Categories
Android Architecture Kotlin testing

Logging in a Java library

It can be useful to emit logs in a library. When doing so, one needs to consider when to emit, how to filter and who is responsible for printing/handling the logs. Correct logging should also be tested.

When to log

There are different reasons to emit a message, for instance on important events, undefined behaviour or different levels of debug events.

Any potentially useful message should be emitted. However, in order to not clutter the terminal, output should be refined.

Filtering the logs

A library logging level should be configurable according to user preference:

/**  Possible logging levels. */
public enum Level {
    /**  No log messages */
    OFF(0),
    /** Informational messages and errors only */
    INFO(1),
    /* Debug messages */
    DEBUG(2),
    /* All messages, including fine traces */
    ALL(3);
}

It is expected that important and error messages are emitted by default, so Level.INFO should be the default setting.

However, if finer traces are required, the filter could be set to DEBUG:

Library.loggingLevel = Level.DEBUG

The library should then filter the messages according to level:

static void logDebug(String message) {
    if (loggingLevel >= Level.DEBUG) {
        loggingFramework.logDebug(message);
    }
}

Example of filtering implementation in hmkit-android library.

Choosing a logging framework

The user could be using any java subsystem, or maybe emitting the messages to a web service. For this reason, the library should never output to System.out.println or android.util.Log. Instead, it should be an interface from where the logs are emitted, and it should be up to the user to choose where the messages are output in the end.

A popular logging facade is slf4j. api part of it should be included in our library:

implementation 'org.slf4j:slf4j-api:1.7.25'

Then logs can be emitted through a Logger instance:

// sample informational log
logger = LoggerFactory.getLogger(Library.class);
...
logger.info("Library initialised")

Our library user could then continue using his favourite logging framework and add an slf4j binding to see the messages. For instance, a Timber binding:

implementation 'at.favre.lib:slf4j-timber:1.0.0'

Testing the emitted logs

To verify the emitted logs, a test setup is necessary. Mockk can be used to mock the slf4j and verify calls to it’s Logger.

For this to work, each test could inherit from BaseTest, which initialises the mock:

lateinit var mockLogger:org.slf4j.Logger

@BeforeEach
fun before() {
  mockLogger = mockk()
  mockkStatic(MyLoggerFactory::class)
  every { MyLoggerFactory.getLogger() } returns mockLogger

  every { mockLogger.warn(allAny()) } just Runs
  every { mockLogger.debug(allAny()) } just Runs
  every { mockLogger.error(allAny()) } just Runs
}

This class could also contain convenience lambda methods to assert the emitted logs:

fun debugLogExpected(runnable: Runnable, count: Int = 1) {
    runnable.run()
    verify(exactly = count) { mockLogger.debug(allAny()) }
}

From the derived class’s test method, an assertion can be then written about the log message:

@Test fun invalidStartControlControlModeThrows() {
    debugLogExpected { 
        val action = Library.resolve()
    }
}

This test will succeed if one debug message is emitted to the slf4j interface.

Please see auto-api-java as an example of using this pattern.

Conclusion

Logging in a library can be very beneficial. The maintainer should however be aware of the library user’s perspective, and filter too precise logs by default. Log printing should also be left to the user, and correct log emittance should be tested with unit tests.

Categories
Android Apps Architecture testing

Part 2: Testing with MockK and Koin

One of the best things about MVVM is the use of separation of concerns principle which by design enables testing of each component in isolation. View, ViewModel and Model are all separated and thus easily testable.

When thinking of testing, then unit testing comes to mind first and for that mocking of dependencies is required.

Mockito vs MockK

After investigating Google’s demo project, it seemed Mockito was the way to go with mocking and verifying tests.

What I soon realised, was that it wasn’t the most convenient library to use in Kotlin, and I also had problems with just getting it to work. I then discovered MockK, written in Kotlin, which was easy to setup and thus made a perfect choice for my project.

Consider mocking a network response:

Mockito

val call = successCall(contributors)
`when`(service.getContributors()).thenReturn(call)

MockK

val call = successCall(contributors)
every { service.getContributors() } returns call

I choose MockK’s every / lambda style over Mockito’s `when`.

The only thing missing from MockK is verifying constructor calls, for which there is a Github issue. Because of this I needed to refactor my code and inject the dependencies, instead of constructing them.

MockK setup

Separate libraries are required for unit and instrumentation tests:

testImplementation "io.mockk:mockk:$version"
androidTestImplementation "io.mockk:mockk-android:$version"

This is enough to start mocking dependencies in Unit tests. For instance, mocking a network client:

val client = mockk<RepoClient>()

For instrumentation tests, you need to launch the real activity and thus need a separate Test App class and mocked Koin modules. Read about this in Instrumentation section below👇🏽👇🏽

Testing the Repository

All the classes can be covered with unit tests. In the @Before block, you should create the class with mocked dependencies:

@Before
fun before() {
    client = mockk<RepoClient>()
    repository = RepoRepository(client, more mocks..)
}

Then, in your test, you can mock answers from your dependencies and verify expected Repository behavior.

// mock the repository observer
val observer = mockk<Observer<Resource<List<Repo>>>>()
// call getRepos() and observe the response
repository.getRepos().observeForever(observer)
// verify repos are fetched from network
verify { client.getRepos() }
// simulate that network data was stored to db
dbData.postValue(repos)
// verify getRepos() observer was called
verify { observer.onChanged(Resource.success(repos)) }

With this style you can write unit tests for all of your classes.

UI Instrumentation tests

Instrumentation tests are used to verify what is visible to the user. The app will launch with mocked ViewModel and the tests can then verify the UI state.

Setup

For instrumentation tests, you have to set up a custom Test App and its companion Test Runner which is then used to run the tests. Needless to say the setup is pretty complicated but the tests are worth it after the initial hurdle.

@Before and @After

Before the UI test, the ViewModel should be mocked and its responses set. Then the tested activity/fragment should be launched.

@Before
fun before() {
    // mock the ViewModel
    loginRequest = MutableLiveData()
    loginViewModel = mockk(relaxed = true)
    every { loginViewModel.user } returns loginRequest

    module = module(true, true) {
        single { loginViewModel }
        // mock other dependencies
        single { mockk<MainViewModel>(relaxed = true) }
        ... etc
    }

    loadKoinModules(module)
    // launch the activity
    scenario = launchActivity()
}

After the test the activity should be closed and Koin modules unloaded so the next test can start with cleared objects.

@After
fun after() {
    scenario.close()
    unloadKoinModules(module)
}

The test

Then, as in Unit tests, you can mock updates from ViewModel and verify expected View behavior. You can also simulate input from the view.

For instance, verifying a Toast message after invalid login:

// input wrong credentials
inputCredentials("wrong", "wrong")

// click the login button
onView(withId(R.id.loginButton)).perform(click())

// verify ViewModel's login is called
verify { loginViewModel.login(any(), any()) }

// simulate error response
loginRequest.postValue(Resource.error(getString(R.string.invalid_credentials), null))

// assert error toast shown
onView(withText(R.string.invalid_credentials))...

Similarly to this, all of the views can be tested.

Conclusion

Although setup and complexity of Android tests could be improved, it is essential for delivering a quality application.

I can say from my experience that numerous times seemingly irrelevant tests have failed after writing new code. These failed tests, if not caught, would have meant bugs in production.

Reference

Please have a look at the unit and instrumentation tests in the sample project’s source code.