Welcome to the Deep Dive, where we take a stack of information
and extract the most important Nuggets for you, our curious
listener. Today we're embarking on a
fascinating journey, really getting into a crucial corner of
software engineering, the world of mocks and mock frameworks in
testing. Now it might sound a bit
technical at first, but honestly, it's absolutely
foundational if you want to build software that's, you know,
robust and reliable. Our guide for this deep dive is
Software engineering a modern approach by Marco Tulio Valente.
We'll be focusing specifically on how developers well, how they
streamline and simplify a really critical part of the software
testing process, making it much more efficient, much more
effective. Now, if you've been with us for
previous deep dives into software development, you might
recall us touching on the idea of creating simple mock objects
by hand. Think of it like this.
If you're testing, say, a book search object, you don't
necessarily want it hitting a real, potentially slow database
every single time you run a test, right?
So you might create a simple stand in a mock book repository
that maybe only returns data for one specific book, just to make
sure that book search part is working correctly.
Totally in isolation, it, it lets you check your logic
without waiting on those external systems.
But I mean, imagine doing that manually.
What if you need dozens, maybe hundreds of these mocks?
And what if their behavior needs to be complex, needs to change
depending on the specific test, That manual approach, well, it
quickly becomes a huge burden. It really slows things down.
Yeah. And what's fascinating here is
that this exact challenge, I mean, the sheer prevalence of
needing these stand in objects, you can call them mocks, some
call them stubs. In unit testing, it directly led
to the development of these really powerful tools to
automate their creation and management.
These mock frameworks, they don't just streamline the
process honestly, they kind of transform it.
And that's exactly what we're going to unpack today.
How do these frameworks work their magic?
Why are they so incredibly beneficial for developers?
And maybe what do they tell us about the different
philosophical approaches to testing software?
So let's really set the stage for the problem these frameworks
are designed to solve. We touched on creating mocks
manually, and yeah, for a very simple isolated test it might
seem OK, manageable. But in the real world of
software development, applications just aren't simple,
are they? They have loads of
interconnected components, each relying on others interacting
with databases, external services, you know, payment
gateways, all that stuff. Well, absolutely.
I mean picture a developer trying to test a function that
processes an online order. That single function might
depend on a payment gateway, an inventory service, maybe an
e-mail notifier. Now if you had to manually write
classes like mock payment gateway, mock inventory service,
mock e-mail notifier and do that for every unique test case.
Like one for a successful payment, another for a failed
payment, one for low inventory, one for out of stock.
You'd honestly spend more time writing mock code than actual
application code. It becomes unsustainable.
It just sounds like a mountain of boilerplate code, so what's
the real core issue with doing it manually like that?
Well, the primary issue, as our source highlights, is just the
sheer volume of repetitive code. It's huge.
Each manual mock implementation, like that mock book repository
example, need you to write a whole separate class for each
specific test scenario or behavior.
And this code it adds overhead, becomes really difficult to
maintain as your main application changes.
It could even introduce bugs into your test themselves, which
kind of defeats the purpose. Remember, the whole point of a
mock is to allow a unit test to focus purely on the logic of the
code being tested, right? Free from the unpredictability
of remote or slow services, Manual mocks ironically could
end up becoming a source of unpredictability and slowness in
your testing workflow. OK, so what's the game changer
here then? What's the fundamental shift
these frameworks introduce that makes them such a massive step
forward? The key observation really is
that mock frameworks completely eliminate the need to implement
these mocks manually. You just don't write those
classes anymore. Instead of you writing a
dedicated class for each mock, the framework does that heavy
lifting for you. It's a real paradigm shift.
You go from coding the stand in to simply declaring the stand in
and telling it how to behave. OK, Declaration, not
implementation. Exactly.
And this dramatically reduces the amount of test code you have
to write and just as importantly, maintain, which in
turn speeds up your development feedback loop massively.
Imagine a test suite that takes minutes, maybe even hours to run
because it's constantly hitting a real database or some slow
external API. It happens.
With effectively used mocks, that same test suite could
shrink down to seconds. It completely transforms your
development rhythm from like a painful chore into a rapid
continuous cycle. That sounds incredibly powerful.
Our source points to a very popular framework that
exemplifies this Makito. Can you give us a sense of how
Makito actually achieves this simplification for a developer?
How does it work under the hood? Sort of.
OK, let's unpack how Makito works its magic.
So instead of writing that whole mock book repository class with
all its methods and logic, our source shows you can create a
mock for book repository with just like a single line of code.
It's simply a call to the mock that Makito provides, where you
basically just tell it what kind of object you want to mock,
interface or the class. Yeah, and what's truly
fascinating here from a technical standpoint is the
underlying technology that allows Makito to do this.
