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Testing Rails apps with RSpec: Part II

In this two-part series, I cover testing a Rails application using RSpec and some other popular gems. If you missed Part 1, you can catch up here.

In Part 2, with the setup out of the way, we’ll dive into writing tests for the various components of a Rails app.

Before diving in, I will say that some of this may be a bit opinionated. When I’m testing a Rails app, my goal is to get the most bang for my buck—that is, the most test coverage for the fewest lines of test code. If you find that a test requiring extensive mocking or brittle networking logic begins taking up more than its share of your development time, in my book it’s fine to just git rm it and move on. Likewise, while getting to 100% coverage is a noble goal, it isn’t always as realistic (in the face of schedule and budget constraints) or as helpful (think: future refactoring) as it might seem. The real objective isn’t to hit some arbitrary percentage—it’s to make your app more reliable for your users and prevent regressions as you develop it.


In this article, we’ll write tests that touch on each part of the Rails MVC architecture:

In addition to those, while not covered here, you might also decide to write unit tests for other parts of your app:

Model specs

If you’re familiar with unit testing, testing Rails models isn’t much different. Model tests—unlike controller and feature tests—don’t require much environment setup or mocking. This is one of the reasons it’s a good idea to move logic out of the controller and into the model (or maybe a service object)—it’s much easier to test that way.

In a model spec, I’ll generally have expectations for validations and any non-trivial class or instance methods. The goal of each example in a model spec is to verify the behavior of the method or validation. When thinking about what to test, we want to both make sure that it works in the normal case, as well as in certain exceptional cases. Consider some example “what-ifs” when a plain old Ruby method is called:

While we won’t (and can’t) think of everything that could go wrong, it’s a good idea to touch on common failure points.

In these examples, imagine we’re building an app to compare cars, trucks, and SUVs. To that end, we’ll have a Vehicle model with attributes like the vehicle’s make, model, year and style.

Defining Factories

To make it easier to generate instances of our model in specs, we’ll create factories using the factory_girl gem. Here’s an example factory definition for our Vehicle model that we’ll use in future examples:

In the factory definition above, we describe how to create a vehicle by assigning some default values for each attribute. To use the new factory, we’ll just need to call build(:vehicle) (to make an instance) or create(:vehicle) (to make an instance and persist it to the data store) inside the specs. More on that in a moment.

We also define two traits, one for trucks and one for SUVs. Traits allow you to easily apply a group of attributes to your factory. How you use them is largely up to you. In short, traits let us write this:

…instead of this:

One approach you might also consider is generating random data in your factories. This had the advantage of making sure your app works with a broader range of inputs. The ffaker gem is great for this purpose. In essence, ffaker generates fake data for a number of common fields like names, phone numbers, addresses, and as it happens: vehicles. Here’s how we might re-define the vehicle factory with ffaker:

Notice that we need to use blocks for these calls so that they’re reevaluated each time. To be completely correct here, you could add additional logic to ensure that the make, model, year and style all agree. With this definition, we might generate a 1950 Tesla F-150 car, but for some tests that’s okay. Here’s how you could go improving this:

Testing Validations

Imagine we’d like to require that all vehicles have a year. When users enter new vehicles without a year, validation should fail and they should be required to enter it in order to continue. To make sure that happens, we’ll need to ensure that our validation in the Vehicle model is working correctly.

The model definition:

The model spec:

In case you’re not familiar with RSpec’s DSL, we’ll take a quick detour and cover the three important pieces here:

Going back to the example spec, our first example (“it has a valid factory”) is the control. We want to make sure that a model without a year is invalid, but first we need to make sure that there are any valid inputs at all. Here we verify that the model generated by our factory is valid.

In the second example, we build another model, but this time override the year and set it to nil. With the year is missing, we expect the model to be invalid.

Testing Methods

Imagine our app will display the average fuel-efficiency (MPG) for each vehicle based on user-submitted values. There’s now an MpgSubmission model and a has_many relationship defined in the Vehicle model. To quickly get the average MPG for a vehicle, we’ll add an average_mpg method to the Vehicle model that will average the MPG submissions.

To make it interesting, we’ll add an additional constraint: if there are fewer than 10 submissions, the method should return nil to indicate insufficient data.

I like to create a describe block for each method that will be tested, using the syntax #instance_method and .class_method to indicate what is being described. This helps with quickly associating example groups with the source code being tested.

In the above spec, we test both sides of the if-expression—an example for the case that there are fewer MPG submissions than required and an example for the case that the requirement is met. In the latter case, we also verify that the average agrees with our test data.

While there may be other scenarios we could test here, these examples cover this method fairly well—all branches are tested and there are no obvious nil issues (ActiveRecord::Calculations.average will handle nil mpg values and we’d likely have a database constraint to eliminate that possibility anyway).

Controller specs

If most of the logic is kept out of your controllers, writing controller specs is easy. Controller specs test your Rails application at the request level. Here are some of the questions you should ask when testing controller actions:

There’s a lot going on in the above specs. We use the context

Feature (a.k.a, Acceptance) specs

Feature specs are a type of integration test. Whereas unit tests are concerned with individual components, integration tests focus on how they work together. An app with unit tests but no integration tests is like a pile of individually-tested car parts with no assurance that they can work together to make a drivable car.

In that sense, feature specs are the place to ensure that all the pieces of your Rails app work together and achieve the functionality you built it for. In feature specs, we assume the role of the user and play out various scenarios. For example, some common scenarios in many applications:

To write feature specs for Rails, we’ll use Capybara, a Ruby gem which lets us automate the browser to interact with web pages like a user would—that is, by clicking, typing and selecting.

The following is a basic feature spec for logging in to the site. We’ll visit the sign in url, fill in credentials, and submit the form. To verify that we were successful, we’ll then look for a success message afterwards.

With feature specs, it’s important to maintain a certain level of abstraction. If you’ve written good model and controller specs, you don’t need to be concerned with the low-level details here. While we might take a cursory glance at the database to ensure that the expected change was actually made, we’re primarily concerned with the user seeing what they should be seeing. In this case: seeing is believing.

Interacting with JavaScript

It’s likely that at one point or another, your feature specs will need to interact with JavaScript-dependent functionality—maybe there’s a confirmation dialog that needs to be accepted or content that is loaded dynamically.

Although Capybara’s default Rack::Test driver does not support JavaScript, it’s easy enough to switch to one that does for feature specs requiring it. You can read more about the available drivers here. If you plan to run specs on a server without X11 (or an alternative) installed, PhantomJS via Poltergeist is a good choice.

The below example, while contrived, demonstrates how expectations can be made about dynamic content, as well as how JavaScript code can be executed from the feature spec.

As an aside, trying to test JS-heavy apps from Capybara can unfortunately be a world of pain. As the setup typically involves multiple threads or processes—your Rails app being one and the browser being the other—it leads to all sorts of timing issues. Race conditions, driver differences, and random AJAX failures all conspire to make these tests brittle and hard to debug. I’ve started to avoid these types of tests altogether and rely instead on client-side testing with Mocha and Karma for JS-heavy pages.

Wrapping Up

One thing you may have noticed is that controller specs have a good deal of overlap with model and feature specs. While there are advantages to having controller specs, I’ve found that most things they test can be covered by feature specs. If you only have time for one or the other, go with feature specs. They prove that your app works in an actual browser—rather than just from a cURL client, which is as far as controller specs will get you.

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