Welcome to the Deep Dive. Today we're plunging into a
really fascinating corner of software engineering code
smells. Right, not quite bugs, but
signals. Exactly.
They're not errors that crash your program, but more like
subtle indicators. You know, like a faint off
Keynote suggesting there might be deeper issues in the codes
design. And these can make software much
harder to maintain, understand, or well build upon later.
Precisely, We're drawing our insights today from some fun
fundamental principles highlighted in text, like
software engineering a modern approach.
Good source, very thorough. It is so.
Our mission here is to unpack some specific, insightful code
smells, the kind that really affect how easily software can
be understood and modified. Things that can give you an edge
in spotting robust code. OK, let's get into.
It Let's dive right in, starting with something pretty
foundational about how objects, well, behave.
We're kicking things off with a concept that often comes up in
discussions about robust design. Mutable objects flagged as a
code smell. Now, for those of us working
with complex systems, the difference between mutable and
immutable is basic, but it's worth revisiting why it matters
so much. So a mutable object lets you
change its internal state after it's created, like a piece of
clay you keep reshaping. Continually changing.
And a mutable object, though is fixed once it's made predictable
steadfast. Set in stone, essentially.
But wait a minute, isn't flexibility usually a good thing
in code? Why would being changeable be a
red flag? Why call it a smell?
That's exactly the core tension, isn't it?
The real insight isn't just that immutable objects are safe,
though they are. It's that they fundamentally
change how you debug. OK, how so?
Think of it like this. If you hand someone a printed
photograph, you know it won't spontaneously change color or
shape while they're holding it. Right, it's fixed.
That's immutability. You create an object, its state
is set and it will not change. Period.
So how do we actually achieve this set in stone quality in a
language like Java? Typically you declare all the
objects attributes its internal data as private and final.
Private, so nothing outside can mess with it directly.
Exactly. And final means once assigned,
maybe in the constructor, those attributes can't be reassigned,
their value is locked in. You also usually make the class
itself final. This stops other classes from
inheriting from it and potentially adding ways to
change the state, breaking the immutability promise.
Or you have to be very careful if you do allow subclassing.
So if you need a modified version.
Good point. You don't change the original,
ever. Instead, you create a brand new
instance of the object, but with the desired changes incorporated
into that new one. I see the original stays
untouched, yeah. Preserved precisely.
The original is still there exactly as it was.
A really classic example everyone knows is the String
object in Java. Let's say you have a String like
deep dive OK, and you call a method like to uppercase on it.
That method doesn't actually change your original duck dive
string. It doesn't.
No, instead it gives you back a completely new String object,
which holds deep dive. Your original deep dive still
there, unchanged. That's immutability right there
in action. OK, that makes it very clear.
Now, the advantages that come from this guaranteed state,
They're pretty profound. First, they're safety and
sharing, right? Imagine passing a mutable object
around a big application, maybe to different functions or
modules. Any one of those functions could
potentially change that object's state.
And you might not know who did it or when.
Exactly. Debugging that kind of
unexpected change can be an absolute nightmare.
Really elusive, time consuming bugs often stem from this.
You can imagine been there. But with immutable objects, you
pass them around with total confidence, you are guaranteed
their content won't magically change.
That's a huge leap in predictability for your system.
2nd, and this is critical today, they are inherently thread safe.
OK, this is important for concurrency.
Hugely important in systems where multiple threads are
running parts of your program at the same time, you run into
problems like race conditions where.
The timing messes things U. Recisely because multiple
threads are trying to change the same object state simultaneously
and the final state depends on who gets there when it's
unpredictable. But if an object state can't
change if it's immutable. Then there's nothing for the
threads to race to change. Exactly.
Those kinds of concurrency problems just disappear by
design. This makes reasoning about
concurrent CON vastly simpler. You can share immutable objects
freely between threads without needing complex locks.
Wow, That distinction really highlights the benefit.
