DOKK Library
Verifying Replicated Data Types with Typeclass Refinements
in Liquid Haskell

YIYUN LIU, University of Maryland, College Park, USA
JAMES PARKER, University of Maryland, College Park, USA
PATRICK REDMOND, University of California, Santa Cruz, USA
LINDSEY KUPER, University of California, Santa Cruz, USA
MICHAEL HICKS, University of Maryland, College Park, USA
NIKI VAZOU, IMDEA Software Institute, Madrid, Spain
This paper presents an extension to Liquid Haskell that facilitates stating and semi-automatically proving
properties of typeclasses. Liquid Haskell augments Haskell with refinement typesÐour work allows such types
to be attached to typeclass method declarations, and ensures that instance implementations respect these types.
The engineering of this extension is a modular interaction between GHC, the Glasgow Haskell Compiler, and
Liquid Haskell’s core proof infrastructure. The design sheds light on the interplay between modular proofs
and typeclass resolution, which in Haskell is coherent by default (meaning that resolution always selects the
same implementation for a particular instantiating type), but in other dependently typed languages is not.
   We demonstrate the utility of our extension by using Liquid Haskell to modularly verify that 34 instances
satisfy the laws of five standard typeclasses.
   More substantially, we implement a framework for programming distributed applications based on replicated
data types (RDTs). We define a typeclass whose Liquid Haskell type captures the mathematical properties
RDTs should satisfy; prove in Liquid Haskell that these properties are sufficient to ensure that replicas’
states converge despite out-of-order update delivery; implement (and prove correct) several instances of our
RDT typeclass; and use them to build two realistic applications, a multi-user calendar event planner and a
collaborative text editor.
CCS Concepts: • Software and its engineering → Formal software verification; Software verification;
• Theory of computation → Distributed algorithms.
Additional Key Words and Phrases: replicated data types, CRDTs, typeclasses, refinement types, Liquid Haskell
ACM Reference Format:
Yiyun Liu, James Parker, Patrick Redmond, Lindsey Kuper, Michael Hicks, and Niki Vazou. 2020. Verifying
Replicated Data Types with Typeclass Refinements in Liquid Haskell. Proc. ACM Program. Lang. 4, OOPSLA,
Article 216 (November 2020), 30 pages.

Liquid Haskell [Rondon et al. 2008; Vazou et al. 2014] is an extension to Haskell that enables formally
verifying logical properties of Haskell programs. Its basis for doing so is refinement types, which
augment standard Haskell types with predicates that restrict the set of valid values [Rushby et al.
1998; Xi and Pfenning 1998; Constable and Smith 1987]; these predicates are checked automatically
Authors’ addresses: Yiyun Liu, University of Maryland, College Park, USA; James Parker, University of Maryland, College
Park, USA; Patrick Redmond, University of California, Santa Cruz, USA; Lindsey Kuper, University of California, Santa Cruz,
USA; Michael Hicks, University of Maryland, College Park, USA; Niki Vazou, IMDEA Software Institute, Madrid, Spain.

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216:2                        Yiyun Liu, James Parker, Patrick Redmond, Lindsey Kuper, Michael Hicks, and Niki Vazou

by an SMT solver. Liquid Haskell uses a mechanism called refinement reflection [Vazou et al. 2017]
to lift the ability to state and check single refinements of individual functions to state and prove
general properties of entire codebases [Vazou et al. 2018].
   Liquid Haskell’s featuresÐbeing based on a widely used, industrial-strength programming
language and having built-in support for proof automationÐmake it appealing as a platform
for real-world, verified software development. Indeed, we were motivated to use it to build a
platform for distributed computing applications employing data replication to increase reliability
and availability. These applications are hard to get right, and we hoped Liquid Haskell’s formal
verification capabilities could help.
   Unfortunately, there was a big stumbling block: Liquid Haskell cannot verify properties of
typeclasses [Wadler and Blott 1989], which are used extensively throughout the Haskell ecosystem.
Typeclasses in Haskell are similar to interfaces in Java or traits in Rust. A typeclass definition
specifies a collection of method signatures that any type that is an instance of the typeclass must
implement. For example, the Ord typeclass from Haskell’s standard library declares that its instances
a must have a method (<=) of type a → a → bool ; numbers, strings, booleans, and many other
types are instances of Ord . The standard sort function can only sort lists of types that are Ord
instances, since it needs a comparison function; this requirement is expressed as a constraint on
sort ’s type, Ord a ⇒ [a] → [a].

   Typeclass refinements for Liquid Haskell. The primary contribution of this paper is an extension to
Liquid Haskell that supports stating and proving properties of typeclasses (Section 2). While it was
previously possible in Liquid Haskell to prove properties of individual instances of a typeclass, it was
not possible to give refinement types to a typeclass definition’s methods. As such, Liquid Haskell
code and proofs could not then modularly use those types when invoking methods from functions
whose arguments (like sort ) have a typeclass constraint. Given the ubiquity of typeclasses in
Haskell code, the ability to do this is key to being able to verify interesting properties of real-world
Haskell applications.
   Implementing typeclass refinements in Liquid Haskell was not straightforward, which perhaps
explains their long absence despite obvious benefits. Our implementation works by verifying
properties not of Haskell source code, but rather of Core expressions, which are the intermediate
representation produced by the Glasgow Haskell Compiler (GHC) [GHC 2020], the de facto Haskell
standard. Doing so leverages functionality that GHC already provides (e.g., typechecking and
elaboration) and allows Liquid Haskell to evolve semi-independently from GHC, since Core’s
definition is relatively stable. But there is a problem: typeclasses are not Core expressionsÐduring
elaboration, GHC translates them to dictionaries, which are basically records of functions. Code
that defines a typeclass instance is translated to create a dictionary, and code that expresses a
typeclass constraint is translated to use a dictionary; e.g., sort will be translated to be passed
an Ord dictionary, from which it invokes the (<=) method. To maintain the current separation
between Liquid Haskell and GHC, our implementation (Section 3) transliterates typeclass methods’
refinement types to checked invariants over dictionaries, so refinement types on typeclasses are
verified when dictionaries are created, and those types can be used by client code. To do this
modularly we had to expand the way Liquid Haskell interacts with GHC.
   While Liquid Haskell is not the first proof system with typeclass supportÐCoq, Isabelle, Idris, F⋆ ,
Agda, and Lean have typeclasses or something like themÐour approach represents an interesting
point in the design space (see Section 5.3). A key element of a typeclass system is the resolution
procedure, which selects an instance for a given type. For example, the code sort [3,1,2] requires
Haskell to resolve an instance of Ord at type Int so it can pass in the required Ord Int dictionary.
Our support for typeclasses in Liquid Haskell reuses Haskell’s typeclass resolution procedure.

