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make-it-easy is a tiny framework that makes it easy to write Test Data Builders in Python. The framework is a port of the Java make-it-easy by Nat Pryce_

.. _Java make-it-easy by Nat Pryce: https://code.google.com/p/make-it-easy/

Test Data Builders are described in the book Growing Object-Oriented Software, Guided by Tests by Steve Freeman and Nat Pryce. This library lets you write Test Data Builders with much less duplication and boilerplate code than the approach described in the book.

make-it-easy can be installed using the usual Python packaging tools. It depends on distribute, but as long as you have a network connection when you install, the installation process will take care of that for you.

Consider the following class hierarchy. This hierarchy illustrates a couple of complicating factors: there is an abstract base class Fruit and there is a property (ripeness) that is not set via the constructor but by an operation of the Fruit class.

.. code:: python

class Fruit(object): def init(self): self._ripeness = 0.0

 def ripen(self, ripeness):
     self._ripeness = ripeness

 @property
 def is_ripe(self):
     return self._ripeness >= 0.9

class Apple(Fruit): def init(self, num_of_leaves): super(Apple, self).init() self.num_of_leaves = num_of_leaves

class Banana(Fruit): def init(self, curve): super(Banana, self).init() self.curve = curve

Doing so in the style documented in Growing Object-Oriented Software, Guided by Tests would look like this:

.. code:: python

class AppleBuilder(object): def init(self): self._ripeness = 0.5 self._leaves = 2

 def with_ripeness(self, ripeness):
     self._ripeness = ripeness
     return self
 
 def with_leaves(self, leaves):
     self._leaves = leaves
     return self

 def but(self):
     return AppleBuilder() \
         .with_ripeness(self._ripeness) \
         .with_leaves(self._leaves)
         
 def build(self):
     apple = Apple(self._leaves)
     apple.ripen(self._ripeness)
     return apple

class BananaBuilder(object): def init(self): self._ripeness = 0.5 self._curve = 0.1

 def with_ripeness(self, ripeness):
     self._ripeness = ripeness
 
 def with_curve(self, curve):
     self._curve = curve
    
 def but(self):
     return BananaBuilder() \
         .with_ripeness(self._ripeness) \
         .with_curve(self._curve)
         
 def build(self):
     banana = Banana()
     banana.ripen(self._ripeness)
     banana.curve = self._curve
     return banana

apple_with_two_leaves = AppleBuilder().with_leaves(2) ripe_apple = apple_with_two_leaves.but().with_ripeness(0.95) unripe_apple = apple_with_two_leaves.but().with_ripeness(0.1)

apple1 = ripe_apple.build() apple2 = unripe_apple.build()

While doing it with make-it-easy can be easy as that:

.. code:: python

from make_it_easy import *

def apple(leaves=2, ripeness=0.0): an_apple = Apple(leaves) an_apple.ripen(ripeness) return an_apple

def banana(curve=0.1, ripeness=0.0): a_banana = Banana(curve) a_banana.ripen(ripeness) return a_banana

apple_with_two_leaves = an(apple, with_(2, 'leaves')) ripe_apple = apple_with_two_leaves.but(with_(0.95, 'ripeness')) unripe_apple = apple_with_two_leaves.but().with_(0.1, 'ripeness'))

apple1 = make(ripe_apple) apple2 = make(unripe_apple)

As you can see, with Make It Easy you have to write a lot less duplicated and boilerplate code.

A value donor is any primitive ('Bob' / 3 / False / etc.) or a Maker (the returned object from the a, an functions). All these can be used as the value in with_. For instance a customer Maker can be a donor in order Maker. It's important to notice that if a Maker is used as a donor, a new instance will be created every time:

.. code:: python

a_customer = a(customer, with_('Bob', as_('name'))) an_order = an(order, with_(a_customer, as_('customer'))) my_order1 = make(an_order) my_order2 = make(an_order) assert_that(my_order1.customer, is_(instance_of(Customer))) assert_that(my_order2.customer, is_(instance_of(Customer))) assert_that(my_order1.customer, is_not(same_instance(my_order2.customer))) # two different instances!!!

Sometimes you will need to share the same value while making new data objects, this can be done using the_same value donor. In the following example both my_order1 and my_order2 will have the same customer instance:

.. code:: python

a_customer = a(customer, with_('Bob', as_('name'))) an_order = an(order, with_(the_same(a_customer), as_('customer'))) my_order1 = make(an_order) my_order2 = make(an_order) assert_that(my_order1.customer, is_(same_instance(my_order2.customer)))

In order to create a custom donor, you will simply need to implement the Donor interface.

.. code:: python

class IndexDonor(Donor): def init(self): self._count = itertools.count()

 @property
 def value(self):
    return next(self._count)

an_indexed_thing = an(an_indexed_thing, with_(IndexDonor(), as_('index'))) indexed_thing1 = make(an_indexed_thing) indexed_thing2 = make(an_indexed_thing) assert_that(indexed_thing1.index, is_(equal_to(0))) assert_that(indexed_thing2.index, is_(equal_to(1)))

Sometimes we want values to be allocated from a sequence, so we can predict their values or understand where data has come from in test diagnostics. make-it-easy lets you define a fixed or repeating sequence of values.

A fixed sequence is defined by the from_ function which expects an iterable:

.. code:: python

letters = from_("abc") assert_that(letters.value, is_(equal_to("a"))) assert_that(letters.value, is_(equal_to("b"))) assert_that(letters.value, is_(equal_to("c")))

A fixed sequence of values will fail if asked to provide more elements than are specified when the sequence is created. A repeating sequence will start back at the beginning of the sequence when all elements are exhausted:

.. code:: python

letters = from_repeating("ab") assert_that(letters.value, is_(equal_to("a"))) assert_that(letters.value, is_(equal_to("b"))) assert_that(letters.value, is_(equal_to("a"))) assert_that(letters.value, is_(equal_to("b"))) assert_that(letters.value, is_(equal_to("a"))) assert_that(letters.value, is_(equal_to("b")))

Both fixed and repeating sequences can be created from any iterable (tuple / list / set / dict / etc.).

If we do not want to explicitly specify a sequence of values, we can use some convenient base classes to help us calculate each element of the sequence. An IndexedSequence calculates each element of the sequence from its integer index, starting at zero.

.. code:: python

class FTagSequence(IndexedSequence): def _value_at(self, index): return 'f' + "'" * index

f_tag_sequence = FTagSequence() assert_that(f_tag_sequence.value, is_(equal_to("f"))) assert_that(f_tag_sequence.value, is_(equal_to("f'"))) assert_that(f_tag_sequence.value, is_(equal_to("f''")))

A ChainedSequence calculates each element of the sequence from the element that preceded it.

.. code:: python

class FTagChainedSequence(ChainedSequence): def _first_value(self): return 'f'

 def _value_after(self, prev_value):
     return prev_value + "'"

f_tag_sequence = FTagChainedSequence() assert_that(f_tag_sequence.value, is_(equal_to("f"))) assert_that(f_tag_sequence.value, is_(equal_to("f'"))) assert_that(f_tag_sequence.value, is_(equal_to("f''")))