It leverages Java's reflection features.
Now, for those maybe not familiar, reflection basically
means a program can, while it's running, look at and even
manipulate its own structure and behavior.
Right, it can inspect itself exactly.
So Mockito uses reflection to dynamically create these mock
objects, these substitute versions of your real
dependencies without you needing to write a concrete class for
them yourself. You simply tell Mockito, hey, I
need a mock of Book repository and boom, it conjures one up for
you. OK.
So the mock is created, but it's initially just an empty shell,
right? Like it doesn't know how to
behave automatically. It won't magically return a
specific book or throw an error. You have to explicitly tell it
what to do for your test. Precisely Once that mock object
is created, it's essentially a blank slate.
It has no inherent behavior. You then configure its behavior
to respond in very specific ways, exactly as needed for
whatever your test case is trying to verify.
And for this configuration step, Makito offers what our source
calls a simple Domain Specific Language or DSL.
ADSL. OK, yeah, think of a DSL is like
a mini language designed for a very particular purpose.
In this case, it's a set of method calls and syntax within
Java itself. But it reads almost like plain
English when you're defining how your mock should behave.
It's designed to be intuitive. That sounds much better than
writing a whole class. Can you give us an example how
does that configuration actually look in practice using this DSL?
Sure, you define its responses to specific method calls.
The source gives a couple of really clear examples.
For instance, the first one would like something like when
the mock repo dot search and E&A ends that return book hunts dot
null book and then maybe a second line when mock repo dot
search 1234 that return book hunts softening.
OK, let me see if I get this the first line when mock repo dot
search any int that sets a kind of general default rule.
If the search method on our mock book repository gets called with
any integer argument at all, it should just return some
predefined null book constant, maybe signaling not found.
It's like a catch all. That's exactly right, it's your
fall back behavior for that method.
And then the second line shows how you can get much more
specific when mock repo dot search 1234.
If a search is called specifically with the integer
1234 that it overrides that general rule and instead it
should return the actual software engineering book data.
Ah, OK, that's incredibly flexible then.
It lets you model both the general cases and very specific
edge cases without cluttering up a mock object with complex if
else logic or big lookup tables. This really highlights that time
saving power you mentioned earlier.
Definitely. And I guess it also makes your
tests much more readable because the expected behavior of the
mock it's declared right there inside the test setup itself.
Exactly. Test intent becomes much
clearer. OK, here's where things get
well, Maybe a bit more nuanced, potentially even confusing for
listeners trying to follow all the jargon.
Our source acknowledges that some prominent authors and
software testing Gerard Mazaros has mentioned they make a
distinction between mocks and stubs.
For someone trying going to get informed, this terminology
difference can be a bit tricky. What exactly is that distinction
about? Yeah, this brings up a really
important discussion about the precise terminology within
software testing, and honestly it's a point of ongoing debate
in the community. Mazzaro's and others who follow
his definitions argue that a true mock isn't just a stand in
for state, meaning you know what data it returns.
It's also about verifying behavior.
With a mock in this stricter sense, you're not just
configuring it to return a value, you're also explicitly
verifying that your code interacted with that mock in a
specific way. OK, so checking if methods were
called. Right, you'd verify if
particular methods were called on the mock, maybe even the
order they were called in, or the number of times it's about
the collaboration. Conversely, if the test object
you're using the stand in only really verifies the state, like
in our book search example where you just check the title of book
that was returned by the Mac repository, then Mazzaros would
typically call that a stub. A stub just provides pre canned
answers to calls made during the test.
It doesn't usually care if those calls were made or how often.
So the core difference is whether you're asserting on the
return value or the data that comes back.
That's more like a stub versus asserting on the interaction
self. Like was this method called?
That's more like a mock. Is that the gist?
That's a good way to put it, yeah.
State verification versus interaction verification.
But interestingly, our source explicitly states it won't make
this distinction. It finds it overly nuanced for
its purposes. Why would an author choose to
deliberately simplify that terminology, especially when
others make a point of separating them?
What really comes down to a pragmatic choice and it reflects
a common perspective you see in the industry.
The author likely believes that for many practical day-to-day
purposes, the general term mock is understood broadly enough to
cover both scenarios, and the benefits of going into dub
detail distinguishing these similar concepts maybe don't
outweigh the cost of adding more paragraphs, more complexity to
the explanation. Right, keeping the focus
practical. Exactly.
