23 Comments Introduction to test-doubles - 03/16/09

As soon as you start unit-testing or test-driving your development, you’ll learn about test-doubles and how they can make your tests lightening-fast sooner or later. And if you set up a continuous integration process (which you should) and you have more than 5 unit tests, you’ll probably have to know about test-doubles sooner in stead of later :-) .

What is a test-double?

Gerard Meszaros defines a test-double as follows:

“Sometimes it is hard to test the system under test (SUT) because it depends on other components that cannot be used in the test environment. This could be because they aren’t available, they will not return the results needed for the test or because executing them would have undesirable side effects. In other cases, our test strategy requires us to have more control or visibility of the internal behavior of the SUT. When we are writing a test in which we cannot (or choose not to) use a real depended-on component (DOC), we can replace it with a Test Double. The Test Double doesn’t have to behave exactly like the real DOC; it merely has to provide the same API as the real one so that the SUT thinks it is the real one! “

The concept is very easy to understand, but if you’ve never heard of them, I assume that how a test-double looks like, is still a blurry thing. First I have to say you have to use them with caution and only when appropriated. Apart from that, there are several types of test-doubles. You can find a list of all types in Meszaros’ book and an enumeration of them here

Why use test-doubles?

I think I can summarize the need of a test-double in one line: Use a test-double to keep your tests focussed and fast. If you’re doing CI and TDD, you’ll have a very big test suite after a while, and it’s critical to keep it running in a few minutes. If you don’t, you’ll end up giving up CI, and you’ll loose the continous feedback it offers you.

If your SUT depends on a component that needs a lot of setup code or needs expensive resources, you don’t want to be doing all this in a simple test. Your SUT shouldn’t care how the component it depends on needs to be configured. If you are doing it, you’re writing integration or even acceptance tests to go through the whole system… That’s why replacing a DOC object with a fake, can come in very handy sometimes. Test your SUT in isolation, that’s the goal. The DOC-components will have tests of their own. And you’ll have integration tests on top of it all.

Expectations, verifications, and stuff like that

Before I get to mocks and stubs, you need to understand the expectation-verification thing.

First of all, a mock  or a stub, is just an object that looks like the real DOC, but is actually just a fake which you can use to pass the test, or record the calls your SUT makes to it. When using such a mock/stub, you can set expectations on it. An expectation, is a statement, in which you explicitly expect a call to a particular method or property, with particular parameters, and even a particular return value. After you’ve set the expectations you consider important*, you can verify that these calls actually took place, and thus verifying that your SUT is executing what you expected.

What is a stub?

A stub is an object that you use just to get your code passing. When you don’t really care about how the interaction with the DOC-object happens, you can use a stub to replace the real dependency. A stub can be an empty implementation or a so-called “dumb” implementation. In stead of performing a calculation, you could just return a fixed value.

When you use stubs using a mocking framework, it’s way easier than creating an extra class that can act as a stub for your test. How you do this exactly is for the upcoming post, but the good news is that you don’t need to manually code the stub.

What is a mock?

You’ll use mocks, when you really want to test the behavior of the system. You can set expectations on a mock, of methods/properties to be called with specified parameters and/or return arguments. The final part of the test is then always verification of the expectations that were set. If they were not satisified, your tests fails. This is especially interesting when you need to be completely sure that these were actually called. Just imagine an overly simplified profit calculator. You can never calculate your profits (or losses), if you havn’t calculated your revenues and expenses first, can you? Well, you could expect these are calculated first. (This is of course an overly simplified example for the sake of simplicity…)

What is a fake?

A fake is a class or method that’s implemented just for the sake of testing. It has the same goal as the other variations of test-doubles, replace the depend-on-component to avoid slow or unreliable tests. The classic example, is replacing a Repository that accesses the database, with an in memory repository, which just has a collection of objects it uses to return. That way you’ve got data that you can use to test the SUT on, without the overhead of communication with expensive or external components.

The database is just an example, you can perfectly use a fake object, to hide a complex processing of some kind, and just return the data you need to continue (which would be returned from the complex processing in production code). It will make your tests focus better on the SUT, and they will be a lot faster.


It’s almost impossible to unit test without using mocking-techniques. Your tests can become extremely slow when you have a lot of them, and the continuous feedback loop is lost.
Mocking is a very powerful technique, but beware of misusing it. I actually try to avoid mocks. Just think: Do I need to verify how my SUT interacts with the DOC? If not, don’t use a mock, use a stub. When using lots and lots of mocks, your tests can become brittle. Just imagine refactoring your DOC and breaking 20 tests. After looking into the problem, you notice that 17/20 tests broke because the expectations set on this DOC as a mock, aren’t fully correct anymore? That’s something you really should avoid. Keep your tests focused ;-) .