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This procedure is coherent by default [Bottu et al. 2019], meaning that it always chooses the
same typeclass instance for a given type (e.g., there cannot be two different implementations of
Ord Int ). The assumption of coherence can often be useful in proofs, especially when there are
diamonds in the class inheritance graph. The above-listed languages do not ensure coherence,
which can be awkward for the proof engineer; typeclass resolution may even fail to terminate.
On the other hand, coherence is only the default in Haskell, not the rule: incoherent resolution
(and overlapping instances) are sometimes useful, and enabled by pragma. As such, it would be
unsound to axiomatize the assumption of coherence. Our implementation introduces a checked
invariant during elaboration to express coherence, which Liquid Haskell proves automatically. This
approach is novel and balances the extremes of accommodating full coherence or none. That said,
reusing Haskell’s resolution precludes different instances that refine the same base type,.g., an Ord
instance for positive integers that is distinct from one for all integers. Fortunately, users can easily
work around this limitation by defining instances on refined newtype wrappers, as is often done in
Haskell anyway to avoid coherence problems (see Section 2.2).
   Case study: Verifying standard typeclass laws. As a simple test of the utility of typeclass refine-
ments, we carried out a small case study: We used Liquid Haskell to verify that instances of standard
Haskell typeclasses satisfy the expected typeclass laws (Section 2.3). Significant prior work has
focused on this application specifically, employing a variety of techniques, including random testing,
term rewriting, contract verification, and conversion to Coq (see Section 5.2). Liquid Haskell type-
class refinements offer a natural, general-purpose approach. In particular, laws can be expressed as
refinements to methods of a subclass of the target typeclass, and proofs of them are carried out in a
subclass of each implementation of that target typeclass. This approach permits proofs of existing
Haskell code without requiring that code be directly modified or annotated. We demonstrate this
for several standard typeclasses, including Semigroup , Monoid , Functor , Applicative , and Monad ,
proving 34 instantiations satisfy their laws, in all (Section 2.3). Mostly, we find that the proofs are
short (just a couple of lines), thanks to Liquid Haskell’s SMT automation, and proof checking time
is fast (typically a few seconds).
   Case study: A platform for programming with verified replicated data types. Spurred by the success
of this case study, we set out to build a platform for programming distributed applications based on
replicated data types (RDTs) (Section 4). Data replication is ubiquitous in distributed systems to guard
against machine failures and keep data physically close to clients who need it, but it introduces
the problem of keeping replicas consistent with one another in the face of network partitions and
unpredictable message latency. RDTs [Shapiro et al. 2011b,a; Roh et al. 2011] are data structures
whose operations must satisfy certain mathematical properties that can be leveraged to ensure
strong convergence [Shapiro et al. 2011b], meaning that replicas are guaranteed to have equivalent
state given that they have received and applied the same unordered set of update operations.
   Liquid Haskell typeclasses provide a natural, modular, and elegant way to implement and verify
RDTs. We define a typeclass VRDT with a refinement type that captures the necessary properties, and
we use Liquid Haskell to prove that those properties hold for a several primitive instances. We also
define several larger VRDT instances by modularly combining both the code and proofs of smaller
ones. We state and prove, in Liquid Haskell, the strong convergence property that VRDT instances
enjoy. Pleasantly, our approach generalizes and relaxes the typical assumption of causal message
delivery. Our VRDT instances are sufficiently expressive that with them we were able to build a
shared calendar event planner and a collaborative text editor. Each application is implemented
using a few hundred lines of Haskell code (Section 4.7).
   Although there exists previous work on mechanized verification of many RDTs (Section 5.4),
ours are, we believe, the first immediately executable, mechanically verified implementations of

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216:4                          Yiyun Liu, James Parker, Patrick Redmond, Lindsey Kuper, Michael Hicks, and Niki Vazou

multiset, two-phase map, and causal tree [Grishchenko 2010] RDTs. Because Liquid Haskell is an
extension of standard Haskell, our applications are real, running Haskell applications, but now
using mechanically verified RDT implementations.
  Contributions. In summary, this paper makes the following contributions:
   • We present an extension to Liquid Haskell that supports stating and proving refinements
     of typeclass methods’ types. The engineering of this extension is an interesting interaction
     between GHC and Liquid Haskell’s core proof infrastructure, and our design sheds light on
     the interplay between coherent typeclass resolution and modular proofs (Sections 2 and 3).
   • We use our extension to Liquid Haskell to modularly verify that 34 standard instances
     satisfy the laws of five widely-used Haskell typeclasses, the Semigroup , Monoid , Functor ,
      Applicative , and Monad typeclasses (Section 2.3).
   • We further use our extension to Liquid Haskell to implement a platform for distributed
     applications based on verified replicated data types. We define a typeclass whose Liquid
     Haskell type captures the mathematical properties that must be true of RDTs, and we prove
     in Liquid Haskell that strong convergence holds if these properties are satisfied (Section 4).
   • We implement (and prove correct) several instances of our refined typeclass, including the
     first immediately executable, mechanically verified implementations of multiset, two-phase
     map, and causal tree [Grishchenko 2010] RDTs (Section 4.2).
     Using these verified RDT instances,1 we implement two realistic applications: a shared
     calendar event planner and a collaborative text editor (Section 4.7).
  As of this writing, we have nearly completed merging our extension into the main Liquid Haskell
implementation, at which point it will be freely available.

This section begins with background on Liquid Haskell [Rondon et al. 2008; Vazou et al. 2014],
which extends Haskell with refinement types (Section 2.1). Then it presents our extension to Liquid
Haskell, which permits annotating a typeclass definition’s methods with refinement types, thus
allowing a typeclass’s clients to assume those richer types, while obligating a typeclass’s instances
to implement them (Section 2.2). As a demonstration of the effectiveness of this approach, we verify
that 34 instances of five standard typeclasses satisfy the expected laws (Section 2.3).

