice(n - m) }, name: "integers(#{m}, #{n})")
+
+    # Any lists whose elements are possible values from `elements` are possible.
+    def lists(elements, min_size: 0, max_size: Float::INFINITY)
+      produce = ->(test_case) {
+        result = []
+        loop do
+          if result.length < min_size
+            test_case.forced_choice(1)
+          elsif result.length + 1 >= max_size
+            test_case.forced_choice(0)
+            break
+          elsif test_case.weighted(0.9).zero?
+            break
+          end
+          result << test_case.any(elements)
+        end
+        result
+      }
+
+      Possibility.new(produce, name: "lists(#{elements.name})")
+    end
+
+    # Only `value` is possible.
+    def just(value) = Possibility.new(->(_) { value }, name: "just(#{value})")
+
+    # No possible values. i.e. Any call to `any` will reject the test case.
+    def nothing = Possibility.new(->(tc) { tc.reject })
+
+    # Possible values can be any value possible for one of `possibilities`.
+    def mix_of(*possibilities)
+      return nothing if possibilities.empty?
+
+      Possibility.new(
+        ->(tc) { tc.any(possibilities[tc.choice(possibilities.length - 1)]) },
+        name: "mix_of(#{possibilities.map(&:name).join(", ")})",
+      )
+    end
+
+    # Any tuple t of of length len(possibilities) such that t[i] is possible
+    # for possibilities[i] is possible.
+    def tuples(*possibilities)
+      Possibility.new(
+        ->(tc) { possibilities.map {|p| tc.any(p) } },
+        name: "tuples(#{possibilities.map(&:name).join(", ")})",
+      )
+    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 init(&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 isinstance(node, Status):
+            #     assert node != Status.OVERRUN
+            #     return node
+          end
+        rescue KeyError
+        end
+      end
+
+      # def __call__(self, choices: Sequence[int]) -> Status:
+      #     # XXX The type of node is problematic
+      #     node: Any = self.tree
+      #     try:
+      #         for c in choices:
+      #             node = node[c]
+      #             # mark_status was called, thus future choices
+      #             # will be ignored.
+      #             if isinstance(node, Status):
+      #                 assert node != Status.OVERRUN
+      #                 return node
+      #         # 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
+      #     except KeyError:
+      #         pass
+
+      #     # We now have to actually call the test function to find out
+      #     # what happens.
+      #     test_case = TestCase.for_choices(choices)
+      #     self.test_function(test_case)
+      #     assert test_case.status is not None
+
+      #     # We enter the choices made in a tree.
+      #     node = self.tree
+      #     for i, c in enumerate(test_case.choices):
+      #         if i + 1 < len(test_case.choices) or test_case.status == Status.OVERRUN:
+      #             try:
+      #                 node = node[c]
+      #             except KeyError:
+      #                 node = node.setdefault(c, {})
+      #         else:
+      #             node[c] = test_case.status
+      #     return test_case.status
+    end
+
+    class TestingState
+      attr_reader :result, :valid_test_cases
+
+      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(self.test_function)
+
+        # def consider(choices: array[int]) -> bool:
+        #     if choices == self.result:
+        #         return True
+        #     return cached(choices) == Status.INTERESTING
+        consider = ->(choices) do
+          return true if choices == @result
+
+          test_function(TestCase.for_choices(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(directory)
+      end
+    end
+
+    # Attempted to make choices on a test case that has been completed.
+    class Frozen < StandardError; end
+
+    # Raised when a test should stop executing early.
+    class StopTest < StandardError; end
+
+    # Raised when a test has no valid examples.
