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quickcheck-dynamic is a library jointly developed by Quviq and IOG, whose purpose is to leverage QuickCheck to test stateful programs against a Model. In other words, it's a Model-Based Testing tool. This article wants to be a gentle introduction to the use of quickcheck-dynamic for Model-Based Testing. It describes the overall approach, how the library works, and how it's being applied within IOG to improve the reach of our testing efforts.


Testing stateful or rather effectful code using QuickCheck is not new: In particular, techniques to test Monadic code with QuickCheck have been introduced in Claessen & Hughes, 2002. quickcheck-dynamic is based on a state-machine approach originally implemented by Quviq in closed-source Erlang version of QuickCheck and put to use to test various systems as explained in John Hughes' paper.

IOG already has had experience with state-machine based testing in the Consensus Storage layer using quickcheck-state-machine library, but this was not widespread practice across the various teams.

When IOG started working on the Plutus Smart Contracts language and application framework, Quviq's consultancy was sought after to help test the platform and build tools for future Smart Contract implementors. This effort lead to the development of a custom library for testing smart contracts based on quickcheck-dynamic's state-machine model and dynamic logic language adapted from Erlang QuickCheck.

As quickcheck-dynamic matured, it attracted interest from other teams willing to invest into model-based testing and reuse existing effort. This finally lead to publication of the library as an independent package on Hackage, independently of the Plutus framework, in the hope it will be useful to a wider audience.

Use Casesโ€‹

Example: Thread Registryโ€‹

The library comes with a complete example defining a model and reference implementation for a Thread Registry. It's inspired by a similar example in Erlang from a couple of papers:

The tests here use IOG's concurrent execution simulator library io-sim to speed-up testing.

Lockstep Testingโ€‹

Edsko de Vries wrote a nice blog post to compare quickcheck-dynamic with quickcheck-state-machine, another library to write model-based tests on top of QuickCheck. This blog post introduces quickcheck-lockstep which provides lockstep-style testing on top of quickcheck-dynamic.

Lockstep-style testing is a special case of Model-Based Testing whereby what's tested at each execution step of a test sequence is the equivalence up to some observability function, of the return values expected by the Model and the one provided by the Implementation. In other words, if we consider each step in the state-machine as a transition that, given some input and a starting state, produces some output and possibly a new state, then lockstep-style testing checks equivalence of the output traces from the model and the implementation.

The quickcheck-lockstep library provides generic implementations for most of the methods of the StateModel typeclass and dedicated type-classes to relate the Model and the Implementation.

Plutus Contractsโ€‹

Within IOG, the quickcheck-dynamic testing approach was initially applied to provide a testing framework for Smart Contracts developers within the Plutus Application Backend. The Plutus documentation contains a detailed tutorial on how to model smart contracts and tests them using underlying Emulator.

While the Contract Model is a specialised library dedicated to Smart contracts modeling and testing, the underlying principles are similar:

  • Define a ContractModel with some state and actions representing the system behaviour,
  • Define a perform function that describes how the model's actions translate to real world calls to a Contract's endpoints,
  • then test the contracts implementation using properties written in the Dynamic Logic language provided by the framework.

On top of quickcheck-dynamic, the Contract Model provides machinery to simplify definition of a model in the case of Plutus smart contracts and running tests against a set of _Wallets. More importantly, it predefines critical properties that all Smart Contracts should validate, like the No Locked Funds or the more subtle Double Satisfaction property. Smart contracts are somewhat infamous for being subject to subtle coding errors leading into loopholes which attackers can abuse to steal currencies from innocent users, because of the intrisic complexity of programming in a highly distributed and asynchronous model.

The Model-Based Testing approach supported by quickcheck-dynamic gives developers the tools to explore the state space in a much more systematic way.


Hydra is a so-called Layer 2 solution for Cardano that aims at increasing the throughput and latency of Cardano transactions by moving most of the traffic out of the main chain (Layer 1) and into smaller networks. The Hydra Head protocol is described in great details in a research paper.

At its core, Hydra is a state machine whose transitions are Layer 1 transactions, as depicted in the following picture:

Hydra State Machine

While the overall state machine appears relatively simple on the surface, the actual protocol is actually quite complex, involving cryptographic signatures and Plutus Smart Contracts to ensure the safety of the protocol. This safety is formally expressed in the paper as properties that are proven against an Adversary Environment whose capabilities are well-defined.

In order to guarantee the implementation provides those safety properties, the Hydra team has developed a diversified palette of testing techniques, including the use of quickcheck-dynamic. While the careful Test-Driven Development approach taken gives reasonable confidence most use cases and issues are covered, hopes are high that such a model is able to explore more corner cases and reveal subtle issues in the protocol implementation.

What was sought after is to be able to define and test Hydra Head security properties against the real implementation. As a first example the team tackled to get a feel of how quickcheck-dynamic could used, here one of the properties from the original paper is stated:

โ€ข Conflict-Free Liveness (Head):

In presence of a network adversary, a conflict-free execution satisfies the following condition: For any transaction tx input via (new,tx), tx โˆˆ T iโˆˆ[n] Ci eventually holds.

