Program to an interface, not an implementation

Something happened today that made me go back to a long long time ago, in a continent far far away, when my mentor at the time said to me “program to an interface, not to an implementation”, and I found myself googling (actually, yahooing, it was that long long ago) what that could possibly mean.

It took me years to have a basic grasp of what he meant. And, as I mentioned, today I read some code that made recall that moment.

Let’s say that we are working on an app that needs to save some data locally. Let’s keep things simple and let’s say we need to save a username locally, to pre-populate a login form.

The simplest thing that could possibly work would be something like this:

But now, once we are saving and retrieving the user name, it would not be completely unexpected if we were asked to display that username someplace else. Let’s say, for example, why not, that in a splash screen

I guess by now you already see the problem.

We are duplicating code. And duplicating code is not good.

But, even if we were not duplicating code, there is another issue here, way more subtle in my opinion.

And the issue is that the code is Snippet 1 is highly procedural, and it is a clear example of programming to an implementation, and not to an interface.

Programming to an implementation

The code in sample 1 is highly imperative and extremely specific. It lacks any abstraction.

We are aware, and we express that clearly in the code, that the username is going to be stored in the user defaults. Not only that, we make the LoginScreen class aware of that fact, and what is worse, we make it also aware of how saving stuff to user defaults work, in painful detail.

This code was easy to write, but the fact that we are exposing the details of how the username is saved is going to be a guaranteed source of pain.

What would happen if we needed to save, for example, the user’s phone number as well? Or his score (if this was a game)? Or an authentication token? And what if we also need to display any of that data in more that one place? We would scatter our source code with extremely specific references to the way we read/write that data.

Programming to an interface

The way I see it, programming to an interface is avoiding implementation details to be known by anyone that should not be aware of said details. And the way to achieve that, is by making my code as declarative as possible.

So, what do we have here? We have data that can be used to identify a User, and we need behaviour to make that data persist between app sessions.

Model objects

So, the first thing to do is identify the Model. In this example, the model is not a username or a telephone number, or a token, it is the abstraction that groups all of those together: a User.

So, let’s declare a User:

Abstract behaviours

Now, what do we need to do with that User? Persist it. Do we need to know how to persist it? No, we don’t. Do we need to know if we save it to UserDefaults, to a remote server, or to the Keychain? No, we don’t.

And this is the whole point of this post: more often than not, we don’t need to know how something is done, we just need to know that something is going to get done. In this case, we don’t need to know how the user’s data is going to be saved, all we need is to know that someone is going to take care of having that data available for us when we need it.

That way, we shift from writing step by step descriptions of what to do, to write high-level expectations of how things should collaborate with each other. We shift from imperative, to declarative code.

So, what’s the behaviour here? If we describe what we need in the most possible abstract way, we need User data to be available.

Abstract stuff, in Swift, like in any other OO language, is the domain of interfaces. In Swift, though, we call the Protocols.

And protocols need an implementation:

Now, the original code would look like this:

Which is better, if you only look at the save() and read() methods.

In fact, you might be wondering if we are actually in a better position now. We have written a protocol, to abstract things, but after all, in LoginScreen we create a concrete class to implement it. And you would be right, if it wasn’t for the coming step.

If we pass LoginScreen the actual implementation of the DataStore protocol that we want it to use, LoginScreen wouldn’t actually know who or how is saving and fetching data. That is called “inverting the dependency”. And I have written about it a couple times before.

Now we are getting somewhere. We have one abstraction (DataStore) and we have a class (LoginScreen) that is provided with a concrete, and unknown to it, implementation of that abstraction. We have shifted from something extremely specific and imperative (LoginScreen saving and reading to user defaults) to something abstract and declarative (LoginScreen asks DataStore to save or fetch data, not caring about how it does it).

And, to me, that is what “program to an interface, not an implementation” means.

Replacing Type Code with Polymorphism

Note: In this post I use the terms class, struct and entity to refer to the same thing. Please bear with me!