2.1     Refinement Types and Liquid Haskell
A refinement type augments a base type T with a predicate ϕ that restricts the set of valid val-
ues [Rushby et al. 1998; Xi and Pfenning 1998; Constable and Smith 1987]. In Liquid Haskell, a
refinement type has the form { x:T | ϕ }Ðthe base type T is refined according to predicate ϕ,
which may refer to values of the base type via the variable x (if it appears free in ϕ). For example, a
refinement type for positive integers would be { x:int | x > 0 }, i.e., T = int and ϕ = x > 0.
   In Figure 1, the function head uses this kind of refinement on the type of its input list xs, stating
the precondition that the list’s length be positive. This refinement thus prevents calling head with
an empty list, thus precluding the exception that could otherwise result.
   Also in the figure we see code for Haskell’s standard list append operator, (++) , which uses
a refinement to state a postcondition. The (standard) code states that appending an empty list []
with a list ys yields ys (line 2), while appending a non-empty list (with a head element x and a
tail xs) with a list ys is the result of cons’ing x to the front of xs appended to ys (line 3). The
refinement type states that the output list’s length is equal to the sum of the lengths of the input

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Verifying Replicated Data Types with Typeclass Refinements in Liquid Haskell                                   216:5

  head :: {xs:[a] | length xs > 0} → a
  head (h:_) = h

  (++) :: xs:[a] → ys:[a] → { v:[a] | length v == length xs + length ys }
  []     ++ ys = ys
  (x:xs) ++ ys = x:(xs ++ ys)

  lAssoc :: x:[a] → y:[a] → z:[a] → { x ++ (y ++ z) == (x ++ y) ++ z }
  lAssoc []    _ _ = ()
  lAssoc (_:x) y z = lAssoc x y z

Fig. 1. Haskell’s list head and append (++) functions augmented with refinement types to capture pre- and
post-conditions; and lAssoc, a statement and proof that append is associative.

lists. The refinement type predicate is able to refer to the function’s inputs via names xs and ys,
which annotate the parameters’ types. Liquid Haskell proves that postconditions such the one on
(++) hold by generating appropriate verification conditions from the code and delegating to an
SMT solver (in particular, Z3 [De Moura and Bjùrner 2008]); we say more on this below.
    In the refinement types of head and (++) , length refers to the Haskell length function on lists.
Such references to normal language terms are lifted into the refinement logic through a process
called refinement reflection [Vazou et al. 2017]. Refinement reflection uses the definitions of Haskell’s
functions to generate singleton refinement types that precisely describe the result of the function.
To ensure soundness of type checking, only provably terminating functions can be reflected.
    Refinement reflection makes it possible to write and mechanically verify proofs of independent,
general properties, e.g., involving many functions and not just a single one. These are called
extrinsic properties, as they are written externally to any particular function’s definition, as opposed
to intrinsic properties like the ones on head and (++) . For example, lAssoc in Figure 1 is an extrinsic
property (and proof) that append is associative. The property is the type, which states that for all
lists x, y, z we have that x ++ (y ++ z) equals (x ++ y) ++ z. Note that the postcondition of
lAssoc is equivalent to { v:unit | x ++ (y ++ z) == (x ++ y) ++ z }Ðthe v:unit part is
dropped since there is no need to name the result, which is not mentioned in the predicate.
    Proofs of extrinsic properties are themselves Liquid Haskell definitions whose type is the desired
property. The proof in our example is the body of lAssoc , which expresses that the property holds
by inductionÐthe base case is for [] and the recursive case is for (_:x) y z. In the base case, there
is nothing specific the programmer has to write other than (), the łreturn typež of the property.
For the recursive case, the inductive argument occurs by referring to the property on the strictly
smaller input x y z (rather than (_:x) y z). This proof follows a standard formula [Vazou et al.
2018] in which the handwritten part shown here provides the structure, and the proof details are
filled in using a combination of Proof by Logical Evaluation (PLE) [Vazou et al. 2017; Leino and
Pit-Claudel 2016] to automate function unfolding, and SMT solving, which automates reasoning
over specific theories (e.g., equality and linear arithmetic). Both strategies preserve decidable type
checking. Such automation helps make it possible to write substantial proofs in Liquid Haskell, as
previously demonstrated with a proof of noninterference for the LWeb system [Parker et al. 2019].
Proofs can also be done by hand, as needed/desired [Vazou et al. 2018].
    Liquid Haskell’s implementation is simplified by making use of GHC, the Glasgow Haskell
Compiler [GHC 2020], to partially evaluate programs. Liquid Haskell first parses refinement types,
which are written in the comments of normal Haskell code. Then it passes the Haskell code to GHC,
and gets back the code as Core, which is GHC’s intermediate language. Liquid Haskell lifts the Core

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 class Semigroup a where                     class Semigroup a ⇒ VSemigroup a where
   (<>) :: a → a → a                           lawAssociativity :: x:a → y:a → z:a →
                                                   {x <> (y <> z) == (x <> y) <> z}

 instance Semigroup [a] where                instance Semigroup [a] ⇒ VSemigroup [a] where
   (<>) = (++)                                 lawAssociativity = lAssoc

 (a) Standard Semigroup typeclass and        (b) VSemigroup extends Semigroup with an associativity law,
 the list instance of it                     which its list instance satisfies via lAssoc

                                   Fig. 2. Typeclasses with Refinement Types

output into the refinement logic using refinement reflection. Finally, it converts the refinement types
and corresponding Core output into SMT-LIB2 queries [Barrett et al. 2010] which can automatically
be verified by Z3. If any queries are invalid, Liquid Haskell reports an error message.

