Tag Archives: TotT

Testing on the Toilet: Separation of Concerns? That’s a Wrap!

This article was adapted from a Google Testing on the Toilet (TotT) episode. You can download a printer-friendly version of this TotT episode and post it in your office.


By Stefan Kennedy


The following function decodes a byte array as an image using an API named SpeedyImg. What maintenance problems might arise due to referencing an API owned by a different team?

SpeedyImgImage decodeImage(List<SpeedyImgDecoder> decoders, byte[] data) {
SpeedyImgOptions options = getDefaultConvertOptions();
for (SpeedyImgDecoder decoder : decoders) {
SpeedyImgResult decodeResult = decoder.decode(decoder.formatBytes(data));
SpeedyImgImage image = decodeResult.getImage(options);
if (validateGoodImage(image)) { return image; }
}
throw new RuntimeException();
}



Details about how to call the API are mixed with domain logic, which can make the code harder to understand. For example, the call to decoder.formatBytes() is required by the API, but how the bytes are formatted isn’t relevant to the domain logic.


Additionally, if this API is used in many places across a codebase, then all usages may need to change if the way the API is used changes. For example, if the return type of this function is changed to the more generic SpeedyImgResult type, usages of SpeedyImgImage would need to be updated.


To avoid these maintenance problems, create wrapper types to hide API details behind an abstraction:

Image decodeImage(List<ImageDecoder> decoders, byte[] data) {
for (ImageDecoder decoder : decoders) {
Image decodedImage = decoder.decode(data);
if (validateGoodImage(decodedImage)) { return decodedImage; }
}
throw new RuntimeException();
}


Wrapping an external API follows the Separation of Concerns principle, since the logic for how the API is called is separated from the domain logic. This has many benefits, including:
  • If the way the API is used changes, encapsulating the API in a wrapper insulates how far those changes can propagate across your codebase.
  • You can modify the interface or the implementation of types you own, but you can’t for API types.
  • It is easier to switch or add another API, since they can still be represented by the introduced types (e.g. ImageDecoder/Image).
  • Readability can improve as you don’t need to sift through API code to understand core logic.

Not all external APIs need to be wrapped. For example, if an API would take a huge effort to separate or is simple enough that it doesn't pollute the codebase, it may be better not to introduce wrapper types (e.g. library types like List in Java or std::vector in C++). When in doubt, keep in mind that a wrapper should only be added if it will clearly improve the code (see the YAGNI principle).


“Separation of Concerns” in the context of external APIs is also described by Martin Fowler in his blog post, Refactoring code that accesses external services


Testing on the Toilet: Separation of Concerns? That’s a Wrap!

This article was adapted from a Google Testing on the Toilet (TotT) episode. You can download a printer-friendly version of this TotT episode and post it in your office.


By Stefan Kennedy


The following function decodes a byte array as an image using an API named SpeedyImg. What maintenance problems might arise due to referencing an API owned by a different team?

SpeedyImgImage decodeImage(List<SpeedyImgDecoder> decoders, byte[] data) {
SpeedyImgOptions options = getDefaultConvertOptions();
for (SpeedyImgDecoder decoder : decoders) {
SpeedyImgResult decodeResult = decoder.decode(decoder.formatBytes(data));
SpeedyImgImage image = decodeResult.getImage(options);
if (validateGoodImage(image)) { return image; }
}
throw new RuntimeException();
}



Details about how to call the API are mixed with domain logic, which can make the code harder to understand. For example, the call to decoder.formatBytes() is required by the API, but how the bytes are formatted isn’t relevant to the domain logic.


Additionally, if this API is used in many places across a codebase, then all usages may need to change if the way the API is used changes. For example, if the return type of this function is changed to the more generic SpeedyImgResult type, usages of SpeedyImgImage would need to be updated.


To avoid these maintenance problems, create wrapper types to hide API details behind an abstraction:

Image decodeImage(List<ImageDecoder> decoders, byte[] data) {
for (ImageDecoder decoder : decoders) {
Image decodedImage = decoder.decode(data);
if (validateGoodImage(decodedImage)) { return decodedImage; }
}
throw new RuntimeException();
}


Wrapping an external API follows the Separation of Concerns principle, since the logic for how the API is called is separated from the domain logic. This has many benefits, including:
  • If the way the API is used changes, encapsulating the API in a wrapper insulates how far those changes can propagate across your codebase.
  • You can modify the interface or the implementation of types you own, but you can’t for API types.
  • It is easier to switch or add another API, since they can still be represented by the introduced types (e.g. ImageDecoder/Image).
  • Readability can improve as you don’t need to sift through API code to understand core logic.

