Encapsulating conditional logic is an easily identifiable refactor, which helps with readability, testability, and can help insulate calling code from changes to business requirements. I frequently observe code with business logic embedded directly into decision logic without the use of abstractions, which leads to unintutive, brittle, and difficult to test code because:

conditionals frequently involve magic numbers

when business requirements change, decision logic with multiple conditionals has to be updated, resulting in risky changes

isloating specific logic branches is difficult and often requires data that is not directly related to the logic branch

To illustrate this, suppose we have an online ordering system. There is a check out routine where the bulk of client ordering logic takes place:

def calculate_order_total(items, user, state, tax_rate, coupon_code): if user is None:

log_exception()

return



if len(items) == 0:

log_exception()

return



# calculate total

if state == 'district_of_columbia' and len(items) > 10 and set(items) == 1:

# DC bulk order discount

total = dc_bulk_order_discount(items)

else:

total = sum(i.price for i in items)



if len(items) > 20:

# heavy shipment surcharge

total += 20.00



# calculate tax

if state == 'MD' or state == 'DE':

if user.location == 'is_city':

tax_rate += .01 # municiple tax rate surcharge

elif state == 'CA' or state == 'WA' or state == 'OR':

# west coast

tax_rate = '.10' # flat tax



# apply coupon

if coupon_code is not None and coupon_code == 'BEST10DISC':

total -= total * 0.10

This pattern frequently emerges in the wild, and is very easy to spot. It manifests as a long chain of conditionals, or embedded conditionals, and usually involves literals and magic numbers. Since business logic IS centralized here it’s obvious that changes to requirements need to occur here. Because core business logic is centralized here it will often be a very commited to piece of code, and will probably frequently result in bugs. There are a series of small safe refactorings which can significantly improve maintainability through increased testability.

The example above is actually no where near as difficult as some of the code seen in the wild. I’ve seen a multimillion dollar company have code like:

if e.use_case == 11 and e.sub_type == 1:

# do something

elif e.use_case == 1 or e.use_case == 2:

if e.subtype not in [2, 4, 11]:

# do something



# way more ...

Refactoring to significantly increase comprehensibility, safety and support testability is a relatively safe operation. The conditional logic should be encapsulated in a method and extracted. Martin Fowler identifies this pattern in “Refactoring” as “Extract Method”. This can be done by copying a conditional pasting it in a method describing what the conditional does, and parameterizing the method to take the required values.

To illustrate using the first example above:

state == ‘district_of_columbia’ and len(items) > 10 and set(items) == 1

Would become:

def is_dc_bulk_order(state, items):

return state == 'district_of_columbia' and len(items) > 10 and set(items) == 1

And then the conditional becomes:

if is_dc_bulk_order(state, item):

total = dc_bulk_order_discount(items)

Since the method name describes the conditional, there is no longer any need for comments, removing the maintanence required to keep comments from becoming stale.

Applying this to every single conditional creates a code block which reads like a series of comments, and isolates magic numbers to only the routines that have to worry about it. Additionally, when business requirements change, less code has to be reasoned about and verified to confidently make a change.

Take the follow example:

if state == 'CA' or state == 'WA' or state == 'OR':

The above represents west coast states. When business requirements change to consider Alaska as a west coast state the calculate_order_total method will need to be changed, and retested. If the logic is encapsulated as:

def is_west_coast_state(state):

return state == 'CA' or state == 'WA' or state == 'OR'

The scope of the change is isolated to a very focused function, and the risk too is better controlled.

Supporting Testability

So what’s the problem? The first example is synchronous, with no IO. It is easy realitvely straightforward to configure test objects and exercise all code paths. Even though it is completely achievable to 100% coverage this routine, when different responsibilities are embededded inthe same routine it becomes difficult to isolate functionality in testing.

To test if the code can identify a west coast state correctly, we need to configure items, user, tax_rate, and a coupon code. Additionall, we have to execute 4 conditionals before we get to the code we are testing. This leads to brittle tests. If a new code flow is introduced, or a regression occurs in a previous statement our test for west coast state will fail. Having a test fail for an unrelated case takes human time to debug and is a waste of human resources. Another downside is actually isolating the code under test. The above conditionals are flat and relatively easy to excercise the west coast detection conditional. But if the code had nested conditionals, or more complicated code flow, it could be really difficult to exercise specific code paths. Magic values may have to be mirrored in the tests.

Let’s look at an example of a unit test excercising the west coast detection logic in the first example:

def test_is_west_coast_state_identified(self):

total = calculate_order_total(items=[test_item], user=test_user, state='CA', tax_rate=0, coupon_code=None)

# item cost has to be a number that would result in a unique total that would verify that

# the west coast tax rate was actually applied

self.assertEqual(total, UNIQUE_WEST_COAST_TOTAL)

Because west coast logic isn’t a discreet unit the test can only indirectly exercise it. The only information the assertion has is that the correct tax rate has been applied based on the total returned. This is a brittle operation, and relies on no other code paths/input combination resulting in the same total. Or else the test will result in a false positive. Not only that, but the test needs to remain aware of the complicated implementation logic of calculate_order_total . It needs a valid user, items can't be more than 20, etc. Any change in the logic could result in this test failing. Since the logic is complicated, and the assertion is indirect, a false positive will most likely require a debugger to step line by line the code to determine why it is failing. And even if the failure cause can be determined from other means it will be wasted time, because the failure will NOT be related to the code's ability to determine if the state qualifies as a west coast state!

Contrast the above test with a test for the refactored is_west_coast_state method.

def test_is_west_coast_state(self):

self.assertTrue(is_west_coast_state(state='CA')



def test_is_not_west_coast_state(self):

self.assertFalse(is_west_coast_state(state='DE')

It is now trivial to verify that the code can accurately determine a west coast state. The tests are so incredibly simple they seem unecessary. Code like this is a perfect candidate for tests because it is so simple. When the code is so focused and tests are so simple, test suite maintainence is at a minimum. The tests can be written once in minutes, and continue to provide near instantaneous value and regression protection that the code can identify a west coast state. Additionally, the tests can easily cover every clause in the conditional.

When routines are composoed of many small subroutines there should still be a select few tests which excercise that the main routine generally works to accomplish its goal, and that its subroutines colloborate correctly.

Now when business requires that Alaska is added as a west coast state, the modification takes place in a small, isolated method, that is extremely easy to verify and reason about. Having 1 or 2 sociable tests which cover calculate_order_total should be enough to confidently update is_west_coast_state to include AK , without being concerned about introducing regressions.

Summary

Encapsulated conditional logic is an easy win for testability. It creates easy to test discreet units, it promotes self documenting code, conditionals are easy to reason about, all while minimizing the impact of changes. Additionally, it cleans up complex buisness logic by providing methods which can incorporate domain terminology. Code with encapsulated conditionals is easier to test, and easier to understand than code without.

Happy Testing!