It's about stability and just eliminating a whole class of
really frustrating bugs by design.
And if you think about how often we chase bugs that, yeah, we're
probably some object mutating unexpectedly.
So is the goal then to make everything immutable?
Is mutability just bad? That's a really good follow up
because context is absolutely key here.
It's not quite black and white. In certain programming
paradigms, like purely functional languages, objects
are actually immutable by definition.
It's the default way things work.
So in those languages the smell wouldn't really even be a
concept. Ah, interesting.
But in what we call imperative languages like Java, Python, C#,
mutable objects are very common and honestly, sometimes they're
necessary or at least the most practical approach.
So it's not about total elimination.
No, not usually. The goal is more about
minimizing their number. Be deliberate, especially for
objects that represent simple values, rather than, say,
complex entities that genuinely have a life cycle where state
changes are fundamental. So as a practical guideline,
focus on making simple small objects immutable.
Think about value types objects, where once you define it, the
value itself doesn't really change conceptually.
Like what? Well, source material gives
great examples. ZIP code, currency, address,
date, time, phone, color, e-mail, right?
If my adage just changes, I have a new address, the old one
didn't morph into the new one. Exactly.
The previous address still existed, just as it was.
It's an immutable concept and languages are evolving to help
with this. Java, for instance, introduced
records back in version 16. Records.
Heard about those? They make creating these
immutable types much, much simpler.
You declare a record, say for a date with fields like day,
month, year. By default, those fields are
final. So immutable right
out-of-the-box. Pretty much it enforces it with
very little extra code needed from you.
Records also automatically give you essential methods like
toast, string, equals and hash code which are really important
for value objects. Saves a lot of boilerplate code.
It does, and they even support validation right in the
constructor. So for that date record, you
could easily add checks to make sure the day is between 1 and
31, the month between 1:00 and 12:00 right when the object is
created. So you encapsulate the
validation logic too. Exactly.
It's a really neat way to build robust, immutable value types
that add a lot of predictability to your code.
That shift towards mutability offers so much clarity.
It really does. It makes me think about other
areas where our coding habits might hide deeper issues.
And Speaking of maybe counter intuitive insights, our next
code smell often catches people by surprise, even experienced
developers. This next one is data classes.
Now on the surface, these seem fine, maybe even convenient,
right? Yeah, they.
Look simple. We're talking about classes that
mostly just hold data. They've got attributes, maybe
some basic getters and setters to access or change that data,
but not much else. No real behavior.
Just containers for data. Yeah, and for new programmers,
that's often the first kind of class they learn to write.
So if they're just holding data, what's the smell?
And when is a data class not a spell?
Right, you've hit the key point. Not every class that primarily
holds data is automatically a problem.
For example, a simple DTO data transfer object.
Used for moving data around. Exactly.
If it's only job is to shuttle data between layers or across
network boundaries, maybe it's fine being just data.
The smell really comes into play when a data class is part of
your core domain model, but it's, well, anemic.
Yeah, an anemic domain model. It's a class that has the data,
but none of the associated behavior.
All the logic that operates on that data is scattered somewhere
else in the system, maybe in service classes or utility
classes. I see the intelligence is
outside the object itself. Yes.
So the core recommendation when you spot this kind of data class
smell is to analyze the code and try to move behavior into the
class. Bring the logic closer to the
data it uses. Precisely.
Think about it, if you have a customer data class with first
name, last name, e-mail. But all the logic for checking
if the e-mail is valid, or generating a full name, or
sending a welcome e-mail is sitting in some separate
customer service class. That's often a sign of the
smell. Why is that considered bad
design? Well, it comes down to
fundamental principles like encapsulation and cohesion.
Encapsulation is about bundling data with the methods that
operate on it. Keeping related things together.
Right, and cohesion is about how well the responsibilities of a
single class or module fit together when the logic is
separate from the data. Both encapsulation and cohesion
suffer. By moving operations like is
valid e-mail or generate a full name directly into the customer
class itself, you make that customer object more
self-sufficient, more capable. It knows how to handle its own
data. Exactly.