Replacing type code with polymorphism is one of those refactoring that are, literally, in the book. However, opportunities to refactor to polymorphism can often times be difficult to identify.

Let’s say that we are working on a SaaS system. The system has different account tiers, with different denomination, different prices, and different capabilities.

To be a little bit more specific, let’s assume just to simplify this post, that our service tracks workouts. There are three account tiers: Free, Plus and Premium, according to the following:

Free account:
– Price: free (duh!)
– Can track: runs, hikes and walks
– Number of workouts: 20 per month

Plus account:
– Price: 2 USD a month
– Can track: Everything supported by the Free Tier plus biking, swimming
– Number of workouts: 50 per month

Premium account:
– Price: 10 USD a month
– Can track: Everything supported by the Plus Tier plus yoga, pilates and roller derby.
– Number of workouts: unlimited.

So, a first attempt at implementing this model might look like this:

Now let’s imagine that we want to present the number of workouts that can be still tracked in the current month in one of our views. Given the previous model, we could do something like this:

I guess you immediately recognise an issue here: the logic in the workoutsAvailable function should be part of the Account object.

There are multiple reasons for that. First, well, that logic is part of the Account abstraction. Second, if we need to present the number of available workouts in two different places in the UI, we would have to duplicate that logic.

So, let’s do the right thing and move that code to the Account entity

It is obvious that the way clients of the Account entity can present the number of available workouts is simpler now. That is important for many different reasons, but to men the most important is readability. Reading and understanding the code in the Current.workousAvailable function takes a second, because there is no complicated logic to understand in there. And as a bonus, when there is no complicated logic to read and understand, it is more difficult to make mistakes.

But now, we also need to present the monthly price in the UI. given our previous experience with the number of available workouts, we might go directly to add that logic to the Account struct

(As a side note, the price should not be typed as a string, but as another struct called Currency, with two properties: the value and the unit it is measured in. But that’s beyond the scope of this post)

Now, notice how the Account struct is basically has the same logic duplicated in two calculated properties. Now, if we add the supported workout types to the mix, we would have three different properties implementing more or less the same logic.

We are turning our Account abstraction into something that can do three different things, depending on a type parameter.

We have code that affects the behaviour of a class, according to an internal enumeration. That is a good indicator that, maybe, the Account struct should be broken down into different classes, each and every one of them modelling only one of those behaviours.

So here is where the refactoring in the post title comes to help.

What do we really need our accounts to provide? Price, number of workouts and a boolean that tells us if that account can track a particular workout type.

Let’s model that as a protocol:

Now, and this also might be a matter for another post, there is something in that protocol that I find a little bit annoying: the lack of symmetry. So I am going to refactor it to:

Now, I shall provide a different implementation of this protocol for each account tier:

Now, each struct models a simple behaviour. There is more code, true, but the code is dumber, simpler. Each logical unit (struct) model a single behaviour. That makes the code easier to read, easier to understand, and therefore more robust and easier to maintain.

My take on iOS app architecture


iOS apps have a single entry point. That single entry point can create and hold a reference to whatever we need to be global, and then inject it everywhere.

Object Oriented Design, or Architecture if you will, is a daunting topic. I believe there is never a right or wrong way to architect a software project, because the number of tradeoffs to consider is huge, so what works for a project might not be the optimal solution for the next one.

That being said, I do believe there are a basic set of practices that help build highly cohesive and decoupled code: single responsibility principle, modularity, a set of domain model objects, and dependency injection.

To illustrate my point, let’s discuss at a very simple app, that reads and writes data from and to a backend. I guess that, conceptually, covers almost 100% of the apps that are developed these days. Let’s say it is a todo list.

Domain model objects

The first step would be looking at the domain (the problem we are trying to solve) and come up with a set of abstractions that model the entities that are part of it.

In our example, we want to display todo’s, create todo’s, present a list of todo’s, and maybe share todo’s… I guess by now we all can see a model object emerging: Todo

Data manipulation.

