Test Infected - Functional Ignorance

Introduction

In this series of Test Infected, I will show you how we can increase the Test Ignorance of our tests by applying Functional approaches to our Imperative code.
If you don’t quite understand what I mean with “ignorance”, I recommend my previous post about the topic. In this post, we will go through with the journey of increasing the Code’s Intent by increasing the Ignorance in a Functional way.

Functional Ignorance

Fixture

The fixture-phase of your test can become very large, several previous posts have already proved this.
How can functional programming help?
Well, let’s assume you want to setup an object with some properties, you would:

• Declare a new variable
• Initialize the variable with a newly created instance of the type of the variable
• Assign the needed properties to setup the fixture

Note that we’re most interested in our test in the last item; so how can we make sure that the last part is the most visible?

Following example shows what I mean:

We would like to test something with the subject property of the message, but note that this is not the first thing which catches your eye (especially if we use the object-initializer syntax). We must also initialize something in a context.

We could, of course, extract the creation functionality with a Parameterized Creation Method and extract the insertion functionality that accepts a message instance.

But note that we do not use the message elsewhere in the test. We could extract the whole functionality and just accept the subject name, but we will have to use an explicit method name to make clear that we will insert a message in the context AND will assign the given subject name to that inserted message. What if we want to test something else? Another explicit method?

What I sometimes do is extract only the assigning functionality like this:

We don’t use the name of the method to state our intentions, we use our code.

In the extracted method, we can do whatever necessary to create an ignored message. If we do need another way to create a message initially, we can always create a new method that only inserts the incoming message and call this from our functional method.

If would be nice if we had immutable values and could use something like F# “Copy-And-Replace Expressions“.

Exercise

Several times, when you want to test your code branches from an external SUT endpoint, the creation of the SUT doesn’t change, but rather the info you send to the endpoint. Since we have a value that does not change across several tests; we could say that the value is not that important to the test case but rather the changing values.

When you come across such a scenario, you can use the approach I will describe in here.

The idea is to split the exercise logic from the SUT creation. If you have different endpoints you want to test for the same SUT fixture, you can even extend this approach by letting the client code decide what endpoint to call.

Following example shows two test cases where the SUT creation is the same:

Note that we have the same pattern: (1) create SUT, (2) exercise SUT. Compare with the following code where the SUT is being exercised differently.

We ignore the unnecessary info by Functional Thinking:

We can extend this idea by letting the client choose the return value. This is rather useful if we want to test the SUT with the same Fixture but with different member calls:

I use this approach in almost every Class Test I write. This idea is simple: Encapsulate what varies. Only we think in Functions rather than in Objects. Functions can be treated as Objects!

Verification

The last topic I will discuss in a Functional approach is the Result Verification phase of the Four-Phase Test.

When I applied some techniques in this phase, I always come back to the same principle: I ask myself the same question: “What is really important?” What interests me the most?

In the Result Verification phase, this is the Assertion itself. WHAT do you assert in the test to make it a Self-Evaluating Test? What makes the test succeed or fail?
That’s what’s important; all the other clutter should be removed.

A good example (I think) is when I needed to write some assertion code to Spy on a datastore. When the SUT was exercised, I needed to check whether there was any change in the database and if this correspondeded with my expectations.
Of course, I needed some logic to call the datastore, retrieve the entities, assert the entities, Tear Down some datastore-related items. But the test only cares whether the updated happened or not.

As you can see, the assertion itself is baked-in into the called method and we must rename the method to a more declarative name in order for the test reader to know what we’re asserting on.

Now, as you can see in the next example, I extracted the assertion, so the test itself can state what the assertion should be.
Also note that when I extract this part, I can reuse this Higher-Order Function in any test that needs to verify the datastore, which is exactly what I did:

Conclusion

Test Ignorance can be interpreted in many ways, this post explored some basic concepts of how Functional Programming can help us to write more Declarative Tests. By extracting not only hard-coded values, but hard-coded functions, we can make complex behavior by composing smaller functions.

Functional Programming hasn’t been a fully mainstream language (yet), but by introducing Functional Concepts into Imperative Languages such as: lambda functions, pattern matching, inline functions, pipelines, higher-order functions, … we can maybe convince the Imperative programmer to at least try the Functional way of thinking.