It becomes the authority on its own state and behavior.
This also aligns with a principle called Tell, don't
ask. Tell.
Don't ask, Yeah. Instead of asking the customer
object for its raw data like e-mail, and then telling some
other service object what to do with that data like validate it,
you just tell the customer object itself validate your
e-mail. OK, delegate the responsibility
to the object that owns the data.
Right. This generally makes the code
easier to understand, much easier to test in isolation, and
easier to maintain because the functionality is logically
grouped with the data it manipulates.
That makes a lot of sense. It's like a ensuring the chef
has all their tools and ingredients right there instead
of running around the building for everything.
Centralizes the logic. Good analogy.
OK, now for our final code smell today.
This one might genuinely surprise you, especially if you
learn programming being told to do the exact opposite all the
time. We're talking about comments.
Yes, comments in your code. Ha, yes, this one always raises
eyebrows. Right, the very thing new
programmers are often hammered to write comment everything can
sometimes be a code smell. So the big question is why?
Why would comments which seem like a good practice for
documentation actually be a smell?
It definitely feels counter intuitive at first, but it's
incredibly insightful once you grasp the underlying idea.
There's a classic piece of advice from Brian Kernighan and
PJ Plogger in their famous book Elements of Programming Style.
They put it very bluntly. Don't comment.
Bad code, Rewrite it. OK, straight to the point.
The core philosophy is actually quite simple, but powerful
comments should not be used as a Band-Aid to explain messy,
unclear, or overly complex code. So if the code is hard to
understand, the first instinct shouldn't be add a comment.
Exactly. If the code itself is confusing,
the real solution isn't to write an essay explaining it, it's to
refactor the code, improve it, make the code itself clearer.
Refactor first. Yes, restructure it, rename
variables and methods, better breakdown complex parts.
Once the code is genuinely clean and clear, you often find that
the comments you thought you needed, well, they become
redundant because the code now explains itself effectively.
Let's take a common example. The long method smell often goes
hand in hand with this. Imagine a method may be called
process order that does like 5 different things inside it.
Yeah, I've seen plenty of those. And often you'll see comments
inside it. Step one, validate input, then
some code, Step 2 check inventory, more code.
Step 3 calculate price. And so on.
Trying to make sense of the monolith.
Right, using comments as section headers.
Precisely the presence of those internal divider comments is a
strong indicator, a smell that the method is probably too long
and doing too much. So the refactoring here is often
something called extract method. You take that block of code
under step one, validate input, and you pull it out into its own
new method, maybe call it validate input.
OK, makes sense. You do the same for Step 2.
Maybe create a check inventory method and so on for each
logical block identified by those comments.
Breaking it down. Exactly what happens is your
original long process order method becomes much shorter.
It now just contains a sequence of calls to these new, clearly
named smaller methods. Validate input, check inventory,
calculate price. So the structure becomes clear
just from the method calls. Precisely.
And after you've done this, refactor and what happens to
those original comments like step one, validate input.
They're not needed anymore. The method name validate input
tells you exactly what that call does.
Exactly. The comments become redundant
because the code itself, through good naming and structure, is
now self documenting. It really is a fascinating shift
in mindset, isn't it? Instead of just explaining the
complexity that's there, the goal is to actively eliminate
the complexity in the first place.
That's the heart of it. This really highlights how
important refactoring is. It's not just clean up, it's a
core part of writing good software, turning code that
needs explanation into code that just tells its own story.
Clearly. Well said.
That's the ideal. And that brings us to the end of
our deep dive into these specific and I think really
intriguing code smells. Thank you for joining us on this
exploration of the practical side of software engineering.
Yeah, good discussion. Hopefully this gives you some
useful perspectives on how we can make our software more
robust, more understandable, and ultimately just more pleasant to
work with. Thanks for tuning in.