We need to send Todos to a backend. And we need to read Todos from a backend as well. That probably implies dealing with network requests, and also with a good deal of JSON (or protocol buffers).

We don’t want to scatter those responsibilities across the codebase, even if it is just because network operations have to be done in a background thread, to avoid blocking the UI, so it makes sense to deal with all that async calls in just one place.

But, we have a difficult solution here: that one thing dealing with networking will have to be used from multiple places

A common solution to this problem is making the Thing That Handles Networking a singleton. (Note: Since I wrote the first draft of this post, three weeks ago, Soroush Khanlou wrote this excellent post about refactoring out those singletons. Go read it now!)

Singletons, per se, are neither good nor bad. Sometimes, something is a singleton and it makes sense to model it as such.

But, in my experience, it is better to move away from them, for one reason in particular: a singleton is a global variable, and as such it is a dependency that is very hard to get rid of.

Dependency Inversion

But what if we invert that dependency? And we have a huge advantage here: the iOS app architecture.

An iOS app has a single entry point (the AppDelegate, an implementation of the AppDelegate protocol).

That ensures that instances variables of the AppDelegate are going to be unique as well. Well, not completely, but let’s say that we can guarantee that an instance of a regular class is created once and only once.

You might say, and you would be right, that there is nothing preventing a developer from creating as many instances of that class as she wants. I believe the solution to that is, as Uncle Bob said, well, just create one.

Back to Data Manipulation

Another look at the domain will tell us that we want to:
– obtain a list of Todos
– obtain a Todo
– create a new Todo
– edit an existing Todo
– delete a Todo

This nothing more than a list of high level operations. We don’t really care, or need to care, at this stage about how those operations are actually implemented, so we can just declare them as a protocol.

Notice how this protocol abstracts two different responsibilities: reading and writing. This is a hint that, eventually, we might need to split the protocol in two (again, this post provides a great discussion of why it might be a good idea to do so).

However, I am not going to do it right now, in part because it is one of the points I want to make: modularity, good separation of concerns, empowers us to further break things down whenever we things it is the right moment to do so.

Now all we have to do is provide an implementation of that protocol to our UI. And the easiest way to do that is, again, relying on the fact that we have a single entry point (the App Delegate), and applying the Dependency Inversion principle. Or, if you will, inject all the things!

So a typical AppDelegate looks like this:

I am not very fond of storyboards, so I usually have a class whose responsibility is bootstrapping the UI, creating the root view controller, the navigation logic (if necessary) and preparing both for collaboration.

I create that bootstrapping logic in the App Delegate, and I would also create an instance of my service, and inject it into the bootstrapping logic:

And then my data service can be injected into any view controller:

Is this approach future-proof?

Nothing is completely future-proof, but I think this approach is flexible enough, and to me, it remains open enough for modification and extension.

  • There is good separation of concerns. There is one thing that deals with data (fetching and saving), there is one thing that creates UI, and there might or might not be one thing that handles navigation.
  • There is good modularity. Any of those things in the previous bullet point can be refactored and split into smaller pieces, if necessary. There is a clearly defined interface in between them, so if, say, I noticed that the implementation of my TodoService is starting to mic business logic and pure data transport, I can break it down in two.
  • There is good testability. Since I am injecting the TodoService into each view controller that needs to collaborate with it, I can inject a mock in my tests.

I have found that by pursuing a dependency graph in the form of a tree, where the root node is the App Delegate, I have more room for adapting to changing requirements, and I can factor and test my code better.

A journey of thousand miles…

This week I spent some time adding a media gallery to the product I’m working on. You know, one of those things that mimic the native Photos user experience, where you can swipe to left and right to navigate to the next item, zoom in and out a photo… The usual suspects.

So, in order to move fast we decided to rely a third party library for it, instead of writing it from scratch.

We already had a well defined model object to represent a media item (photo or video). But the third party library that we chose requires its own model object, which makes total sense.