Not all external APIs need to be wrapped. For example, if an API would take a huge effort to separate or is simple enough that it doesn't pollute the codebase, it may be better not to introduce wrapper types (e.g. library types like List in Java or std::vector in C++). When in doubt, keep in mind that a wrapper should only be added if it will clearly improve the code (see the YAGNI principle).


“Separation of Concerns” in the context of external APIs is also described by Martin Fowler in his blog post, Refactoring code that accesses external services


Testing on the Toilet: Separation of Concerns? That’s a Wrap!

This article was adapted from a Google Testing on the Toilet (TotT) episode. You can download a printer-friendly version of this TotT episode and post it in your office.


By Stefan Kennedy


The following function decodes a byte array as an image using an API named SpeedyImg. What maintenance problems might arise due to referencing an API owned by a different team?

SpeedyImgImage decodeImage(List<SpeedyImgDecoder> decoders, byte[] data) {
SpeedyImgOptions options = getDefaultConvertOptions();
for (SpeedyImgDecoder decoder : decoders) {
SpeedyImgResult decodeResult = decoder.decode(decoder.formatBytes(data));
SpeedyImgImage image = decodeResult.getImage(options);
if (validateGoodImage(image)) { return image; }
}
throw new RuntimeException();
}



Details about how to call the API are mixed with domain logic, which can make the code harder to understand. For example, the call to decoder.formatBytes() is required by the API, but how the bytes are formatted isn’t relevant to the domain logic.


Additionally, if this API is used in many places across a codebase, then all usages may need to change if the way the API is used changes. For example, if the return type of this function is changed to the more generic SpeedyImgResult type, usages of SpeedyImgImage would need to be updated.


To avoid these maintenance problems, create wrapper types to hide API details behind an abstraction:

Image decodeImage(List<ImageDecoder> decoders, byte[] data) {
for (ImageDecoder decoder : decoders) {
Image decodedImage = decoder.decode(data);
if (validateGoodImage(decodedImage)) { return decodedImage; }
}
throw new RuntimeException();
}


Wrapping an external API follows the Separation of Concerns principle, since the logic for how the API is called is separated from the domain logic. This has many benefits, including:
  • If the way the API is used changes, encapsulating the API in a wrapper insulates how far those changes can propagate across your codebase.
  • You can modify the interface or the implementation of types you own, but you can’t for API types.
  • It is easier to switch or add another API, since they can still be represented by the introduced types (e.g. ImageDecoder/Image).
  • Readability can improve as you don’t need to sift through API code to understand core logic.

Not all external APIs need to be wrapped. For example, if an API would take a huge effort to separate or is simple enough that it doesn't pollute the codebase, it may be better not to introduce wrapper types (e.g. library types like List in Java or std::vector in C++). When in doubt, keep in mind that a wrapper should only be added if it will clearly improve the code (see the YAGNI principle).


“Separation of Concerns” in the context of external APIs is also described by Martin Fowler in his blog post, Refactoring code that accesses external services


Testing on the Toilet: Testing UI Logic? Follow the User!

This article was adapted from a Google Testing on the Toilet (TotT) episode. You can download a printer-friendly version of this TotT episode and post it in your office.

By Carlos Israel Ortiz García


After years of anticipation, you're finally able to purchase Google's hottest new product, gShoe*. But after clicking the "Buy" button, nothing happened! Inspecting the HTML, you notice the problem:

<button disabled=”true” click=”$handleBuyClick(data)”>Buy</button>

Users couldn’t buy their gShoes because the “Buy” button was disabled. The problem was due to the unit test for handleBuyClick, which passed even though the user interface had a bug:

it('submits purchase request', () => {
controller = new PurchasePage();
// Call the method that handles the "Buy" button click
controller.handleBuyClick(data);
expect(service).toHaveBeenCalledWith(expectedData);
});

In the above example, the test failed to detect the bug because it bypassed the UI element and instead directly invoked the "Buy" button click handler. To be effective, tests for UI logic should interact with the components on the page as a browser would, which allows testing the behavior that the end user experiences. Writing tests against UI components rather than calling handlers directly faithfully simulates user interactions (e.g., add items to a shopping cart, click a purchase button, or verify an element is visible on the page), making the tests more comprehensive.