This property and similar ones are encoded as a Dynamic Logic expressions, and a suitable Model of a Hydra network is defined as an instance of StateModel from which test sequences representing User actions are generated.

Hydra is a distributed system where nodes are interconnected through a network layer, and each node needs to be connected to a cardano-node in order to preserve the security of the protocol. While testing an actual "cluster" of hydra and cardano nodes is definitely possible, and certainly desirable at some point in order to strengthen confidence in the whole system, it would also be very slow: Spinning up processes, establishing network connections, producing blocks on a chain, all take seconds if not minutes which makes any signficant exploration of the model state space practically infeasible.

Generated test traces are run within the IOSim monad which allows testing 100s of traces within seconds.

Of course, this means we won't be using real TCP/IP networking stack nor connection to a real Cardano node and chain to create a Hydra network, but this is actually not a liability but an asset. By mocking the interfaces Hydra nodes use to communicate with other nodes and Cardano network, we are able to control the behaviour of the communication layer and inject faults representing some Adversary: Reordering or dropping messages, tampering the data, delaying requests...


We'll use the latter example to illustrate quickcheck-dynamic's principles and give the reader an intuition on the four steps that need to be defined in order to use it: Defining a test Model, stating how the model relates to the Implementation, expressing Properties and, last but not least, checking properties.

Defining a Modelโ€‹

In quickcheck-dynamic, a Model is some type, a representation of the expected state of the system-under-test, for which there exists an instance of the StateModel class which sets the building blocks needed to generate and validate test sequences.

In the case of Hydra, the Model is a IdealWorld data type that control the Head parties and maintains a GlobalState which reflects the expected Head state:

data IdealWorld = IdealWorld
{ hydraParties :: [(SigningKey HydraKey, CardanoSigningKey)]
, hydraState :: GlobalState

We won't bother the reader with details of the GlobalState which basically encode the states as depicted in the state-machine picture hereabove in the form of an Algebraic Data-Type.

As the old saying from Alfred Korzybski goes, "The map is not the territory", hence to be useful a Model should abstract away irrelevant details for the purpose of testing. Furthermore, it's perfectly fine to use different models to test different aspects of the same implementation.

While the real Hydra layer two ledger does support a myriad of possible Cardano transactions, our model at hand is simpler and only uses Two Party Payment transactions:

data Payment = Payment
{ from :: CardanoSigningKey
, to :: CardanoSigningKey
, value :: Value

The first important part of the StateModel instance to define is the type of Action that are meaningful for the given Model and that can also be executed against the concrete implementation. The Action associated data-type is a GADT which allows the model to represent the type of observable output that can be produced by the implementation and which can be part of the model's validation logic.

The Hydra model needs to represent both on-chain and off-chain actions as the properties required from Hydra relates the two. The Action data-type represent user-facing commands and observations that can be made on the state of the system (please note at the time of writing this, the model is incomplete):

  data Action IdealWorld a where
Seed :: {seedKeys :: [(SigningKey HydraKey, CardanoSigningKey)]} -> Action IdealWorld ()
Init :: Party -> ContestationPeriod -> Action IdealWorld ()
Commit :: Party -> UTxOType Payment -> Action IdealWorld ActualCommitted
Abort :: Party -> Action IdealWorld ()
NewTx :: Party -> Payment -> Action IdealWorld ()
Wait :: DiffTime -> Action IdealWorld ()
ObserveConfirmedTx :: Payment -> Action IdealWorld ()

Then one needs to define:

  • An initialState,
  • How to generate arbitraryActions which will be used to produce sequences (or traces) of Actions to execute, depending on the current state,
  • A precondition function ensuring some Action is valid in some state. This function may seem redundant with the generator but is actually important when shrinking a failing test sequences: The execution engine will ensure the reduced sequence is still valid with respect to the model,
  • A nextState (transition) function that evolves the model state according to the Actions,
  • Auxiliary function actionName to providea human-readable representation of actions.

The reader is invited to check the Haddock documentation of the library for further details.

Exercising Implementationโ€‹

A Model alone is somewhat useless if we don't provide a way to relate it to the actual implementation of the system-under-test. quickcheck-dynamic provides the RunModel typeclass for this purpose. The most important function to define is perform which defines how StateModel's Action should be executed against the implementation within some monadic context m. Having the actual execution Monad m be a parameter of the RunModel gives more flexibility to the implementor which is not tied to IO for example.