Also, it is possible we might end up switching to a different library in the nearly future, so it makes sense to have a clear boundary between our code and the third party library. I call it a boundary, some people call it a wrapper, some other people would call it an abstraction layer, but the point is that we don’t want to scatter references to this third party library across our codebase, we want those references to be isolated, encapsulated in just one class.

To the point

First, let’s assume our model object, the one in our codebase, looks like this:

The model object required by the third party library looks like this:

We could discuss if having a property to mark a MediaItem as an image or a video is an indication that MediaItem is the wrong abstraction, but that’s beyond the scope of this post (hint: I think it is the wrong abstraction, that any kind of type property in any class or structs suggests that there we are trying to model two different things with just on type)

Let’s say that boundary is a class Called MediaBrowser, that, first of all, transforms our model objects to instances of the model objects required by the third party library:

Now, that code might look quite straight forward, but in plain english, it will read like this:

For each instance of MediaItem, create an instance of GalleryItem, having in mind that, when creating an instance of GalleryItem, first, we need to check if MediaItem is a photo or a video. If it is a video, we need to pass to GalleryItem’s initialiser the value of the videoURL property in MediaItem, mark it as Video. However, it MediaItem is of type Image, we need to provide its imageURL to GalleryItem’s initialiser, as well as mark GalleryItem as Image.

It’s not rocket science. But describing what galleryData() code does requires a paragraph. And every single time you read this function, you need to translate it into that paragraph in order to understand what it is doing.

That happens because the code is quite imperative . There are a lot of specific, unnecessary details in there. Unnecessary because we don’t need to be that specific to describe what we expect the galleryData() function to do.

This is what we expect: we want to map each instance of MediaItem to a brand new instance of GalleryItem. Simple as that. However, the code, again, is telling a different, more complex, story, with plenty of details that we don’t need to be made aware of every time we read it.

The declarative solution

One of the things I like most about Swift is that it provides multiple ways to model abstractions, and multiple tools that can be used to write more declarative code.

For example, we can declare initialisers in extensions. So, we could do something like this, to extend the GalleryItem object (remember, this object is part of a third party library, and therefore out of our control):

And now, this would be our galleryData() function:

This function, now, clearly express my expectation: I want to create a new GalleryItem for each MediaItem. Period. I don’t care about how that happens, I don’t care about the details, I just trust GalleryItem to do the right thing and create an instance of itself correctly.

We have turned the previous, imperative code into declarative code.

Now, in plain English the galleryData() function reads:

For each instance of MediaItem, create an instance of GalleryItem.

Rinse and repeat

In the scope of this post, there is not a huge difference in terms of lines of code, or in terms of code complexity between both solutions. This, per se, is not going to turn complex code into simple code, buggy code into robust code.

But now, imagine applying the spirit of this approach, using the tools Swift provides to write declarative code, every single time there is an opportunity to do so. That would make a significant difference. Because a journey of a thousand miles begins with a single step.

On unit testing private behaviours

TL;DR; every time I find myself in a situation where I need to unit test a private behaviour, I consider that a clear indication that I might need to rethink my design.

Let me tell you a story

Some time ago, in what now seems like a galaxy far far away, I was introduced to unit testing by means of someone adding the following requirement to the project my team was developing: “code must be unit tested”.

As you can probably guess by the hint of sarcasm in the previous paragraph, unit testing, like almost everything else in life, is something you learn, not something you are born with. Which was painfully obvious at that time, as I have already written about.

But the thing is, a couple days ago I finally had the opportunity, since I was trapped in a plane for 13 hours, to catch up with some reading. In particular I was finally able to devour Sandy Metz’s new book.

The book is still unfinished, but I highly recommend grabbing a copy, and reading it cover to cover, as soon as possible.

There are plenty of potential quotes in that book, but I would like to focus on one in particular:

… the first step in learning the art of testing is to understand how to write tests that confirm what your code does without any knowledge of how your code does it.