2.2     Refinement Types for Typeclasses
We have extended Liquid Haskell to allow typeclass methods to be annotated with refinement
types. Doing so allows a developer to state properties that a typeclass’s methods should always
satisfy. Clients of that typeclass can thus assume those properties in their own proofs. Of course,
implementors of the typeclass’s instances must prove the properties hold for their instance.
   Laws as Refinement Types. We illustrate the utility of our extension by showing how standard
typeclass laws can be encoded as refinement types. Laws are properties that clients of a typeclass
generally assume, and that implementors of a typeclass are supposed to ensure. Of course, without
something like our extension, there is no guarantee that they do so.
   Figure 2(a) shows the Semigroup typeclass, which defines a type a that is equipped with a single
operator <>. One particular implementation of this typeclass for lists ([a]) is also shown, where
<> corresponds to the List append operator. A key law of semigroups is that the <> operator is
associative. Clients of Semigroup may assume this law holds of any instance they are given; they
may break if it does not. Fortunately, as we proved in the previous subsection, List append is
associative, so the List instance of Semigroup satisfies the law. How can we show this?
   We extend the syntax of typeclasses to allow for refinement types on method declarations. Below
is a version of Semigroup extended to capture the associativity typeclass law as a refinement type.
  class VSemigroup a where
    (<>)             :: a → a → a
    lawAssociativity :: x:a → y:a → z:a → {x <> (y <> z) == (x <> y) <> z}
 VSemigroup matches the definition of Semigroup from Figure 2(a) but adds typeclass method
 lawAssociativity , which (extrinsically) defines the associativity property. All VSemigroup in-
stances are now required to define lawAssociativity and provide an explicit associativity proof.
The lower portion of Figure 2(b) implements the list instance of VSemigroup by extending Semigroup
list instance and providing the associativity proof lAssoc from Section 2.1.
  Using the Laws, Modularly. By allowing refinement types on typeclass definitions, we extend the
modularity benefits of typeclasses from code to proofs. In particular, clients of a refined typeclass
can take advantage of its stated refinement types when conducting their own proofs. For example,
below we express and prove an extrinsic property that extends associativity to four elements.
  assoc2 :: VSemigroup a ⇒ x:a → y:a → z:a → w:a

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         → { x <> (y <> (z <> w)) == ((x <> y) <> z) <> w }
  assoc2 x y z w = lawAssociativity x y (z <> w)
                   `const` lawAssociativity (x <> y) z w
The proof is a consequence of lawAssociativity , which is applied twice, combined with Haskell’s
const ant function. The proof is carried out once, independent of any VSemigroup instance, but
the property holds for all of them.
   The code of our VRDT case study (Section 4) is set up similarly. We define a VRDT typeclass
with operations on data that enjoy particular properties. Relying on these properties, we can prove
strong convergence of all VRDTs; this property essentially states that two replicas that start in the
same state will end up in the same state if they apply the same operations, in any order.
   Refinements in Subclasses. For improved modularity, our extension allows typeclass method
refinements to refer to superclass methods. For example, another way to write VSemigroup is
shown at the top of Figure 2(b), which literally extends Semigroup with the added method. Defining
properties in subclasses is particularly useful when not wanting to modify typeclasses in other
packages (including those in normal, not Liquid, Haskell). It can also be useful when not wanting to
necessarily require implementations to prove all possible properties; different subsets of properties
of interest can be defined in different subclasses.
   Haskell typeclasses can have multiple superclasses, which allows defining a typeclass containing
properties of data structures that implement multiple typeclasses. For example, consider the Monoid
typeclass, which extends Semigroup to also include the mempty identity element. Since a particular
data structure (like a list) can implement both typeclasses, we could define the verified typeclass
VMonoid that extends VSemigroup and Monoid with two laws.
  class (VSemigroup a, Monoid a) ⇒ VMonoid a where
    lawEmpty   :: x:a → { x <> mempty == x && mempty <> x == x }
    lawMconcat :: xs:[a] → { mconcat xs == foldr (<>) mempty xs }
That mempty is an identity for <> is encoded in the lawEmpty method; it refers to (<>) , which is
defined in the VSemigroup parent typeclass. The law lawMconcat guarantees that mconcat , defined
by Monoid , is equivalent to folding over a non-empty list with (<>) .
  We can also define verified components from other verified components, where proofs of the
former’s properties can depend on properties that hold of the latter. For example, in our VRDT
case study, we define a VRDT TwoPMap in terms of any other VRDT; here is the beginning of the
instance definition:
  instance (Ord k, VRDT v) ⇒ VRDT (TwoPMap k v) where ...
The proofs of TwoPMap k v’s properties make use of the properties that hold for Ord k and VRDT v.
   Coherent Typeclass Resolution and Refined Instances. There is an interesting twist in our VMonoid
example. As mentioned, Monoid extends Semigroup ; as such, proofs of properties in VMonoid may
wish to assume that the VSemigroup instance resolved for VMonoid has the same parent superclass
as that of the resolved Monoid instance. Indeed, this assumption is critical for the given properties:
we require that the <> operator in both Monoid and VSemigroup to be literally the same function.
   Our implementation reuses Haskell’s existing typeclass resolution procedure, which is useful
from an engineering perspective, and has two consequences. First, and importantly for the above
example, Haskell typeclass resolution is coherent by default, but not as a rule. That resolution
is coherent means that there is only one possible typeclass instance for a particular base type,
e.g., Semigroup [a] will always resolve to the same instance definition. Coherence is handy for
addressing the above łdiamond problemž [Stroustrup 1989]. That said, programmers may override
coherence via pragma. While doing so is rare, the possibility means that axiomatizing an assumption

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of coherence would be unsound. Our implementation therefore introduces a checked invariant
on an elaborated dictionary when coherence could be consequential, i.e., because the class in
question has multiple parent superclasses that should have a common ancestor. Liquid Haskell
then automatically proves this invariant after resolution has taken place. We give more details
about how this works in Section 3.3.
   The second consequence of reusing Haskell’s resolution procedure is that we cannot directly
support instances on refined types, only base types. For example, we cannot have distinct semigroup
instances for positive and negative numbers, i.e., instance VSemigroup { v:Int | 0 < v } and
instance VSemigroup { v:Int | v < 0 }.
   Fortunately, there is an easy workaround: users can define instances on refined newtype wrappers;
newtype is already commonly used in Haskell to offer an alternative instance implementation
while maintaining coherence. So, for example, a user could define:
  newtype Pos = Pos {getPos :: {v:Int | v > 0}}
  newtype Neg = Neg {getNeg :: {v:Int | v < 0}}

Now we can define the VSemigroup instances without any additional refinements, as follows:
  instance VSemigroup Pos where
    lawAssociativity x y z = ()
    mappend (Pos x) (Pos y) = Pos (x + y)
  instance VSemigroup Neg where
    lawAssociativity x y z = ()
    mappend (Neg x) (Neg y) = Neg (x + y)