The test for the “Buy” button should instead exercise the entire UI component by interacting with the HTML element, which would have caught the disabled button issue:

it('submits purchase request', () => {
// Renders the page with the “Buy” button and its associated code.
render(PurchasePage);
// Tries to click the button, fails the test, and catches the bug!
buttonWithText('Buy').dispatchEvent(new Event(‘click’));
expect(service).toHaveBeenCalledWith(expectedData);
});


Why should tests be written this way? Unlike end-to-end tests, tests for individual UI components don’t require a backend server or the entire app to be rendered. Instead, these  tests run in the same self-contained environment and take a similar amount of time to execute as unit tests that just execute the underlying event handlers directly. Therefore, the UI acts as the public API, leaving the business logic as an implementation detail (also known as the "Use the Front Door First" principle), resulting in better coverage of a feature.

Disclaimer: “gShoe” is not a real Google product. Unfortunately you can’t buy a pair even if the bug is fixed!

Testing on the Toilet: Avoid Hardcoding Values for Better Libraries

This article was adapted from a Google Testing on the Toilet (TotT) episode. You can download a printer-friendly version of this TotT episode and post it in your office.

By Adel Saoud


You may have been in a situation where you're using a value that always remains the same, so you define a constant. This can be a good practice as it removes magic values and improves code readability. But be mindful that hardcoding values can make usability and potential refactoring significantly harder.

Consider the following function that relies on hardcoded values:
// Declared in the module.
constexpr int kThumbnailSizes[] = {480, 576, 720};

// Returns thumbnails of various sizes for the given image.
std::vector<Image> GetThumbnails(const Image& image) {
std::vector<Image> thumbnails;
for (const int size : kThumbnailSizes) {
thumbnails.push_back(ResizeImage(image, size));
}
return thumbnails;
}


Using hardcoded values can make your code:
  • Less predictable: The caller might not expect the function to be relying on hardcoded values outside its parameters; a user of the function shouldn’t need to read the function’s code to know that. Also, it is difficult to predict the product/resource/performance implications of changing these hardcoded values.
  • Less reusable: The caller is not able to call the function with different values and is stuck with the hardcoded values. If the caller doesn’t need all these sizes or needs a different size, the function has to be forked or refactored to avoid aforementioned complications with existing callers.

When designing a library, prefer to pass required values, such as through a function call or a constructor. The code above can be improved as follows:
std::vector<Image> GetThumbnails(const Image& image, absl::Span<const int> sizes) {
std::vector<Image> thumbnails;
for (const int size : sizes) {
thumbnails.push_back(ResizeImage(image, size));
}
return thumbnails;
}


If most of the callers use the same value for a certain parameter, make your code configurable so that this value doesn't need to be duplicated by each caller. For example, you can define a public constant that contains a commonly used value, or use default arguments in languages that support this feature (e.g. C++ or Python).
// Declared in the public header.
inline constexpr int kDefaultThumbnailSizes[] = {480, 576, 720};

// Default argument allows the function to be used without specifying a size.
std::vector<Image> GetThumbnails(const Image& image,
absl::Span<const int> sizes = kDefaultThumbnailSizes);

Testing on the Toilet: Don’t Mock Types You Don’t Own

This article was adapted from a Google Testing on the Toilet (TotT) episode. You can download a printer-friendly version of this TotT episode and post it in your office.

By Stefan Kennedy and Andrew Trenk

The code below mocks a third-party library. What problems can arise when doing this?