Tests always tell at least two stories.

Unit tests always tell at least two stories. One, specially if the tests are well written, is a cristal clear description of the expectations you have about the behaviour of the code under test.

The second story, though, is usually one that nobody likes to hear. If your tests need to manipulate internal behaviour of the code under test, in order to test your external expectation of that code, then you need to rethink your design. And allow me to illustrate this with an example, that links with the introduction to this very same post you are reading.

When testing goes wrong.

Back to my story: no one in our team had any experience testing, so at the time we did not recognise the code smell, but here is what we were trying to do.

We had this one class that was supposed to be a one to one mapping to a RESTful API, what in the classic three-tier architecture would correspond to the data tier, service layer, transport layer, whatever you call it. The point is, this was the class that performed networking operations against a RESTful API. Let’s call it MediaService.

Part of the expected behaviour of MediaService was that it should cache data, using a key-value cache. This data cache would expire after a given time.

So, the flow would be like this: with every request, we would first check if there was data cached and not expired. If there was data cached, MediaService would return it, if there was no previously cached data or the existing data was marked a expired, MediaService would attack the network, fetch data, and add it to the cache, associating a timestamp to each key-value pair.

So, the next time we wanted to request data to the same endpoint, we would check the cache again, providing a timestamp. The cache would calculate if data was expired or not… rinse and repeat.

In code, that looked similar to the following snippet (those still were the Obj-C times, so this is a rough translation to Swift of my recollection of the events):

Well, not very idiomatic Swift code, of course, but again, those were the dark days before generics.

Now, if you wanted to test that cache was ignored when cached object have expired… how would you do that?


I don’t recall who said it, but someone said that good software engineering is all about arranging the code in a way that complexity does not collapse on you.

That usually implies breaking down your codebase, organising it into separate subsets or structures. Since you are already breaking down your code and organising it in smaller chunks, you might as well make each of those chunks contain code that is related, that deals with one concern. So, instead of one big ball of code, you would end up having smaller balls, each one of them taking care of one and only one smaller part of the big problem your software is trying to solve. (That’s how I visualise cohesion, by the way)

But of course, the problem your software tries to solve is bigger than each and everyone of those small, cohesive structures that you have used to organise your code. So they need to collaborate with each other.

And here is where dependencies are born. One block of code needs to collaborate with another block of code. One part of your solution needs to collaborate with another part of your solution. Or, if you will, one part of your solution depends on another part of the solution.

The tricky part here is how to set up those dependencies in a way that do not defy the whole purpose of breaking down your code into smaller structures. The key is setting up those collaborations in a way that the collaborators are still independent from each other.

Depending on abstractions not concretions

Yep, that’s kind of the definition of the Dependency Inversion Principle.

In the previous code sample, MediaService needs TimeCache. The key here is that it actually depends on the existence of TimeCache, because MediaService creates TimeCache, therefore the behaviour provided by TimeCache is, in a way, embedded within MediaService. Or, the MediaService abstraction depends on the implementation details of TimeCache.

But, why does that matter?

Firstly, MediaService is tightly coupled to TimeCache. So coupled to it, that it actually needs to create it in order to make itself functional.

Secondly, and due to the previous point, it is not possible to modify the behaviour of TimeCache, without modifying MediaService.

Thirdly, there is a more subtle issue here. The fact that there is a cache, and the fact that said cache expires, is not part of the public API of the MediaService class. It is an implementation detail, buried into the MediaService class code, but an implementation detail that is so significant that leaks outside its container. Because we know MediaService handles a cache that marks items as expired, because we expect actually expect that, however, the expectation is not declared anywhere in MediaService’s public interface.

So now, when it comes to unit testing, we face a very interesting problem: we need to test a behaviour that is a private implementation detail, but since we need to test it, it is not private anymore, it is a well-known expectation, but an expectation that we can not test because it is a private implementation detail. You see the infinite loop here, right?

And notice how I have not even mentioned networking…

No worries, mocks, stubs and spies can help!

