Unit Testing

Writing tests with Déjà Fu is a little different to traditional unit testing, as your test case may have multiple results. A "test" is a combination of your code, and a predicate which says something about the set of allowed results.

Most tests will look something like this:

dejafu "Assert the thing holds" myPredicate myAction

The dejafu function comes from Test.DejaFu. Another useful function is dejafuWithSettings (see Advanced Usage).

Actions

An action is just something with the type MonadConc m => m a, or (MonadConc m, MonadIO m) => m a for some a that your chosen predicate can deal with.

For example, some users on Reddit found a couple of apparent bugs in the auto-update package a while ago (thread here). As the package is simple and self-contained, I translated it to the MonadConc abstraction and wrote a couple of tests to replicate the bugs. Here they are:

deadlocks :: MonadConc m => m ()
deadlocks = do
  auto <- mkAutoUpdate defaultUpdateSettings
  auto

nondeterministic :: forall m. MonadConc m => m Int
nondeterministic = do
  var <- newIORef 0
  let settings = (defaultUpdateSettings :: UpdateSettings m ())
        { updateAction = atomicModifyIORef var (\x -> (x+1, x)) }
  auto <- mkAutoUpdate settings
  auto
  auto

These actions action could be tested with autocheck, and the issues would be revealed. The use of ScopedTypeVariables in the second is an unfortunate example of what can happen when everything becomes more polymorphic. But other than that, note how there is no special mention of Déjà Fu in the actions: it's just normal concurrent Haskell, simply written against a different interface.

The modified package is included in the test suite, if you want to see the full code.

Note

The predicates in dejafu-tests are a little confusing, as they're the opposite of what you would normally write! These predicates are checking that the bug is found, not that the code is correct.

If the RTS supports bound threads (the -threaded flag was passed to GHC when linking), then the main thread of an action given to Déjà Fu will be bound, and further bound threads can be forked with the forkOS functions. If not, then attempting to fork a bound thread will raise an error.

Conditions

When a concurrent program of type MonadConc m => m a is executed, it may produce a value of type a, or it may experience a condition such as deadlock.

A condition does not necessarily cause your test to fail. It's important to be aware of what exactly your test is testing, to avoid drawing the wrong conclusions from a passing (or failing) test.

Setup and Teardown

Because dejafu drives the execution of the program under test, there are some tricks available to you which are not possible using normal concurrent Haskell.

If your test does some set-up work which is required for your test to work, but which is not the actual thing you are testing, you can define that as a setup action:

withSetup
  :: Program Basic n x
  -- ^ Setup action
  -> (x -> Program Basic n a)
  -- ^ Main program
  -> Program (WithSetup x) n a

dejafu will save the state at the end of the setup action, and efficiently restore that state in subsequent runs of the same test with a different schedule. This can be much more efficient than dejafu running the setup action normally every single time.

If you want to examine some state you created in your setup action even if your actual test case deadlocks or something, you can define a teardown action:

withSetupAndTeardown
  :: Program Basic n x
  -- ^ Setup action
  -> (x -> Either Condition y -> Program Basic n a)
  -- ^ Teardown action
  -> (x -> Program Basic n y)
  -- ^ Main program
  -> Program (WithSetupAndTeardown x y) n a

The teardown action is always executed.

Finally, if you want to ensure that some invariant holds over some shared state, you can define invariants in the setup action, which are checked atomically during the main action:

-- slightly contrived example
let setup = do
      var <- newEmptyMVar
      registerInvariant $ do
        value <- inspectMVar var
        when (x == Just 1) (throwM Overflow)
      pure var
in withSetup setup $ \var -> do
     fork $ putMVar var 0
     fork $ putMVar var 1
     tryReadMVar var

If the main action violates the invariant, it is terminated with an InvariantFailure condition, and any teardown action is run.

Predicates

There are a few predicates built in, and some helpers to define your own.


`abortsNever`	checks that the computation never aborts
`abortsAlways`	checks that the computation always aborts
`abortsSometimes`	checks that the computation aborts at least once

An abort is where the scheduler chooses to terminate execution early. If you see it, it probably means that a test didn't terminate before it hit the execution length limit. Aborts are hidden unless you use explicitly enable them, see (see Advanced Usage).


`deadlocksNever`	checks that the computation never deadlocks
`deadlocksAlways`	checks that the computation always deadlocks
`deadlocksSometimes`	checks that the computation deadlocks at least once

Deadlocking is where every thread becomes blocked. This can be, for example, if every thread is trying to read from an MVar that has been emptied.


`exceptionsNever`	checks that the main thread is never killed by an exception
`exceptionsAlways`	checks that the main thread is always killed by an exception
`exceptionsSometimes`	checks that the main thread is killed by an exception at least once

An uncaught exception in the main thread kills the process. These can be synchronous (thrown in the main thread) or asynchronous (thrown to it from a different thread).


`alwaysSame`	checks that the computation is deterministic and always produces a value
`alwaysSameOn f`	is like `alwaysSame`, but transforms the results with `f` first
`alwaysSameBy f`	is like `alwaysSame`, but uses `f` instead of `(==)` to compare
`notAlwaysSame`	checks that the computation is nondeterministic
`notAlwaysSameOn f`	is like `notAlwaysSame`, but transforms the results with `f` first
`notAlwaysSameBy f`	is like `notAlwaysSame`, but uses `f` instead of `(==)` to compare

Checking for determinism will also find nondeterministic failures: deadlocking (for instance) is still a result of a test!


`alwaysTrue p`	checks that `p` is true for every result
`somewhereTrue p`	checks that `p` is true for at least one result

These can be used to check custom predicates. For example, you might want all your results to be less than five.


`gives xs`	checks that the set of results is exactly `xs` (which may include conditions)
`gives' xs`	checks that the set of results is exactly `xs` (which may not include conditions)

These let you say exactly what you want the results to be. Your test will fail if it has any extra results, or misses a result.

You can check multiple predicates against the same collection of results using the dejafus and dejafusWithSettings functions. These avoid recomputing the results, and so may be faster than multiple dejafu / dejafuWithSettings calls.

Using HUnit and Tasty

By itself, Déjà Fu has no framework in place for named test groups and parallel execution or anything like that. It does one thing and does it well, which is running test cases for concurrent programs. HUnit and tasty integration is provided to get more of the features you'd expect from a testing framework.

The integration is provided by the hunit-dejafu and tasty-dejafu packages.

There's a simple naming convention used: the Test.DejaFu function dejafuFoo is wrapped in the appropriate way and exposed as testDejafuFoo from Test.HUnit.DejaFu and Test.Tasty.DejaFu.

Our example from the start becomes:

testDejafu "Assert the thing holds" myPredicate myAction

The autocheck function is exposed as testAuto.