To indicate an expectation you call the method, with the arguments you expect on the mock. You follow this with a call to the control if you want a return value. Once you've finished setting expectations you call replay on the control - at which point the mock finishes the recording and is ready to respond to the primary object. Once done you call verify on the control. It has an advantage over the constraints of jMock in that you are making actual method calls to the mock rather than specifying method names in strings.
This means you get to use code-completion in your IDE and any refactoring of method names will automatically update the tests. The downside is that you can't have the looser constraints. The developers of jMock are working on a new version which will use other techniques to allow you use actual method calls.
When they were first introduced, many people easily confused mock objects with the common testing notion of using stubs. Since then it seems people have better understood the differences and I hope the earlier version of this paper helped.
However to fully understand the way people use mocks it is important to understand mocks and other kinds of test doubles. Don't worry if this is a new term to you, wait a few paragraphs and all will be clear. When you're doing testing like this, you're focusing on one element of the software at a time -hence the common term unit testing.
The problem is that to make a single unit work, you often need other units - hence the need for some kind of warehouse in our example.
In the two styles of testing I've shown above, the first case uses a real warehouse object and the second case uses a mock warehouse, which of course isn't a real warehouse object. Using mocks is one way to not use a real warehouse in the test, but there are other forms of unreal objects used in testing like this. The vocabulary for talking about this soon gets messy - all sorts of words are used: stub, mock, fake, dummy.
For this article I'm going to follow the vocabulary of Gerard Meszaros's book. It's not what everyone uses, but I think it's a good vocabulary and since it's my essay I get to pick which words to use. Meszaros uses the term Test Double as the generic term for any kind of pretend object used in place of a real object for testing purposes.
The name comes from the notion of a Stunt Double in movies. One of his aims was to avoid using any name that was already widely used. Meszaros then defined five particular kinds of double:. Of these kinds of doubles, only mocks insist upon behavior verification. The other doubles can, and usually do, use state verification. Mocks actually do behave like other doubles during the exercise phase, as they need to make the SUT believe it's talking with its real collaborators - but mocks differ in the setup and the verification phases.
To explore test doubles a bit more, we need to extend our example. Many people only use a test double if the real object is awkward to work with. A more common case for a test double would be if we said that we wanted to send an email message if we failed to fill an order. The problem is that we don't want to send actual email messages out to customers during testing. So instead we create a test double of our email system, one that we can control and manipulate.
Here we can begin to see the difference between mocks and stubs. If we were writing a test for this mailing behavior, we might write a simple stub like this.
Of course this is a very simple test - only that a message has been sent. We've not tested it was sent to the right person, or with the right contents, but it will do to illustrate the point. In both cases I'm using a test double instead of the real mail service. There is a difference in that the stub uses state verification while the mock uses behavior verification. In order to use state verification on the stub, I need to make some extra methods on the stub to help with verification.
As a result the stub implements MailService but adds extra test methods. Mock objects always use behavior verification, a stub can go either way. Meszaros refers to stubs that use behavior verification as a Test Spy. The difference is in how exactly the double runs and verifies and I'll leave that for you to explore on your own.
Now I'm at the point where I can explore the second dichotomy: that between classical and mockist TDD. The big issue here is when to use a mock or other double. The classical TDD style is to use real objects if possible and a double if it's awkward to use the real thing.
So a classical TDDer would use a real warehouse and a double for the mail service. The kind of double doesn't really matter that much. A mockist TDD practitioner, however, will always use a mock for any object with interesting behavior. In this case for both the warehouse and the mail service. Although the various mock frameworks were designed with mockist testing in mind, many classicists find them useful for creating doubles. This led to renaming tests as behaviors to better explore where TDD helps with thinking about what an object needs to do.
BDD takes a mockist approach, but it expands on this, both with its naming styles, and with its desire to integrate analysis within its technique. I won't go into this more here, as the only relevance to this article is that BDD is another variation on TDD that tends to use mockist testing. I'll leave it to you to follow the link for more information. You sometimes see "Detroit" style used for "classical" and "London" for "mockist". This alludes to the fact that XP was originally developed with the C3 project in Detroit and the mockist style was developed by early XP adopters in London.
I should also mention that many mockist TDDers dislike that term, and indeed any terminology that implies a different style between classical and mockist testing. They don't consider that there is a useful distinction to be made between the two styles. What are the arguments to bear in mind when making the choices between them? I'll begin with the state versus behavior verification choice. The first thing to consider is the context. Are we thinking about an easy collaboration, such as order and warehouse, or an awkward one, such as order and mail service?
If it's an easy collaboration then the choice is simple. I use a real object and state verification. No decisions at all. If it's an awkward collaboration, then there's no decision if I'm a mockist - I just use mocks and behavior verification. If I'm a classicist then I do have a choice, but it's not a big deal which one to use.
Usually classicists will decide on a case by case basis, using the easiest route for each situation. So as we see, state versus behavior verification is mostly not a big decision.
The real issue is between classic and mockist TDD. As it turns out the characteristics of state and behavior verification do affect that discussion, and that's where I'll focus most of my energy. But before I do, let me throw in an edge case. Occasionally you do run into things that are really hard to use state verification on, even if they aren't awkward collaborations. A great example of this is a cache. The whole point of a cache is that you can't tell from its state whether the cache hit or missed - this is a case where behavior verification would be the wise choice for even a hard core classical TDDer.
I'm sure there are other exceptions in both directions. Mock objects came out of the XP community, and one of the principal features of XP is its emphasis on Test Driven Development - where a system design is evolved through iteration driven by writing tests.