// Mock a salary payment library
@Mock SalaryProcessor mockSalaryProcessor;
@Mock TransactionStrategy mockTransactionStrategy;
...
when(mockSalaryProcessor.addStrategy()).thenReturn(mockTransactionStrategy);
when(mockSalaryProcessor.paySalary()).thenReturn(TransactionStrategy.SUCCESS);
MyPaymentService myPaymentService = new MyPaymentService(mockSalaryProcessor);
assertThat(myPaymentService.sendPayment()).isEqualTo(PaymentStatus.SUCCESS);

Mocking types you don’t own can make maintenance more difficult:
  • It can make it harder to upgrade the library to a new version: The expectations of an API hardcoded in a mock can be wrong or get out of date. This may require time-consuming work to manually update your tests when upgrading the library version. In the above example, an update that changes addStrategy() to return a new type derived from TransactionStrategy (e.g. SalaryStrategy) requires the mock to be updated to return this type, even though the code under test doesn’t need to be changed since it can still reference TransactionStrategy.
  • It can make it harder to know whether a library update introduced a bug in your code: The assumptions built into mocks may get out of date as changes are made to the library, resulting in tests that pass even when the code under test has a bug. In the above example, if a library update changes paySalary() to instead return TransactionStrategy.SCHEDULED, a bug could potentially be introduced due to the code under test not handling this return value properly. However, the maintainer wouldn’t know because the mock would not return this value so the test would continue to pass.
Instead of using a mock, use the real implementation, or if that’s not feasible, use a fake implementation that is ideally provided by the library owner. This reduces the maintenance burden since the issues with mocks listed above don’t occur when using a real or fake implementation. For example:
FakeSalaryProcessor fakeProcessor = new FakeSalaryProcessor(); // Designed for tests
MyPaymentService myPaymentService = new MyPaymentService(fakeProcessor);
assertThat(myPaymentService.sendPayment()).isEqualTo(PaymentStatus.SUCCESS);

If you can’t use the real implementation and a fake implementation doesn’t exist (and library owners aren’t able to create one), create a wrapper class that calls the type, and mock this instead. This reduces the maintenance burden by avoiding mocks that rely on the signatures of the library API. For example:


@Mock MySalaryProcessor mockMySalaryProcessor; // Wraps the SalaryProcessor library
...
// Mock the wrapper class rather than the library itself
when(mockMySalaryProcessor.sendSalary()).thenReturn(PaymentStatus.SUCCESS);

MyPaymentService myPaymentService = new MyPaymentService(mockMySalaryProcessor);
assertThat(myPaymentService.sendPayment()).isEqualTo(PaymentStatus.SUCCESS);

To avoid the problems listed above, prefer to test the wrapper class with calls to the real implementation. The downsides of testing with the real implementation (e.g. tests taking longer to run) are limited only to the tests for this wrapper class rather than tests throughout your codebase.

“Don’t mock types you don’t own” is also described by Steve Freeman and Nat Pryce in their book, Growing Object Oriented Software, Guided by TestsFor more details about the downsides of overusing mocks (even for types you do own), see this Google Testing Blog post.

Testing on the Toilet: Tests Too DRY? Make Them DAMP!

This article was adapted from a Google Testing on the Toilet (TotT) episode. You can download a printer-friendly version of this TotT episode and post it in your office.

By Derek Snyder and Erik Kuefler

The test below follows the DRY principle (“Don’t Repeat Yourself”), a best practice that encourages code reuse rather than duplication, e.g., by extracting helper methods or by using loops. But is it a well-written test?
def setUp(self):
self.users = [User('alice'), User('bob')] # This field can be reused across tests.
self.forum = Forum()

def testCanRegisterMultipleUsers(self):
self._RegisterAllUsers()
for user in self.users: # Use a for-loop to verify that all users are registered.
self.assertTrue(self.forum.HasRegisteredUser(user))
def _RegisterAllUsers(self): # This method can be reused across tests.
for user in self.users:
self.forum.Register(user)

While the test body above is concise, the reader needs to do some mental computation to understand it, e.g., by following the flow of self.users from setUp() through _RegisterAllUsers(). Since tests don't have tests, it should be easy for humans to manually inspect them for correctness, even at the expense of greater code duplication. This means that the DRY principle often isn’t a good fit for unit tests, even though it is a best practice for production code.