Recommended readings

Mocks aren’t stubs by Martin Fowler
Test doubles by Martin Fowler
xUnit Test Patterns by Gerard Meszaros (also check out the website)
Test Doubles: When (not) to use them by Davy Brion

I’ll continue this post with how you can use these types of test-doubles using a mocking framework like Rhino.Mocks as soon as I get the chance ;-) .

3 Comments Test types and Continous Integration - 02/23/09

Martin Fowler defines continuous integration as follows:

“Continuous Integration is a software development practice where members of a team integrate their work frequently, usually each person integrates at least daily – leading to multiple integrations per day. Each integration is verified by an automated build (including test) to detect integration errors as quickly as possible. Many teams find that this approach leads to significantly reduced integration problems and allows a team to develop cohesive software more rapidly.”

I’m writing this post as a follow-up to my previous one, types of testing. I’ll talk about how each type of test, fits into a continuous integration process.


You can read all about Continuous integration in Martin Fowler’s paper here. A nice addition (and one that’s lying on my bookshelf as many others), is the book -> Continuous integration: Improving software quality and reducing risk, by Paul Duvall, Steve Matyas and Andrew Glover.

I’ll be talking about two types of builds. I’ll refer to them as the commit build and the secondary build. The primary-stage build (aka the commit build) automatically runs whenever someone commits changes to the repository (see Every build should build the mainline on an integration machine). When this build has tests that fail, the build also fails. The broken tests must be repaired as soon as possible to fix the build. This is a show-stopper and must be dealt with as soon as possible. The secondary-stage build, is a build that runs whenever possible; in my opinion, at least once a day. It can be done manually, or it can run as a nightly build in a script that grabs the latests executables and runs these specific test suite. If this build fails, developers can carry on working. Don’t get me wrong, this build has to be fixed too, but it doesn’t have the same priority as a broken commit build.

Unit tests

Unit tests are the most important part of your continuous integration process (in the sense that these tests are ran the most). After each commit to the repository, the build executes all unit tests to finalize the commit. Your unit tests should run within the commit build and make the build fail if any test fails.

It’s very important to keep these tests focused, and especially fast. You must realize that each commit will execute the tests, and it’s important to have immediate feedback. You can’t be waiting half an hour just to commit some changes, right?! That’s why unit tests use test double patterns (use a test double for each of the SUT’s expensive dependencies). I’ve only read a few pages in Meszaros’ book, but I know it contains a chapter that covers these patterns (can’t wait to get there!).

Integration tests

Integration tests run within the secondary build. These tests are normally slower than unit tests since they test the integration of several components, thus they do use (and set up) actual dependencies. This makes these tests slower, but still, we should try to keep them relatively fast. Running these tests is also very important, but since it’s an expensive operation, we do it far fewer times than running the unit tests. In my opinion, they should run at least once a day. These tests normally include testing access to your database, so I try to  run these tests after each database-change, for example. If they fail, you’ve probably broken a NHibernate mapping, a typed DataSet, or some code using an ugly magic string somewhere. My rule is, run them at least once a day, and every time you’ve made a change that directly affects the integration of your code with an external component.

Acceptance testing

If you’re using automated acceptance testing, these tests should can also be executed automatically within your integration process. I think it’s a good habit to run these tests daily, only it can be very annoying when developing your user interface. Whenever you need to add some textbox somewhere, you’ll have some failing tests (hopefully -remember TDD-). In that case,  I tend to keep the general rule of having them all pass at the end of the iteration, that’s the final deadline. If you choose to do so, it might be a good idea to set up a third build, or to just run them manually as part of your iteration (a bit like regression tests in this sense). If you just run them at the same level as your integration tests, you’ll have your secondary build failing during the whole iteration, which is not a good thing.

If you’re doing user acceptance testing, you should have your CI process deploy your application to the UAT-environment automatically (we do this after each iteration).

Performance testing

I’ve heard of projects where the secondary build also includes performance tests. Usually I don’t think this is necessary, unless in applications where performance is absolutely critical. If a certain level of performance is a requirement, including them in your continuous integration process gives you the advantage of constant feedback and easily identifying what part of your code might contain a memory leak and needs some investigation or rolling back.

I’d use these rules to make up my mind:
1) Do I really need performance tests?
2) Do I really need constant feedback on my application’s performance?
3) Can I have these tests executed by an independent build (not in the commit build, nor in the secondary build)?

Smoke testing and regression testing

I have skipped these two types of tests out of my initial list in my previous post, because these are just unit tests, integration tests, acceptance tests or performance tests in the long run. The big difference in the naming is just because of when they are executed, basicly. And in a continuous integration process, this would be during the commit build, or during the secondary build (or any other builds), depending on the type of test :D .


I think this post gives a nice overview of what tests to put in what build within a continuous integration process. Maybe this approach isn’t the best one, so if you’ve got any other ideas, be sure to leave them in the comments :) .