Thus it's no surprise that the mockists particularly talk about the effect of mockist testing on a design. In particular they advocate a style called need-driven development. With this style you begin developing a user story by writing your first test for the outside of your system, making some interface object your SUT.
By thinking through the expectations upon the collaborators, you explore the interaction between the SUT and its neighbors - effectively designing the outbound interface of the SUT. Once you have your first test running, the expectations on the mocks provide a specification for the next step and a starting point for the tests.
You turn each expectation into a test on a collaborator and repeat the process working your way into the system one SUT at a time. This style is also referred to as outside-in, which is a very descriptive name for it. It works well with layered systems. You first start by programming the UI using mock layers underneath.
Then you write tests for the lower layer, gradually stepping through the system one layer at a time. This is a very structured and controlled approach, one that many people believe is helpful to guide newcomers to OO and TDD.
Classic TDD doesn't provide quite the same guidance. You can do a similar stepping approach, using stubbed methods instead of mocks. To do this, whenever you need something from a collaborator you just hard-code exactly the response the test requires to make the SUT work.
Then once you're green with that you replace the hard coded response with a proper code. But classic TDD can do other things too. A common style is middle-out. In this style you take a feature and decide what you need in the domain for this feature to work.
You get the domain objects to do what you need and once they are working you layer the UI on top. Doing this you might never need to fake anything.
A lot of people like this because it focuses attention on the domain model first, which helps keep domain logic from leaking into the UI. I should stress that both mockists and classicists do this one story at a time. There is a school of thought that builds applications layer by layer, not starting one layer until another is complete. Both classicists and mockists tend to have an agile background and prefer fine-grained iterations.
A stub exists to tell the accessing entity the file has been moved to another location. There are several problems with this approach that stem from the fact that stubs are proprietary and static. The biggest problem is that if the stub file is corrupted or deleted, the moved data gets orphaned. Stubs are proprietary, so you need either storage agents so each storage can understand the stub, or a proprietary interface to each storage. This means they are not portable, and managing revisions of stubs along with storage upgrades or migrations is time consuming, cumbersome, and error-prone.
Since stubs are proprietary, adding storage agents impacts the data path of both hot and cold data which is also undesirable as it can cause performance degradation of hot data and metadata. Symbolic links are transparent to users; the links appear as normal files or directories and can be acted upon by the user or application in exactly the same manner. A symbolic link contains a text string that is automatically interpreted and followed by the file system as a path to another file or directory.
The symbolic link is a second file that exists independently of its target. So, they do not require any agents on each storage or any proprietary interfaces. While stubs are proprietary by nature, symbolic links are based on industry standard protocols and are widely recognized. This precludes the need for any extra agents or overhead to manage them.
Komprise took the native advantages of symbolic links and innovated further, dynamically binding them to the file at runtime, akin to a DNS router.
This makes the links themselves disposable — if a link is accidentally deleted, Komprise can restore it. When Komprise archives data from a file system, it replaces the original file with a dynamic link address: resilient, always available and flexible. There are several benefits to the dynamic link approach:. Once Komprise moves a file and replaces it with a dynamic link, if the file is moved again — say, for example, after the first archive a file to an object store and another later to the cloud — the dynamic link address does not need to be changed.
This eliminates the headaches of managing links. Users and applications continue to access the moved data transparently from the original location even as the data is moved throughout its lifecycle, without any changes. If a user accidentally deletes the links on the source, Komprise can repopulate the links since the link itself does not contain the context of the moved file. Data can be moved from one destination to another e.
To the user, this means no disruption. Users and applications that rely on the data that has been moved by Komprise are unaffected. While most file servers support symbolic links, there are a few situations where the file servers do not support symbolic links. For such file servers, Komprise uses stubs that are dynamic. Some organizations utilize contract testing to completely replace end-to-end or functional testing.
When the user service connects to the endpoint of the weather service with the date the request , the user service processes the date data to get the weather for that date. These two services have a contract: the weather service will maintain the endpoint to be always accessible by the user service and provide the valid data that the user service is requesting, and in the same format.
Instead of the user service making the actual request call to the weather service in the test, you can create a mocked response. Since there is a contract between two services, the endpoint and response should not change.
This will free both services from depending on each other during tests, allowing tests to be faster and more reliable. In the last post, we talked about running different tests in different environments and how sometimes, it can be useful to run the same test in a different environment with a different configuration.
Contract tests are one of the great examples of the latter case. We can achieve different goals when running contract tests in different environments with different configurations. However, when it goes to an upper layer environment such as QA or Staging, the same test can be used without a mocked contract but with the actual external dependency connection.
Mbtest is one tool that can help with the kind of contract testing and mocking response I explained above. Angel Rivera Developer Advocate. By submitting this form, you are agreeing to our Terms of Use and Privacy Policy. You should receive an automated response notifying you that we received your info.
Someone from our Enterprise team will be reaching out to you shortly. Start Building for Free. Showing results for:. Test-driven development using mocking and stubbing In How and where to segregate test environments , I talked about building a structured path to production: which tests to include, when to do them, and why. What is mocking and stubbing used for? What is mock testing? What is stub testing? Which means wherever and whenever you run the test for that function: You will need to ensure that the file that the test will be looking for exists; when it does not exist, the test fails.
The test code for the implementation above looks like this: unittest. Mocking and stubbing of internal functions Mocks and stubs are very handy for unit tests.
Mocking in integration testing With integration tests, you are testing relationships between services. You have the flexibility to scope the test to cover just the parts you can control and change.
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