In tests we can use the DAMP principle (“Descriptive and Meaningful Phrases”), which emphasizes readability over uniqueness. Applying this principle can introduce code redundancy (e.g., by repeating similar code), but it makes tests more obviously correct. Let’s add some DAMP-ness to the above test:

def setUp(self):
self.forum = Forum()

def testCanRegisterMultipleUsers(self):
# Create the users in the test instead of relying on users created in setUp.
user1 = User('alice')
user2 = User('bob')


# Register the users in the test instead of in a helper method, and don't use a for-loop.
self.forum.Register(user1)
self.forum.Register(user2)
# Assert each user individually instead of using a for-loop.
self.assertTrue(self.forum.HasRegisteredUser(user1))
self.assertTrue(self.forum.HasRegisteredUser(user2))

Note that the DRY principle is still relevant in tests; for example, using a helper function for creating value objects can increase clarity by removing redundant details from the test body. Ideally, test code should be both readable and unique, but sometimes there’s a trade-off. When writing unit tests and faced with a choice between the DRY and DAMP principles, lean more heavily toward DAMP.

Code Health: Respectful Reviews == Useful Reviews

This is another post in our Code Health series. A version of this post originally appeared in Google bathrooms worldwide as a Google Testing on the Toilet episode. You can download a printer-friendly version to display in your office.

By Liz Kammer (Google), Maggie Hodges (UX research consultant), and Ambar Murillo (Google)

While code review is recognized as a valuable tool for improving the quality of software projects, code review comments that are perceived as being unclear or harsh can have unfavorable consequences: slow reviews, blocked dependent code reviews, negative emotions, or negative perceptions of other contributors or colleagues.

Consider these tips to resolve code review comments respectfully.

As a Reviewer or Author:
  • DO: Assume competence. An author’s implementation or a reviewer’s recommendation may be due to the other party having different context than you. Start by asking questions to gain understanding.
  • DO: Provide rationale or context, such as a best practices document, a style guide, or a design document. This can help others understand your decision or provide mentorship.
  • DO: Consider how comments may be interpreted. Be mindful of the differing ways hyperbole, jokes, and emojis may be perceived.
    Author Don’t:
    I prefer short names so I’d rather
    not change this. Unless you make
    me? :)
    Author Do:
    Best practice suggests omitting
    obvious/generic terms. I’m not
    sure how to reconcile that
    advice with this request.
  • DON’T: Criticize the person. Instead, discuss the code. Even the perception that a comment is about a person (e.g., due to using “you” or “your”) distracts from the goal of improving the code.
    Reviewer Don’t:
    Why are you using this approach?
    You’re adding unnecessary
    complexity.
    Reviewer Do:
    This concurrency model appears to
    be adding complexity to the
    system without any visible
    performance benefit.
  • DON’T: Use harsh language. Code review comments with a negative tone are less likely to be useful. For example, prior research found very negative comments were considered useful by authors 57% of the time, while more-neutral comments were useful 79% of the time.  

As a Reviewer:
  • DO: Provide specific and actionable feedback. If you don’t have specific advice, sometimes it’s helpful to ask for clarification on why the author made a decision.
    Reviewer Don’t:
    I don’t understand this.
    Reviewer Do:
    If this is an optimization, can you
    please add comments?
  • DO: Clearly mark nitpicks and optional comments by using prefixes such as ‘Nit’ or ‘Optional’. This allows the author to better gauge the reviewer’s expectations.

As an Author:
  • DO: Clarify code or reply to the reviewer’s comment in response to feedback. Failing to do so can signal a lack of receptiveness to implementing improvements to the code.
    Author Don’t:
    That makes sense in some cases but
    not here.
    Author Do:
    I added a comment about why
    it’s implemented that way.
  • DO: When disagreeing with feedback, explain the advantage of your approach. In cases where you can’t reach consensus, follow Google’s guidance for resolving conflicts in code review.

Testing on the Toilet: Exercise Service Call Contracts in Tests

This article was adapted from a Google Testing on the Toilet (TotT) episode. You can download a printer-friendly version of this TotT episode and post it in your office.

By Ben Yu

The following test mocks out a service call to CloudService Does the test provide enough confidence that the service call is likely to work?