2.3     Verifying Laws of Standard Typeclass Instances
Before getting into the details of how we implemented typeclass refinements (in the next section)
we present a case study demonstrating that the pattern we have shown for stating and verifying
the laws of standard typeclass instances works well.
   In our case study, we considered five standard typeclasses: Semigroup , Monoid , Functor , Monad ,
and Applicative . Then we defined subclasses ( VSemigroup , VMonoid , etc.) that contain the parent’s
expected typeclass laws. We have shown the definitions of VMonoid and VSemigroup already;
Functor , Monad , and Applicative are shown in Figure 3 with their refined subclasses. We defined
and verified instances of the above typeclasses for the All , Any , Dual , Endo , Identity , List , Maybe ,
Peano , Either , Const , State , Reader , and Succs datatypes. Because datatypes are instances of
multiple subclasses, we performed 34 instance-verifications in total.
   This effort was quite manageable. Table 1 tabulates the results, indicating the instance type in the
first column, and the typeclasses it implements in the second. For each implementation we tabulate
the lines of proof required to verify the stated laws. We also report the average (and standard
deviation) of the time (in seconds) it took Liquid Haskell to verify each module.2
   For many of the proofs, Liquid Haskell is able to automatically verify the typeclass properties
using PLE (Proof by Logical Evaluation) [Vazou et al. 2017; Leino and Pit-Claudel 2016]. As such,
most of the proofs are a couple of lines of code. In general, PLE reduces manual effort but increases
verification time, but for most modules the proofs are checked within just a few seconds. There are
some exceptionsÐthe List , Reader , and Succs Applicative instances are more involved as they
require the user to manually specify certain lemmas.

2 Experiments
            were run with five trials. All experiments in this paper were run on a machine with an Intel Xeon CPU with
64GB of RAM, running Ubuntu 16.04 with Z3 version 4.8.8.

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Verifying Replicated Data Types with Typeclass Refinements in Liquid Haskell                                   216:9

  class Functor f where
    fmap :: (a → b) → f a → f b
    (<$) :: a → f b → f a

  class Functor m ⇒ VFunctor          m where
    lawFunctorId :: x:m a →           {fmap id x = id x}
    lawFunctorComposition ::          f:(b → c) → g:(a → b) → x:m a
                          →           {fmap (f . g) x = (fmap f . fmap g) x}

  class Functor f ⇒ Applicative f where
    pure :: a → f a
    (<*>) :: f (a → b) → f a → f b
    liftA2 :: (a → b → c) → f a → f b → f c
    (*>) :: f a → f b → f b
    (<*) :: f a → f b → f a

  class (VFunctor f, Applicative f) ⇒ VApplicative f where
    lawApplicativeId :: v:f a → {pure id <*> v = v}
    lawApplicativeComposition :: u:f (b → c) → v:f (a → b) → w:f a
                              → {pure (.) <*> u <*> v <*> w = u <*> v <*> w}
    lawApplicativeHomomorphism :: g:(a → b) → x:a → {px:f a | px = pure x}
                               → {pure g <*> px = pure (g x)}
    lawApplicativeInterchange :: u:f (a → b) → y:a
                              → {u <*> pure y = pure ($ y) <*> u}

  class Applicative m ⇒ Monad m where
    (>>=) :: m a → (a → m b) → m b
    (>>) :: m a → m b → m b
    return :: forall a. a → m a

  class (VApplicative m, Monad m) ⇒ VMonad m where
    lawMonad1 :: x:a → f:(a → m b) → {f x = return x >>= f}
    lawMonad2 :: m:m a → {m >>= return = m }
    lawMonad3 :: m:m a → f:(a → m b) → g:(b → m c)
              → {h:(y:a → {v:m c | v = f y >>= g}) | True}
              → {(m >>= f) >>= g = m >>= h}
    lawMonadReturn :: x:a → y:m a → {(y = pure x) ⇔ (y = return x)}

      Fig. 3. Typeclass definitions for Functor, Applicative, and Monad and their associated laws.

  In sum, this case study shows that typeclass refinements constitute a natural and modular
approach to stating typeclass laws and proving that they are satisfied by their instances. Section 4
presents further evidence, in the form of our VRDT case study, of the utility of typeclass refinements.

Now we turn to the question of how we extended Liquid Haskell to implement typeclass refinements.
  Liquid Haskell statically verifies Haskell programs by analyzing Core expressions. Core is a small,
explicitly-typed variant of System F generated during compilation by GHC, the Glasgow Haskell
Compiler. Liquid Haskell can thus ignore many of Haskell’s myriad source-level constructs, and
focus on a smaller language. This implementation approach is also useful for managing Liquid

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216:10                       Yiyun Liu, James Parker, Patrick Redmond, Lindsey Kuper, Michael Hicks, and Niki Vazou

Table 1. Total lines of proofs for each typeclass instance and the average verification time in seconds. Each
reported time covers the laws on its row and those on the following rows up to the next reported time.

         Type        Typeclass     # Lines   Verif. Time        Type     Typeclass     # Lines   Verif. Time
                                     Proof    (Std. dev.)                                Proof    (Std. dev.)
         All         Semigroup           2   1.555 (0.133)      Maybe    Semigroup          3    4.360 (0.500)
                     Monoid              2                               Monoid             3
         Any         Semigroup           2   1.129 (0.112)               Functor            3    3.961 (0.428)
                     Monoid              2                               Applicative        8
         Dual        Semigroup           2   1.522 (0.254)               Monad              6
                     Monoid              2                      Peano    Semigroup          3    1.236 (0.157)
         Endo        Semigroup           2   1.326 (0.191)               Monoid             3
                     Monoid              2                      Either   Functor            3    4.672 (0.538)
         Identity    Semigroup           2   1.534 (0.186)               Applicative        8
                     Monoid              2                               Monad              6
                     Functor             2   2.727 (0.274)      Const    Functor            2    0.530 (0.077)
                     Applicative         4                      State    Functor           12    1.182 (0.118)
                     Monad               4                      Reader   Functor           11    2.997 (0.347)
         List        Semigroup           3   1.436 (0.148)               Applicative       21
                     Monoid              3                      Succs    Functor            2    6.390 (0.649)
                     Functor             4   8.117 (0.380)               Applicative       18
                     Applicative        25
                     Monad              10

Haskell as an independent codebase. Even as Haskell is actively modified with new or improved
features, Liquid Haskell needs no modification because those features are translated to Core.
   The challenge with implementing typeclass refinements is that GHC removes typeclasses entirely
during the translation to Core; each typeclass is replaced with a dictionary of its various operations.
Thus, our extension to Liquid Haskell needs a way to connect the refinements the programmer
writes on typeclass methods with the translated Core that comes back from GHC, and it needs to
do so in a way that is robust to (at least some) future changes in GHC’s elaboration. This section
explains how we do this by delegating as much work as possible to GHC. We also explain how we
model the fact that typeclass elaboration is coherent by default, to simplify user proofs.