Well, not anymore. In the old Objective-C days, we could just declare a category in the testing bundle to expose and override any private property of a class with a mock (or stub, or spy, to be honest, I never really understood the difference).

In this case, we could override cache and network, provide mocks, set our tests, and go on our merry way.

The problem with that is that we would still be testing private behaviour. And why should not do that?

The reason, is again, subtle. When we test private behaviour we are basically coupling our tests to the production code very tightly, because we are not testing public APIs anymore, we are testing internal implementation details we should not even be aware of.

Have you ever heard anyone complaining about how unit testing is a waste of time and effort because if you change the production code there is always going to be a bunch of tests that need to be rewritten? I bet the reason is because of vey tight coupling between tests and the code under testing. Or, in other words, because the tests are not testing why they should (public expectations) but private details about how those expectations are fulfilled.

Also, in the age of Swift, doing that is not even possible anymore. If MediaService is final, we can not subclass and override, and if cache and network are declared as constants with let there is no way to override them.

So, what to do?

Invert the dependencies

This is not the first time I blog about dependency injection. Once even using the same example I am using today, another time in a different context. And I am afraid it won’t be the last time I blog about it.

But that’s for a reason. Inverting the dependencies, in my experience, only has upsides. It makes code more decoupled, easier to test, and therefore easier to maintain in the long term.

In this case, the best solution would be something along these lines:

Cache and networking behaviours can be provide now through the MediaService initialiser. Notice the shift in the way we refer to caching and networking now: as behaviours. Because for MediaService, that’s what they are.

The details on how caching and networking work are not known to MediaService anymore. MediaService is provided the behaviours it needs to rely on, the behaviours it needs to collaborate with.

And that’s the beauty of inverting the dependencies. By carefully setting the way collaborators know about each other, and by carefully hiding whatever is not necessary for collaborators to know, and by shifting from concretions to abstractions we make each of our building blocks rely on the expectations that the other building blocks publish about their own behaviours.

The reason why I try to avoid storyboards

Sometimes you know that you like or dislike something, but you are not sure exactly why. Sometimes you find yourself in a unpleasant situation, but it is hard for you to pinpoint why you find it unpleasant.

Well, I just had an epiphany, and I think I finally understand why I don’t feel comfortable working with storyboards, for anything bigger than a two screens app.

Storyboards do not let me slice the problem.

Consciously, or most likely unconsciously, I tend to slice problems, and rearrange those slices in a way that suits me, or suits the context I am working on.

Each of those slices can then be sliced down even further, so those new slices can be rearranged again.

That helps me, first, make the problem solvable. Which, immediately, increases my productivity: when I am facing a problem that happens to be too big, or just seems to be too big, I tend to procrastinate a lot.

But by slicing problems as much as possible, trying to comply with the single responsibility principle, abstractions start popping up, screaming at me.

Because even though I would love to say that I can design a solution to a problem upfront, truth is I have embraced the fact, long time ago, that I am better at noticing when the problem I am trying to solve is suggesting me a solution, than at trying to make the problem fit my pre-conceived solution to it.

Also, I find easier to make small slices work faster. It might be the time spent doing TDD, it might be that it’s just the way my brain works, but the shorter the feedback cycles, the faster I can move forward. It is like I physically feel impediments removing themselves, getting out of my way. Taking a storyboard of a certain complexity to a stage where I can make it work takes so long!

So I guess we are back to a recurrent theme of this blog: the single responsibility principle.

A storyboard can be a time saver, a great solution to build a kick prototype or a product with a well defined, clean and simple navigation flow.

But, in my opinion, more often than not, it is just a big chunk of muddy of responsibilities than I’m better off breaking down.

This is how I parse JSON in Swift

There are great libraries to deal with JSON data in Swift. Just for the record, here are some of them, in no particular order:

Third party libraries are great. I am even guilty of releasing a couple of them myself, but the thing is that I believe pulling in a third party dependency should be carefully considered.