@Test public void uploadFileToCloudStorage() {
when(mockCloudService.write(
WriteRequest.newBuilder().setUserId(“testuser”).setFileType(“plain/text”)...))
.thenReturn(WriteResponse.newBuilder().setUploadId(“uploadId”).build());

CloudUploader cloudUploader = new CloudUploader(mockCloudService);


Uri uri = cloudUploader.uploadFile(new File(“/path/to/foo.txt”));
// The uploaded file URI contains the user ID, file type, and upload ID. (Or does it?)
assertThat(uri).isEqualTo(new Uri(“/testuser/text/uploadId.txt”));

Lots of things can go wrong, especially when service contracts get complex. For example, plain/text may not be a valid file type, and you can’t verify that the URI of the uploaded file is correct.

If the code under test relies on the contract of a service, prefer exercising the service call instead of mocking it out. This gives you more confidence that you are using the service correctly:
@Test public void uploadFileToCloudStorage() {
CloudUploader cloudUploader = new CloudUploader(cloudService);
Uri uri = cloudUploader.uploadFile(”/path/to/foo.txt”);
assertThat(cloudService.retrieveFile(uri)).isEqualTo(readContent(“/path/to/foo.txt));
}

How can you exercise the service call?

  1. Use a fake.  A fake is a fast and lightweight implementation of the service that behaves just like the real implementation. A fake is usually maintained by the service owners; don’t create your own fake unless you can ensure its behavior will stay in sync with the real implementation.  Learn more about fakes at testing.googleblog.com/2013/06/testing-on-toilet-fake-your-way-to.html.
  2. Use a hermetic server.  This is a real server that is brought up by the test and runs on the same machine that the test is running on. A downside of using a hermetic server is that starting it up and interacting with it can slow down tests.  Learn more about hermetic servers at testing.googleblog.com/2012/10/hermetic-servers.html.
If the service you are using doesn’t have a fake or hermetic server, mocks may be the only tool at your disposal. But if your tests are not exercising the service call contract, you must take extra care to ensure the service call works, such as by having a comprehensive suite of end-to-end tests or resorting to manual QA (which can be inefficient and hard to scale).

Code Health: Make Interfaces Hard to Misuse

This is another post in our Code Health series. A version of this post originally appeared in Google bathrooms worldwide as a Google Testing on the Toilet episode. You can download a printer-friendly version to display in your office.

By Marek Kiszkis

We all try to avoid errors in our code. But what about errors created by callers of your code? A good interface design can make it easy for callers to do the right thing, and hard for callers to do the wrong thing. Don't push the responsibility of maintaining invariants required by your class on to its callers.
Can you see the issues that can arise with this code?
class Vector {
explicit Vector(int num_slots); // Creates an empty vector with `num_slots` slots.
int RemainingSlots() const; // Returns the number of currently remaining slots.
void AddSlots(int num_slots); // Adds `num_slots` more slots to the vector.
// Adds a new element at the end of the vector. Caller must ensure that RemainingSlots()
// returns at least 1 before calling this, otherwise caller should call AddSlots().
void Insert(int value);
}

If the caller forgets to call AddSlots(), undefined behavior might be triggered when Insert() is called. The interface pushes complexity onto the caller, exposing the caller to implementation details.

Since maintaining the slots is not relevant to the caller-visible behaviors of the class, don't expose them in the interface; make it impossible to trigger undefined behavior by adding slots as needed in Insert().
@Test public void class Vector {
explicit Vector(int num_slots);
// Adds a new element at the end of the vector. If necessary,
// allocates new slots to ensure that there is enough storage
// for the new value.
void Insert(int value);
}


Contracts enforced by the compiler are usually better than contracts enforced by runtime checks, or worse, documentation-only contracts that rely on callers to do the right thing.
Here are other examples that could signal that an interface is easy to misuse:
  • Requiring callers to call an initialization function (alternative: expose factory methods that return your object fully initialized).
  • Requiring callers to perform custom cleanup (alternative: use language-specific constructs that ensure automated cleanup when your object goes out of scope).
  • Allowing code paths that create objects without required parameters (e.g. a user without an ID).
  • Allowing parameters for which only some values are valid, especially if it is possible to use a more appropriate type (e.g. prefer Duration timeout instead of int timeout_in_millis).
It is not always practical to have a foolproof interface. In certain cases, relying on static analysis or documentation is necessary since some requirements are impossible to express in an interface (e.g. that a callback function needs to be thread-safe).

Don’t enforce what you don’t need to enforce - avoid code that is too defensive. For example, extensive validation of function parameters can increase complexity and reduce performance.