It really is incredible when you stop and think about it, isn't
it? How much of our day-to-day life,
I mean everything from waking up to ordering lunch, managing
money. It just depends on software
working perfectly. We use so many apps, so many
services all the time and mostly we just take it for granted.
But how often do we actually consider the quality underneath?
You know, the hidden stuff that make sure it all works every
single time. So today, that's exactly what
we're doing. We're going to dive deep into
software testing and specifically we're zeroing in on
unit tests. These are like the tiny building
blocks checking individual code pieces.
We'll unpack what makes a good unit test, the best practices
and also look at the common pitfalls, the the test smells
that can make them less helpful. Basically helping you understand
what makes a test a real asset. Yeah, absolutely.
And it's not just, you know, academic theory, good unit
tests, well written ones, they give you really practical
benefits. They give them like a safety
net. They catch regressions.
That's when you make a change, maybe fix a bug, add something
new, and whoops, you've accidentally broken something
else that used to work fine. Yes, the dreaded regression.
Exactly. Good unit tests catch those,
ideally right away, and they also work as like living
documentation for your code. If you want to figure out what a
piece of code should do, often the unit test is the clearest
explanation you'll find. And that's like big companies,
you know, Google, Meta. They don't just suggest unit
testing, they actually require it for code going into
production. They even have systems that flag
code that doesn't have tests. It really shows how vital this
is in practice. OK, so a safety net and living
documentation that makes a lot of sense.
So to help developers write these effective, maintainable
tests, there's this acronym right FIRT.
That's the one. First, it's a great way to
remember the key characteristics of a really solid unit test.
Let's break it down then. What's the FF?
Is for fast. Unit tests need to run
incredibly quickly. We're talking milliseconds.
Think about a developer coding, making changes.
They want instant feedback, run tests, see the result.
If that takes seconds, let alone minutes, it just breaks their
concentration, their flow. Right, that friction adds up.
Massively across a whole team running tests dozens of times a
day. Slow tests can waste weeks,
maybe months of productivity over a year.
Now if you do have tests that are naturally slower, maybe they
touch a database or talk to an external service, the usual fix
is to split them. You have your super fast suite
you run constantly and then a separate slower suite that maybe
runs, you know, once a day or as part of the build process.
OK, speed is key for that immediate feedback.
Got it. What about the I independent?
I stands for independent. This is super important.
It means the order your tests run in shouldn't matter at all.
Test A before test B or B before A.
Same result every time. Why?
Because one test should never mess with the environment or
like global state that another test depends on.
So no shared data or settings between tests.
Exactly. Each test needs to set up its
own little isolated world, do its checks, and then clean up
completely. This isn't just about
predictability, though that's huge.
It also means you can run tests in parallel, you know, across
multiple CPU cores, which can drastically speed things up.
Makes sense. Parallel execution needs
independence. OK, next U is R reeatable.
This sounds like consistency. Recisely R for reeatable.
A test has to give you the same result ass Oregon fail every
single time you run it. Assuming the code hasn't changed
of course. If it asses once, it should ass
1000 times. If it fails, it should always
fail. When tests don't do this, when
they sometimes pass and sometimes fail for seemingly no
reason, we call them flaky tests or sometimes erratic tests.
Flaky tests? I've heard horror stories.
What causes them? They are developers nightmare.
Seriously. A common cause is often
concurrency issues. Like maybe you have an
asynchronous operation, something running in the
background and the test doesn't wait properly or waits for a
fixed time. That isn't always enough.
So maybe it calls a function that runs on another thread.
The test waits, say, one second, but sometimes maybe the system's
busy. That function takes 1.1 seconds.
Boom, the test fails. But not because the code is
wrong, just because the timing was off that one time.
So it's actually a bug in the test, not the application code
itself. That sounds incredibly
frustrating. What's the impact?
Oh, it's hugely frustrating. And it's, well, surprisingly
common. Google did a study on their own
code. Massive code base, right?
They found something like 16% of their tests showed non
deterministic behavior. Wow, 16%, yeah.
Think about that. Nearly one in six test failures
could be a false alarm. Developers waste hours chasing
ghosts, investigating failures that aren't real bugs.
It slows everything down, and worse, it erodes trust.
If your tests cry wolf all the time, developers start ignoring
them. Then when a real failure
happens, it might get missed. It's a productivity killer, yes,
but it also just destroys confidence in your safety net.
That Google number really hits home.
OK, so we need fast, independent, repeatable tests.
What's S self checking? Yes, S is for self checking.
This basically means that test results should be obvious
immediately. A developer shouldn't need to
like manually read through log files or compare output data to
figure out if the test passed or failed.
The test runner, you know the tool integrated into their
coding environment should just tell them, usually with colors.
Green for pass, red for fail. Simple visual feedback.
And if it fails? Crucially, if it fails, it needs
to point you exactly to the line, the specific assertion, or
check within that test that failed.
That makes debugging way faster. No guesswork.
That instant feedback loop again seems vital.
OK last 1 T for timely. This sounds like it relates to
when you write the tests. It does indeed.
T for timely means you should write your test early, really
early ideally. And this is a cornerstone of
practices like Test Driven Development or TDD.
Yeah, you write the test before you write the production code.
It's testing. Write the test first, yeah?
It sounds a bit backward maybe, but it forces you to think
really clearly about what the code is supposed to do.
What is requirements are before you start implementing it.
It often leads to to cleaner, better designed and naturally
more testable code right from the start.
OK, F IRST, Fast, independent, repeatable, self checking,
timely. That's a really solid framework,
but like you said, it's not always perfect.
Sometimes things go a bit wrong, even if not technically broken.
These are the test smells. Exactly.
