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Hello everyone.
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If you're attending the talk about magic tricks, I have a survey for you to complete before my talk begins.
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I want to ensure I don't use my allotted time for this, so please feel free to discuss among yourselves.
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I’m going to ask you a question and request an answer by a show of hands. Here’s the question:
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What percentage of the code that you see is tested? This question is not about your own code, so you won’t have a conflict of interest.
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Please consider how well the code you see is tested. Is it 100%, 80%, 60%, 40%, 20%, or 0%? Please raise your hand to indicate.
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So if the code you see is 100% tested, please raise your hand. All right, how about 80%? Now, if the code you see is 60%, 40%, 20%, or 0%, raise your hand.
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For those in the 50% or less category, please raise your hand. And now, let’s begin the talk.
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We are the people who embrace testing; we are the clan that drank the Kool-Aid. This is our public persona. However, you don’t have to look far to realize that many perfectly legitimate members of our community express frustration with their tests.
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Many of you might say, 'I hate my tests.' Why do you hate your tests? Because they kill your productivity; they are slow. You often find yourselves on Twitter, wondering when you’ll be back from waiting for those slow tests. They hold you back. You can make a small change in your application, and while the application still works perfectly fine, all your tests might break. Now, you must go fix all those tests before you can return to actual work. It feels like such a waste of time.
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If your tests are slow and break every time you change something, it’s reasonable to start questioning their value. Everyone tells you that you should have them; they probably save you just enough to make you afraid to delete them entirely. However, the cold, hard truth is that they can make you miserable. If this resonates with you, you are not alone. For many, the promise of testing has not been fulfilled.
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But it doesn't have to be this way; you can have cost-effective tests. Good tests aren’t magic; they are magic tricks, and since they are tricks, you can learn them. There are a few simple and straightforward tricks I’ll explain in the next 28 minutes. For many of you, this will involve reconsidering the number of tests you wrote or previously oversaw.
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Often, we find ourselves writing too many tests or testing the wrong things. This causes frustration and dissatisfaction with the testing process. Today's talk will specifically focus on unit tests; the magic tricks apply to unit tests, not integration tests. Integration tests can be complex and difficult to manage.
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In integration testing, you poke one side of your application, and a series of messages and changes flow through various objects before reaching the end. In contrast, unit tests narrow the focus down to a single object, allowing for precise validations of behavior. They help confirm that every individual piece behaves as expected.
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To summarize, unit tests focus on the internal functionality of one object at a time. We want our unit tests to be thorough; we want them to logically prove that the object behaves correctly. This means we want them to remain stable, so that changes to implementation details don’t break the tests.
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Additionally, we desire speed. Slow tests can hinder development, and maintaining the parsimony in our test code will allow us to focus on what matters. So, we seek thorough, stable, fast, and few unit tests.
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Achieving this requires clarity of vision about your application. I invite you to do a thought experiment: close your eyes, if you need to. Imagine the application you're currently working on and visualize it running in memory; picture the objects and the messages passing between them.
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For many of us, it looks like a complicated tangle of spaghetti code. When our applications appear this way, it’s no wonder that maintaining tests becomes a challenge as well. The way out of this thicket is through focusing on messages and employing the magic tricks I will share with you.
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The objects we will be testing have a very straightforward perspective regarding messages. Each object operates as a black box, managing an internal state while only exposing a specific interface for communication.
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An object knows about messages from three places: incoming messages from other objects, outgoing messages it sends to others, and messages it sends to itself. Incoming messages can breach the containment of the object, so the internal workings must be protected.
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Outgoing messages must also uphold the same level of protection, making sure that the inside of an object does not get exposed. Self-sent messages are contained within the object's own boundaries, as they do not expose anything externally.
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These messages come in two categories: queries and commands. A query message runs without side effects, while a command message has an effect but does not return a directly useful value. It's important to distinguish between the two types of messages for effective testing.
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Some of you might have encountered situations where a message inadvertently acts as both a query and a command, leading to unforeseen consequences. This can create dependencies in your code, making it crucial to identify what type of message you are dealing with.
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In our testing endeavors, we will now look at a grid that classifies messages into six different cells, each with its own testing rules. My goal is to walk you through how to populate this grid effectively.
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We'll start with incoming query messages. For example, consider a bicycle class with a diameter method. This method is a query; it calculates and returns a result without causing any state changes. To test it, we will create a new instance and verify the returned value.
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This assertion checks if the output is within a specified margin of error. It’s a direct comparison of results without concerning the internal operation of the method—resulting in a straightforward test.
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Continuing with tests for incoming query messages, let’s consider a gear class that implements a gear_in_inches method. This method sends a private message to itself and another to a collaborating object, effectively returning a value while ensuring no side effects occur.
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The approach to testing remains the same: we send the message and assert the value returned. This means testing the interface rather than focusing on the implementation details, allowing for safe refactoring later.
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Now let’s discuss incoming command messages, such as a set_cog method in our gear class. This method takes an argument and modifies internal state, potentially returning a result. In this instance, we should test the command aspect of this method.
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To test it, we’d send the message that triggers a change and follow that up with an assertion about the side effect caused. This indicates that incoming command messages should be tested by asserting the expected immediate outcomes.
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Now we move into a crucial aspect: do not test private methods directly. If a public interface correctly relies on private methods, testing them only creates redundancies and can lead to fragility in your tests.
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Instead of having tests that bind you to the current implementation, focus on what the public interface promises. Keep this in mind to allow for safer and easier refactoring of your code.
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As we proceed with the second pattern, remember the core guideline: we should avoid making expectations on private messages unless absolutely necessary. It's essential to balance clarity and testing effectiveness.
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Next, we’ll observe outgoing query messages—similar in testing rules to incoming query messages. An outgoing query, like sending a diameter message to another class, is also devoid of side effects and should be treated accordingly.
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Testing the outgoing query message with assertions about the result is redundant; the incoming message does all the necessary work. So, we should avoid verifying the outgoing queries per the previously stated rules.
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For outgoing command messages, let's say we have a set_cog method that not only modifies internal state but also sends a change message to an observer. The observer notices this change in real time and reacts accordingly.
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This raises the question of testing: should we test the side effects created by this outgoing command message? Reach out to the observer message; this could lead to unnecessary integration tests that would bind us to distant side effects, compromising our unit tests.
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Thus, instead, we should use mocks here to verify that calls to the relevant methods happen without needing to check what lies beyond. Ensuring that the closest edge is validated while observing stability in tests.
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Mocking allows us to maintain that bounds of testing where we only assert the message sending, not the distant side effects. This practice should help keep our unit tests focused, fast, and reliable.
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To summarize, testing rules for outgoing command messages state that we should use mocks for expectations, avoiding unnecessary bindings with intermediate objects when it can be easily avoided.
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In conclusion, I encourage you to adopt these principles in your testing processes, aiming for simple, effective tests focusing on the essential aspects of your code, tailoring them to meet your team's needs as best as possible.
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As the talk draws to a close, I want to thank you all for your time. Please keep exploring these magic tricks and remember that good testing can transform your development experience. Test quality can lead to an overall improvement in productivity and job satisfaction.
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Ultimately, with practice and persistence, you can make testing your ally rather than a burden. Thank you!