+    class Unsatisfiable < StandardError; end
+
+    module Status
+      # Test case didn't have enough data to complete
+      OVERRUN = 0
+
+      # Test case contained something that prevented completion
+      INVALID = 1
+
+      # Test case completed just fine but was boring
+      VALID = 2
+
+      # Test case completed and was interesting
+      INTERESTING = 3
+    end
+  end
+end
+
+class TestProperty < Minitest::Property
+  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
+
+  # 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(
+      ->(tc) { n = tc.choice(10); Array.new(n) { tc.choice(10_000) }},
+      name: "bad_list",
+    )
+
+    (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
+
+  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(tmpdir):
+  #     db = DirectoryDB(tmpdir)
+  #     count = 0
+
+  #     def run():
+  #         with pytest.raises(AssertionError):
+
+  #             @run_test(database=db)
+  #             def _(test_case):
+  #                 nonlocal count
+  #                 count += 1
+  #                 assert test_case.choice(10000) < 10
+
+  #     run()
+
+  #     assert len(tmpdir.listdir()) == 1
+  #     prev_count = count
+
+  #     run()
+
+  #     assert len(tmpdir.listdir()) == 1
+  #     assert count == prev_count + 2
+
+  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
+    end
+  end
+
+  def test_error_on_too_strict_precondition
+    assert_raises(Unsatisfiable) do
+      run_test("error_on_too_strict_precondition", database: {}) do |test_case|
+        n = test_case.choice(10)
+        test_case.reject
+      end
+    end
+  end
+
+  def test_error_on_unbounded_test_function
+    # TODO Make the warnings go away
+    orig_buffer_size = Minitest::Property::BUFFER_SIZE
+    Minitest::Property.const_set(:BUFFER_SIZE, 10)
+
+    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
+    Minitest::Property.const_set(:BUFFER_SIZE, orig_buffer_size)
+  end
+
+  # def test_function_cache():
+  #     def tf(tc):
+  #         if tc.choice(1000) >= 200:
+  #             tc.mark_status(Status.INTERESTING)
+  #         if tc.choice(1) == 0:
+  #             tc.reject()
+
+  #     state = State(Random(0), tf, 100)
+
+  #     cache = CachedTestFunction(state.test_function)
+
+  #     assert cache([1, 1]) == Status.VALID
+  #     assert cache([1]) == Status.OVERRUN
+  #     assert cache([1000]) == Status.INTERESTING
+  #     assert cache([1000]) == Status.INTERESTING
+  #     assert cache([1000, 1]) == Status.INTERESTING
+
+  #     assert state.calls == 2
+
+  # 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
+
+      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 max_examples, 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_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
+
+  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
+
+    assert_equal <<~OUT, out
+      choice(1000): 1000
+      choice(1000): 1000
+    OUT
+  end
+
+  def test_can_target_a_score_upwards_without_failing
+    max_score = 0
+
+    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
+
+  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
+      end
+    end
+
+    assert_equal <<~OUT, out
+      choice(1000): 0
+      choice(1000): 0
+      choice(#{big}): #{big}
+    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
+
+    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
+
+  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.choice(10)
+    end
+
+    assert_raises(Frozen) do
+      tc.forced_choice(10)
+    end
+  end
+
+  def test_errors_on_too_large_choice
+    tc = TestCase.for_choices([0])
+    assert_raises(RangeError) do
+      tc.choice(2 ** 64)
+    end
+  end
+
+  def test_can_choose_full_64_bits
+    run_test("can_choose_full_64_bits", database: {}) do |tc|
+      tc.choice(2 ** 64 - 1)
+    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?
+    end
+  end
+
+  def test_selected_possibility
+    run_test("selected_possibility", database: {}) do |tc|
+      n = tc.any(integers(0, 5).satisfying(&:even?))
+      assert n.even?
+    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)
+    end
+  end
+
+  def test_cannot_witness_nothing
+    assert_raises(Unsatisfiable) do
+      run_test("cannot_witness_nothing", database: {}) do |tc|
+        tc.any(nothing)
+      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)
+      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
+    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
+        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?
+        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
+      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)
+    end
+  end
+
+  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
+
+  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
+
+  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
+          else
+            tc.mark_status(Status::INVALID)
+          end
+        elsif n1 == 2
+          raise Failure
+        else
+          tc.mark_status(Status::INVALID)
+        end
+      end
+    end
+  end
+end