In the case of Hydra, the perform function is defined as:

  perform ::
IdealWorld ->
Action IdealWorld a ->
LookUp (StateT (Nodes m) m ->
StateT (Nodes m) m a
perform st command _ = do
case command of
Seed{seedKeys} ->
seedWorld seedKeys
Commit party utxo ->
performCommit (snd <$> hydraParties st) party utxo

The actual monad used is a classical State monad whose state maps a Party to the corresponding client connection to the Hydra node:

data Nodes m = Nodes
{ nodes :: Map.Map Party (TestHydraNode Tx m)
, logger :: Tracer m (HydraNodeLog Tx)

The m parameter is here kept somewhat unconstrained in order to make it possible to run properties within the IOSim monad for faster tests execution. Also note the presence of the logger field which is used to capture logging output from all the nodes: Should an error happen or a postcondition fail, we can dump the log of each node which is invaluable to troubleshoot such failures. In general, testing systems in a black-box way emphasises the importance of good logging to provide as much context as possible should issues arise, and using quickcheck-dynamic makes no exception.

Expressing Properties with Dynamic Logicโ€‹

Once we have a StateModel we can express interesting properties to check against our RunModel. Dynamic Logic allows one to express properties through monadic expressions relating actions, states and logic predicates.

Dynamic Logic is a form of modal logic, similar to temporal logic, but whose modalities are the actions (or programs) themselves. One can intertwine programs and logic predicates to specify the behaviour of the former when executing some statements and actually Dynamic Logic evolved from Hoare's Triples.

Here is the dynamic logic reformulation of the previously stated Hydra property which has been kept as close as possible to the original English statement:

conflictFreeLiveness :: DL IdealWorld ()
conflictFreeLiveness = do
getModelStateDL >>= \case
st@IdealWorld{hydraState = Open{}} -> do
(party, payment) <- forAllQ (nonConflictingTx st)
action $ Model.NewTx party payment
eventually (ObserveConfirmedTx payment)
_ -> pass
nonConflictingTx st = withGenQ (genPayment st) (const [])
eventually a = action (Wait 10) >> action a

Note that in order to define this property we have introduced two "pseudo-actions" in the Model, Wait and ObserveConfirmedTx: Those Actions have no effect on the model itself, the former being used to introduce some delay in the context of distributed and asynchronous execution, and the latter serving the purpose of observing the current state of the SUT. An alternative formulation would have been to make ObserveConfirmedTx return the set of confirmed transactions and then express the condition as a logic predicate within the conflictFreeLiveness property's body.

Checking Propertiesโ€‹

The last step in putting quickcheck-dynamic at work is to be able to connect the StateModel, the RunModel, and the DynamicLogic expression and turn those into a QuickCheck Property which can then be checked using the standard testing framework.

quickcheck-dynamic provides 2 functions for that purpose. The forAllDL_ function (actually more a family of functions) will leverage DL formulae to generate sequences of Actions:

prop_checkConflictFreeLiveness :: Property
prop_checkConflictFreeLiveness =
forAllDL_ conflictFreeLiveness prop_HydraModel

The runActions function will execute the generated trace against the RunModel.

prop_HydraModel :: Actions IdealWorld -> Property
prop_HydraModel actions = property $
runIOSimProp $ do
_ <- runActions runIt actions
assert True

In this particular instance from Hydra, we need some additional machinery (the runIOSimProp function) to handle the execution of some monadic PropertyM into IOSim monad, turning it into a Property.

When run and successful, this Property generates the following output:

  check conflict-free liveness
+++ OK, passed 100 tests.

Actions (1334 in total):
49.93% NewTx
25.86% Commit
7.50% Seed
7.42% Init
4.80% Abort
2.25% ObserveConfirmedTx
2.25% Wait

Transitions (1334 in total):
54.42% Open -> Open
23.61% Initial -> Initial
7.50% Start -> Idle
7.42% Idle -> Initial
4.80% Initial -> Final
2.25% Initial -> Open

By default, runActions decorate the QuickCheck output tabulating the executed Action. And thanks to the monitoring helper provided by the RunModel, this example also tabulates the executed transitions between each of the possible values for GlobalState. These pieces of information are important to assess the "quality" of the model: We want to make sure its generators and the properties execution covers all interesting parts of the Model, hence exercise all relevant parts of the implementation. Please note that, as we mentioned before, the Hydra model is still a work in progress hence the reason why there's no Open -> Final transition!


This articled introduced quickcheck-dynamic, a novel Model-Based Testing library initially developed by Quviq for testing Plutus Smart Contracts and which has recently been open-sourced by IOG. I have tried to convey to the user a sense of the Whys, Whats and Hows questions this library answers through various examples and a high-level walkthrough of the steps needed to use this library for testing an implementation.

Model-Based Testing is a powerful tool that simultaneously addresses both aspects of Customer-facing tests as Brian Marick puts it in his famous Agile Testing Quadrant popularised by Lisa Crispin and Janet Gregory through their Agile Testing books: Supporting the team by providing a reference model to build against, and Critiquing the product through the unique state-space exploration QuickCheck provides, possibly uncovering corner cases and blind spots in either the specification or the implementation.

The library is still evolving towards better developer experience and flexibility but it's already in a state that makes it possible to test something as significant as a Hydra network. And while it may appear somewhat involved when compared to more traditional forms of writing Functional tests, I hope I have demonstrated quickcheck-dynamic lowers the barrier to entry associated with most MBT tools.