3.1 GHC Typeclass Elaboration
Haskell compilers, including GHC, translate typeclass definitions and instances to datatypes known
as dictionaries [Sulzmann et al. 2007]. As an example, the Semigroup typeclass definition from Fig-
ure 2(a) is translated to a dictionary as the following datatype, Semigroup (simplified for clarity).
  data Semigroup a = CSemigroup { (<>) ::                    a → a → a }

The datatype Semigroup a has a single constructor CSemigroup and one field for the <> method.
In general, one field is defined for each typeclass method.
   Typeclass instances are translated into dictionary values. For example, the list Semigroup instance
from Figure 2(a) generates a Semigroup [a] dictionary, which GHC names $fSemigroup [].
  $fSemigroup[] :: Semigroup [a]
  $fSemigroup[] = CSemigroup ($c<>[])
  $c<>[]        = (++)

The dictionary’s field is the list append method (++) , which is assigned to the generated variable
$c <>[] . (Both the dictionary and field variables are prefixed with $ to indicate they are internal
variable names, and posfixed with [] to indicate the list instance.)

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  Elaboration. The translated dictionaries are inserted after each method call via a process known
as elaboration. For example, the Haskell code x <> y that appends two list variables x, y ::
[a] is elaborated to (<>) $fSemigroup [] x y, where now (<>) is the record selector of the
Semigroup data type. Functions that explicitly mention the Semigroup a constraint, as in f below,
are elaborated to take an explicit dictionary argument; f elaborates to fElab , on the right.
  f :: Semigroup a ⇒ a → a → a                      fElab :: Semigroup a → a → a → a
  f x y = x <> y                                    fElab d x y = (<>) d x y

   Subclass Encoding and Coherence. In Core, subclass dictionaries store references to parent dic-
tionaries as fields. For example, the dictionary of the VMonoid typeclass from Section 2.2 has four
fields, two for the class methods and two for the superclass dictionaries:
  data VMonoid a = CVMonoid {
      p1VMVSemigroup :: VSemigroup a
    , p2VMMonoid     :: Monoid a
    , lawEmpty       :: a → ()
    , lawMconcat     :: [a] → ()

Interestingly, Semigroup is a superclass of both Monoid and VSemigroup , which leads to the
łdiamond problem.ž When the user writes x <> y, it is unclear if GHC’s elaboration will access
(<>) via the Monoid or via the VSemigroup field. That is, GHC can elaborate the coherence code
below to either coherenceElab1 or coherenceElab2 .
  coherence :: VMonoid a ⇒ a → a → a
  coherence x y = x <> y

  coherenceElab1, coherenceElab2 :: VMonoid a → a → a → a
  coherenceElab1 d x y = (<>) (p1VSSemigroup (p1VMVSemigroup d)) x y
  coherenceElab2 d x y = (<>) (p1MSemigroup (p2VMMonoid d)) x y

Here, p1VSSemigroup and p1MSemigroup access the semigroup dictionary from the VSemigroup
and Monoid , respectively. Such nondeterminism of elaboration could lead to problems, as the runtime
semantics of coherence could change with GHC’s elaboration decision. Fortunately, by default
GHC’s elaboration is coherent [Bottu et al. 2019], meaning that the dictionary for each typeclass
instance at a given type is unique; as such, we know that the Semigroup dictionary is the same
irrespective of how it is accessed, i.e., ( p1VSSemigroup . p1VMVSemigroup ) = ( p1MSemigroup
. p2VMMonoid ). Such an equality may be needed in a proof, so our implementation reflects it (in a
safe manner) in the proof state, as discussed in Section 3.3.

3.2   Interaction with GHC
Now we explain how we modified Liquid Haskell’s interaction with GHC so that we can verify
typeclass refinements.
   Refinements are ported to Dictionaries. The core intuition of typeclass verification is that re-
finements on typeclasses should be turned into refinements on the respective GHC-translated
dictionaries. For example, the dictionary for the VSemigroup refined typeclass of Figure 2(b) should
be refined to carry the associativity proof obligation (as a normal refinement):
  data VSemigroup a = CSemigroup {
    p1VSSemigroup    :: Semigroup a
  , lawAssociativity :: x:a → y:a → z:a → {x <> (y <> z) == (x <> y) <> z}

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                                                                              Liquid Haskell        GHC

                                                                 Haskell                          Typecheck,
                                                              Refinements          Parse
                    Liquid Haskell           GHC              (comments)

                                                                                Refine Dicts;       GHC
                                            Parse,                                                  Core
     Haskell                              Typecheck,                              Embed
                                           Elaborate          Liquid Types
   Refinements          Parse                                 Liquid Core       Break Mut.         Haskell
                                                                                Recursion           AST

   Liquid Types       Refinement             GHC                       Core                       Typecheck,
                      Reflection             Core                               Refinement         Elaborate
   Liquid Core                                                                  Reflection

                                                              Liquid Types                          GHC
   Liquid Types                                                Liquid Core       Constraint
                                                               (elaborated)      Generation
   Liquid Core

                                                              Add Coherence
                        SMT                                    Constraints         SMT
                       Queries                                                    Queries

   (a) Liquid Haskell’s original architecture.         (b) Liquid Haskell’s architecture with typeclass support.

                                   Fig. 4. Changes to Liquid Haskell’s architecture.

(Here, the first field of the dictionary is a link to the parent typeclass.) But of course we cannot
literally do this because lawAssociativity is not well-formed Core. We make it so by expanding
Liquid Haskell’s interaction with GHC, using it to parse, typecheck, and elaborate refinements.
   Liquid Haskell’s Architecture. Figure 4 summarizes Liquid Haskell’s architecture and interaction
with GHC API before and after support for refined typeclasses.
   The first step is similar in both architectures: send Haskell source to GHC, which comes back as
Core, and parse refinement types appearing in comments. Before typeclass support (Figure 4a), the
Core expressions were used by Liquid Haskell to strengthen the exact types for reflected functions
and to generate the verification constraints that were finally checked by an SMT. After typeclass
support, the returned Core may include dictionary definitions, elaborated implementations of those
dictionaries, and elaborated clients that use them, as explained in Section 3.1. These dictionaries
need to be connected to the refinement types retrieved from typeclass methods.
   To connect typeclass method refinements to elaborated dictionaries, Liquid Haskell converts
the parsed-out refinements into Haskell abstract syntax trees that make explicit reference to the
relevant typeclasses. This occurs in the Refine Dicts; Embed step of the architecture diagram. As an
example, for the VSemigroup method’s refinement of lawAssociativity (Figure 2), the following
Haskell source-code AST expression is constructed:
  (\x y z () → x <> (y <> z) = (x <> y) <> z) :: VSemigroup a ⇒
      a → a → a → () → Bool