The advantages and disadvantages, the benefits and risks of relying on third party libraries are definitelly worth a separate post. But, for now, let me just say that I think relying on third party code is a commitment that needs to be very carefully considered. It is a dependency on code that is out of your control.

But back to the post at hand. Even though the existing third party JSON parsers and serializers are great, I tend to do all my JSON parsing manually.

Why? First of all, check the previous paragraph. I am not for reinventing the wheel at all, but so far, I haven’t found myself in any situation where relying on a third party library had any sensible advantages over rolling out my own parsing.

Secondly, I have always liked the NSCoding approach.

An object should know how to serialize and materialize itself. Now, when it comes to simple model objects (kind of a POJO), it could be argued that embedding that knowledge into an object is not the best possible idea. After all, the NSCoding approach implies that a model object would need to know the specifics of a format (JSON, XML, plist, or whatever the cool kids do at the moment), and that knowledge should not be part of the model object itself.

But Swift provides awesome tools to model this in a modular way. Like, for example, extensions. So we could still declare our model object as a simple entity, without any behaviour:

And then declare a failable initializer that creates an instance of BlogPost from JSON data in an extension:

The code looks clean enough to me, I am still complying to the single responsibility principle, and I still have good separation of concerns.

If I needed to materialize a BlogPost from a different format, say a plist file, all I would need to do would be add a new extension with a new failable initializer.

And that’s how I parse JSON nowadays.

TIL: UIViewController.init() might initialize a xib

It is all in the documentation, both in the UIViewController Class Reference, and in the comments in the class header. It also makes sense, but today I spent a couple of hours debugging a weird crash, due to this.

So this is something I think I won’t forget in a long time.

When initializing a view controller passing nil as a nib, the view controller will attempt to load a nib whose name is the as same as the view controller’s class. That’s what the UIViewController header clearly states:

If you
invoke this method with a nil nib name, then this class’ -loadView method will attempt to load a NIB whose
name is the same as your view controller’s class. If no such NIB in fact exists then you must either call
-setView: before -view is invoked, or override the -loadView method to set up your views programatically

But there is more. According to the UIViewController class reference:

If you use a nib file to store your view controller’€™s view, it is recommended that you specify that nib file explicitly when initializing your view controller. However, if you do not specify a nib name, and do not override the loadView method in your custom subclass, the view controller searches for a nib file using other means. Specifically, it looks for a nib file with an appropriate name (without the .nib extension) and loads that nib file whenever its view is requested. Specifically, it looks (in order) for a nib file with one of the following names:

If the view controller class name ends with the word ‘Controller’, as in MyViewController, it looks for a nib file whose name matches the class name without the word ‘€œController’, as in MyView.nib.

It looks for a nib file whose name matches the name of the view controller class. For example, if the class name is MyViewController, it looks for a MyViewController.nib file.

And that’s exactly what happened to me earlier today. I was initializing a view controller like this:

While also having in the project a completely unrelated xib, named AddContactView. Unrelated, like in “AddContactViewController does not use AddContactView.xib at all. Nothing. Zero.”

So my app was crashing, throwing an assertion that kindly reminded me that AddContactView did not have any owner properly setup.

Well, there is a reason for that.

Remember, read your docs! 💁

Swift extensions can be applied to Objective-C types

The more I think about this, the more obvious it sounds. And the more I think about it, the less I believe it deserves a post. But it was a pleasant surprise anyway, so here it goes.

Let’s assume we have an Objective-C project, that contains a class like this:

And it also contains another class like this:

Now, let’s say we declare a Swift protocol like this:

And extensions to the Person and Movie object to enforce compliance with the Displayable protocol:

Now, we can consume the Displayable protocol from Swift:

Or from Objective-C (please, excuse the weakness of my ObjC-generics kung fu):

Which, believe me, is a huge relief when dealing with large legacy projects.

In case you want to play with a complete example, I uploaded a sample project to GitHub.