Test smells. It's an analogy to code smells
in your main application code. They aren't necessarily bugs,
not strict anti patterns always, but they're warning signs, hints
that maybe your test could be better, simpler, clearer, more
efficient. So not rules, but prompts to
maybe refactor the test. Precisely prompts to just take a
look and ask. Is there a better way here?
One really common smell is the obscure test.
This is a test that's just hard to understand.
Maybe it's really long or the logic is super complex and
tangled. Remember we said test or
documentation. Right living documentation.
Well, if the test itself is confusing, it fails as
documentation. And it fails as a good test
because if it breaks, figuring out why is a major headache.
It makes maintenance harder, onboarding new people slower,
just general friction. The ideal usually is for a test
to check one specific thing. One requirement makes its
purpose crystal clear. Keep it focused.
OK, that makes sense. What's another smell to look out
for? Another big one is tests with
conditional logic. So tests that have if statements
or switch cases or loops inside the test method itself.
Why is that bad? Conditionals are normal in code.
They are in production code, yes, but in a unit test you
generally want the code to be linear.
Just a straight path, set something up, execute the code
under test, check the result. Simple.
When you add ifs or loops, you add branching.
It makes it much harder to see at a glance exactly what
scenario that test is covering, which path was taken or all
conditions even being tested. It obscures the tests intent and
makes it harder to read and trust.
OK, so aim for straight line code and tests for maximum
clarity. Got it.
Any others? The other classic one is code
duplication. Just like you avoid copying and
pasting code in your main application, you should avoid it
in your tests too. If you see the same chunk of
setup code or the same sequence of actions repeated across
multiple test methods, that's a smell.
Why? Seems like it might be faster
sometimes just to copy paste. It might seem faster initially,
but it makes your test sweep bloated and worse if that
duplicated logic needs to change later.
Maybe an API detail changes or a set of step needs updating.
You have to find and fix it in every single place you copied
it. It's easy to miss one, leading
to inconsistent tests and potential errors.
Right maintenance nightmare. Exactly.
Often you can fix duplication by creating shared helper methods
or using setup and tear down functions provided by your
testing framework. Keep it DRY, don't repeat
yourself. Applies to test too.
Obscure test, conditional logic, code duplication, great
warnings, and I think it's worth repeating what you said.
These are smells, not absolute commandments.
They're flags. Totally.
Yeah, they're signals to pause and think.
Could this test be simpler? Shorter.
More direct? Less repetitive?
Refactoring your tests is just as important as refactoring your
production code. Keep them clean, keep them
understandable. It's an ongoing effort.
OK, that leads nicely into something that, well, people
definitely discuss a lot in testing circles.
How many assert statements should you have in a single test
method? Is there a magic number?
Yeah, that's a perennial debate. There are definitely strong
opinions. A very common guideline, almost
a rule for some folks, is at most one assert per test.
One assert. Why so strict?
The argument is all about clarity and pinpointing
failures. If you stick to 1 assert, your
test is incredibly focused. For example, testing a stack
data structure. You wouldn't have one test
checking both that it's empty initially and that it has one
item after you push something. Instead, you have two tests.
Test one test eyes empty initially 1 Assert checking size
is 0. Test 2 Test push ads item,
assert size is 1. After a push, if test push ads
item fails, you know exactly what went wrong.
The push didn't work as expected.
OK, I see the appeal super clear.
Failure messages makes debugging faster.
Exactly. If you had multiple asserts in
one test and it fails, you might have to dig a bit to figure out
which assert failed. Was the initial state wrong, or
did the action not produce the right final state?
One assert Remove that ambiguity.
Is it always practical? Does being that strict sometimes
create more work or make tests less readable in other ways?
That's the counter argument, and it's a valid 1.
You definitely shouldn't be dogmatic about the one assert
rule. There are situations where
multiple asserts and one test are perfectly fine, even
preferable. Think about testing a function,
say get book details, that returns a complex object
representing a book. This book object have a title,
an author, a ublication year, maybe a ublisher.
Right, multile related pieces of data.
Exactly. It makes perfect sense to have
one test method forget book details and then have say 4A
suit statements inside it. One checking the title is
correct, one for the author, one for the year, 1 for the
publisher. These are all checking different
aspects of the single logical result of that function called
the book object. Splitting that into four
separate tests would feel really artificial and just add a lot of
repetitive setup code. That makes total sense.
You're testing 1 cohesive unit of data OK?
Are there other examples where multiple asserts are OK?
Yeah, another common one is testing a simple method with
several related input cases where you can check them all
linearly without needing if statements in the test.
Imagine a simple string utility function like strings dot
repeat. You give it a string and a
number and it repeats the string that many times.
OK, like repeat a three gives AAA.
Right, you could have one test method for repeat and inside it
assert that repeat. AI Arrow gives an empty string
repeat A1 gives a repeat, A2 gives a, a repeat, a three gives
AAA. These are all testing the core
logic of repeat with different simple inputs.
It's linear, easy to read, and efficiently covers several basic
cases in one go. Trying to make 4 separate tests
for that might be overkill. So it really boils down to
judgement, doesn't it? The principle of 1 assert is a
great guideline for clarity, especially for pinpointing
failures, but you have to apply it sensibly.
Sometimes checking multiple related properties of a single
result or checking multiple simple inputs for one function
within one test just makes more practical sense.
That's exactly it. Principles are guides, not rigid
laws. The ultimate goal is tests that
are clear, effective, maintainable, and actually help
your team build better software faster.
It's about finding that right balance.
Well, this has been a fantastic deep dive.
I think understanding those first principles really gives
you a solid foundation for writing good, robust tests, and
knowing about the test smells gives you the awareness to keep
improving them overtime, making sure they stay valuable.
Absolutely, thanks for digging into this with me.
These ideas really are fundamental if you're serious
about software quality today. Thanks everyone for joining us
on this deep dive.