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GHC typechecks and elaborates the expression in the context of the VSemigroup definition and
applies the appropriate dictionary arguments to typeclass methods. It returns the following:
  (\d x y z () → x `<> (p1VSemigroup d)` (y `<> (p1VSemigroup d)` z) =
    (x `<> (p1VSemigroup d)` y) `<> (p1VSemigroup d)` z)
  :: VSemigroup a → a → a → a → () → Bool

Now the dictionary d is explicit in the elaborated Core expression. Liquid Haskell converts this
into a refinement expression using refinement reflection, and then combines it with the Core code
returned from GHC in the first step; for the example, the constructor and selectors for VSemigroup
in Figure 2 are the following:
  data VSemigroup a = CVSemigroup {
    p1VSSemigroup    :: Semigroup a
  , lawAssociativity :: x:a → y:a → z:a
      → {x `<> p1VSSemigroup` (y `<> p1VSSemigroup` z)
         == (x `<> p1VSSemigroup` y) `<> p1VSSemigroup` z}

  lawAssociativity :: d:VSemigroup a → x:a → y:a → z:a
    → {x `<> (p1VSemigroup d)` (y `<> (p1VSemigroup d)` z)
       == (x `<> (p1VSemigroup d)` y) `<> (p1VSemigroup d)` z}

First, notice that lawAssociativity basically matches the elaborated Core expression returned
by GHC, but it has been converted to match Liquid Haskell’s refinement type syntax. Second, notice
that the VSemigroup data constructor uses the returned Core, but has applied one additional step of
transformation so that it can refer to the superclass’ (i.e., Semigroup) operator. Now, VSemigroup
instances must satisfy the required properties since the dictionary datatype has been refined.
   Typeclass methods do not only appear in the refinements of typeclasses. Functions with type-
class constraints may also contain typeclass methods in their refinements. We also elaborate the
refinement expressions of these functions so that the appropriate dictionary arguments to typeclass
methods are applied.
   This whole process corresponds to the refine dicts; embed, typecheck, elaborate, and refinement
reflection steps in the diagram. The breaking mutual recursion step inlines selector calls in the derived
GHC dictionaries to break superficial mutual recursion since Liquid Haskell requires explicit proof
of termination. The adding coherence constraints step is detailed in Section 3.3.
   Our implementation of all of this amounts to about 2000 lines of code, and is part of a fork
Liquid Haskell’s codebase which is up to date with the main trunk as of August, 2020. Around
400 lines of code is used to define functions that communicate with the GHC API. The top-level
driver function that orchestrates GHC’s Typecheck; Elaborate and Liquid Haskell’s Refine Dicts;
Embed step takes another 700 lines of code. This also includes the embedding functions from Liquid
Haskell predicates to the Haskell AST. The rest of the code roughly corresponds to the Refinement
Reflection step, where elaborated dictionaries are being converted into ordinary refined data types
which Liquid already knows how to process and verify.

3.3   Reasoning About Coherence
In Section 3.1 we mentioned that GHC’s elaboration is, by default, coherent. Various proofs on
typeclass methods rely on coherence of elaboration (i.e., only hold when instances are globally
unique). Here, we give an example of such a proof and detail how coherence is automatically
encoded and checked using refinement types.

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  lawMconcat for the VMonoid Dual Instance Requires Coherence. Given any binary operation <>,
we can always define a dual operation <+> :
  x <+> y = y <> x
Therefore, if we have a Semigroup instance for some type a, we can also define a Dual instance of
a Semigroup . Indeed, we can define Semigroup s of Dual s of any type a once and for all:
  newtype Dual a = Dual {getDual :: a}
  instance Semigroup a ⇒ Semigroup (Dual a) where
    Dual (v :: a) <> Dual (v' :: a) = Dual (v' <> v)
Now, whenever we define a Semigroup instance for some type, GHC will automatically create its
corresponding Dual instance. We do the same for Monoid , VSemigroup , and VMonoid .
  instance Monoid a ⇒ Monoid (Dual a) where
    mempty     = Dual mempty
    mconcat xs = foldr (<>) mempty xs

  instance VSemigroup a ⇒ VSemigroup (Dual a) where
    lawAssociative (Dual v) (Dual v') (Dual v'') = lawAssociative v'' v' v

  instance VMonoid a ⇒ VMonoid (Dual a) where
    lawEmpty (Dual v) = lawEmpty v
     -- lawMconcat :: VMonoid a ⇒ xs:_ → {mconcat xs = foldr (<>) mempty xs}
     lawMconcat xs = () `const` mconcat xs
The proof of lawMconcat proceeds by a simple unfolding of the mconcat definition, which is
expressed as a call to that function (the full proof must be then cast to unit via Haskell’s const ()).
   This proof requires coherence to hold. To see why, consider the proofs for the elaborated
definitions. The elaborated equality mconcat xs = foldr (<>) mempty xs is as follows, for the
dictionary d :: VMonoid a.

             mconcat xs
         =   by elaboration
             mconcat ( $fSemigroupDual ( p2VMMonoid d)) xs
         =   by unfolding of mconcat
             foldr ((<>) ( $fSemigroupDual ( p1SMonoid ( p2VMMonoid d))))
                              ( mempty ( $fMonoidDual ( p2VMMonoid d)))
         =   by coherence
             foldr ((<>) ( $fSemigroupDual ( p1VSSemigroup ( p1VMVSemigroup d))))
                              ( mempty ( $fMonoidDual ( p2VMMonoid d)))
         =   by de-elaboration
             foldr (<>) mempty xs

   The proof employs a coherence step to equate the two different ways that the semigroup
operator <> is accessed. Concretely, the above equational proof only holds when p1VSSemigroup
( p1VMVSemigroup d) equals p1SMonoid ( p2VMMonoid d), for all d :: VMonoid a. This equality
cannot be asserted by the programmer, as dictionaries do not appear in the source.
   Coherence as Dictionary Refinements. We represent the expected effect of coherent resolution as
a refinement type on the datatype dictionary definitions for typeclasses. In particular, the equality
of ancestor typeclasses is expressed as a refinement type on the fields of parent typeclasses. For
example, the Core representation of VMonoid is as follows.