It avoids bogging down the reader in what some might
consider somewhat academic distinctions when the core
purpose, which is isolating code for testing, remains the same in
both cases. However, it is worth noting,
like you said, that for very complex systems or perhaps teams
that are really deeply invested in particular testing
methodologies like Behavior Driven Development,
understanding this nuance between mocks and stubs can
actually lead to clearer test intent and sometimes even better
architectural decisions down the line, but for a general
introduction may be less critical.
That makes sense. It's that classic trade off
between conceptual purity and practical simplicity.
So connecting back to that IDEA interaction, what then is a
behavioral test or interaction test as the source mentions?
Right, so that terminology refers specifically to text that
do focus on those interactions we just discussed.
Instead of primarily checking a return value or a final state,
you're checking for specific events like method calls that
occurred on your mock object during the execution of your
test. OK, so you're verifying the
conversation between objects. Precisely.
The source gives a great example using Mockito's verify command.
Imagine a test scenario where you want to confirm that after a
new user signs U, an e-mail was attempted to be sent.
Now in your unit test, you don't necessarily care if the e-mail
actually reached the users inbox.
That's a different kind of test. You just want to know that your
sign up service correctly called the send method on whatever
Mailer dependency uses. Got it.
Just checking the collaboration happened.
Exactly. So you'd use something like
verify m.sendanystring where M is your mock Mailer.
This line functions much like an assertion, like checking if a
value is true, but instead of checking a return value, it
checks if a certain event occurred.
In this case, was the send method of the mock Mailer
executed at least once with any kind of spring argument.
It confirms that specific interaction took place.
That's a really powerful tool. Then it gives you a whole
different dimension to what you can actually verify in your
tests. So what does this all mean for
us? Broadly, it sounds like mocks
and stubs, even with their nuances, are just part of a
bigger family of testing tools. Precisely.
That's a great way to think about it.
Mocks and stubs are indeed considered special cases within
a broader category known collectively as test double S
the term test double. It's an umbrella term.
It covers any kind of object that stands in for a real object
during a test, much like a stunt double stands in for an actor in
a movie. OK, test double S makes sense.
What other kinds are there? Well.
Our source clarifies two other significant types of test double
S, each with its own specific use case.
First, you have dummy objects. These are really the simplest
form. They typically get passed as
arguments to a method, maybe in a constructor call, but they
aren't actually used within the methods body during that
specific test you're running. So they're just.
Essentially, yes. Their main purpose is simply to
satisfy the languages type system so you don't get
compilation errors. Imagine a method or a
constructor that requires, say, 5 arguments, but for the
specific behavior you're testing right now, you only actually
care about what happens with two of them.
Dummy objects fill in the other three spots just so the code
compiles and runs. They satisfy the method
signature even though they don't play an active role in the test
logic itself. OK, simple enough.
What's the other type? The other key type mentioned is
fake objects. Now, these are different from
mocks. Unlike a mock, which you
configure to respond in specific ways to specific calls, a fake
object actually has a simpler but real working implementation
of the object it replaces. So it actually does something.
It's not just responding based on pre programmed rules.
Exactly. It's still a stand in.
It's not the production object, but it has its own working
logic, usually simplified for testing purposes.
A really common and very useful example is an in memory
database. Instead of your test connecting
to a slow external persistent database like Postgres or MySQL,
a fake in memory database like H2 in the Java world for
example, can genuinely store and retrieve data during your test.
It performs real database like operations, inserts, queries,
updates, but it does it all entirely in the computer's
memory and all the data disappears as soon as the test
finishes. Right, so you database like
behavior but super fast. Precisely.
This allows for incredibly fast tests that feel closer to
integration tests because real logic is executing, but without
the setup complexity and slowness of using a full real
database system. Wow, OK, so dummies, fakes,
stubs, mocks. This really gives you, our
listener, a much more comprehensive map, doesn't it?
A whole toolkit for different testing situations, knowing
these distinctions. Dummy, fake, stub, mock.
It helps you navigate those technical conversations,
understand documentation better, maybe even articles or blog
posts you read. And ultimately, it empowers you
to choose the right kind of test double for the specific testing
job you need to do, leading to better, more reliable software.
What an insightful deep dive. We've really unpacked how these
mock frameworks, like Makito genuinely streamline the
creation and configuration of mock objects.
It saves developers just immense amounts of time and effort in
unit testing, fostering those rapid feedback cycles we talked
about. Absolutely, and we've explored
those sometimes subtle but important distinctions between
the different types of test double s, from the simple
dummies through fakes, stubs and the interaction focused mocks.
Hopefully provides A clearer, more nuanced picture of how
developers effectively isolate and test specific units of code,
ensuring both correctness and, crucially, inability overtime.
Thank you so much for joining us on this deep dive.
We really hope this exploration has given you a useful shortcut
to being well informed on mocks and test double S.