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  data VMonoid a = CVMonoid {
    p1VMVSemigroup :: VSemigroup a
  , p2VMMonoid :: { v:Monoid a | p1VSSemigroup p1VMVSemigroup = p1MSemigroup v }
  , ... -- as before
The added refinement on the second field states that the Semigroup from the VSemigroup par-
ent (i.e., p1VSSemigroup p1VMVSemigroup ) must be equal to the Semigroup from the Monoid
parent (i.e., p1MSemigroup v). (This invariant is similar in purpose to to an ML-module shar-
ing constraint [MacQueen 1984].) As a result, coherence becomes a checked invariant for each
VMonoid ÐLiquid Haskell assumes the property for any VMonoid but checks it for instance declara-
tions. This approach is sound even when GHC’s elaboration is potentially incoherent due to use
of the INCOHERENT language pragma. Clients of dictionaries will still assume the invariant, but
constructed dictionaries (i.e., typeclass instances) will induce an error from Liquid Haskell if the
choice made by incoherent resolution breaks the invariant. We note that inheritance diamonds are
rare in Haskell, so such checked invariants will also be rare.
   Being able to take advantage of Haskell’s (mostly) coherent typeclass resolution is the silver
lining of our limitation that refined types cannot be distinct typeclass instances, as discussed at the
of end of Section 2.2. If we allowed different instances of a class TC for both { v:int | v > 0 }
and int , say, then we could not use Haskell’s proved-coherent mechanism, and would have to
develop our own and/or allow one to be customized as part of proof search. There is no guarantee
that the result would be coherent. In other dependently typed systems with typeclasses, e.g., Coq,
the user has to explicitly encode and prove coherence requirements, case by case (see Section 5).

3.4 Incompatibility with SMT interpreted Operators
Predating our extension’s support for typeclass refinements, Liquid Haskell provided specialized
support for the Eq, Ord , and Num typeclasses by hard-coding their expected behavior. For example,
in the case of Eq, Liquid Haskell interprets uses of the == operator from the Eq typeclass as SMT
equality, which amounts to structural equality in Haskell. This specialized support allows the
programmer to reason about equality, ordering, and numbers in Liquid Haskell programs, and
leverages the SMT solver’s automation. Unfortunately, it is neither sound nor complete. In the case
of Eq, soundness fails because a user-defined equivalence relation does not necessarily imply the
stronger structural equality. In the case of Ord , completeness fails when a concrete Ord instance is
collapsed into an abstract partial order, losing the extra information from the instance definition.
   We might imagine this legacy specialized handling can now be dropped in favor of typeclass
refinements. Unfortunately, such an approach would break existing proofs, even for properties
that were not affected by the potential unsoundness. This is because there would no longer be
a connection between the user-defined instances of the three classes and their corresponding
SMT theories, thus hampering automated reasoning. The SMT solver is unable to recognize that
the post-elaboration expression (+) $fNumFoo (the + method of the Foo instance of Num ) can be
reasoned about abstractly using the solver’s internal knowledge of the axioms of numbers; the
method (+) is merely a ternary operation (when including the dictionary argument), like any other.
Of course, the programmer can start from the class laws and derive lemmas manually, but that is
not what is happening in current proofs. And indeed, we want to let the SMT solver automatically
derive those properties using its decidable theories.
   As such, at present we continue to ignore the instances from the aforementioned classes, e.g., by
discarding the dictionary argument and interpreting the instances specially. In the future we plan
to explore a hybrid approach that leverages SMT theories when possible (e.g., when we can prove
soundness and retain precision) and falls back to general (dictionary-based) refinements otherwise.

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This section presents our platform for programming distributed applications based on replicated
data types (RDTs) [Shapiro et al. 2011b,a; Roh et al. 2011]. We define a Haskell typeclass VRDT to
represent RDTs and use type refinements to state the mathematical properties that RDTs satisfy.
We then prove in Liquid Haskell that VRDT instances (which must satisfy these properties) enjoy
strong convergence.
   We have implemented several primitive instances of the VRDT typeclass, as well as a mechanism
for building compound VRDT s based on smaller components, where the necessary properties of
the former automatically follow from the latter. With this infrastructure, as well as libraries for
message delivery and user interaction we developed, we constructed two applications, a shared
event planner and a collaborative text editor.

4.1 Background: Replicated Data Types
An RDT is a data structure designed for use in a distributed system that stores multiple replicas of
the same data. The mathematical properties of an RDT ensure the strong eventual consistency (SEC)
of replicas [Shapiro et al. 2011b], the most important aspect of which is strong convergence (SC). SC
states that RDT replicas that have received and applied the same set of updates will always have
the same state, regardless of the order in which those updates were received and applied.
   Shapiro et al. [2011b] describe two styles of RDT specifications: state-based, in which replicas
apply updates locally and periodically broadcast their local state to other replicas, which then
merge the received state with their own, and operation-based, in which every state-updating
operation is broadcast and applied at each replica. Shapiro et al. [2011b] prove that state-based
and operation-based RDTs are equivalent in the sense that each can be implemented in terms of
the other (although practical implementation considerations may motivate the choice of one or
the other in a particular application). In this work, we focus our attention on the operation-based
style, which is especially suitable for implementing ordered sequence RDTs that are useful for
applications such as collaborative text editing [Roh et al. 2011; Ahmed-Nacer et al. 2011; Attiya
et al. 2016; Oster et al. 2006; Preguica et al. 2009; Weiss et al. 2009; Kleppmann and Beresford 2017].
   The key to proving convergence of (operation-based) RDTs is to require that, under appropriate
circumstances, an RDT’s operations commute. A replica that receives an operation from a client
can update its own state and broadcast that operation to the other replicas. Since the operations
commute, they can be applied in any order and produce the same final state, as required by SC.

4.2 Verifying Strong Convergence of RDTs using Typeclass Refinements
We define an RDT as the Liquid Haskell typeclass VRDT , shown in Figure 5(a). Each instance of
VRDT has an associated Op type that specifies the operations that can update the RDT’s state. The
apply method takes an RDT state t, runs the given operation Op t on it, and returns the updated
state. Read operations on an RDT do not affect convergence, so they are not included in Op; rather,
each VRDT instance defines its own read methods.
   The required mathematical properties of a VRDT are specified extrinsically as methods lawCommut
and lawCompatCommut . The type of lawCommut specifies the property that operations that are com-
patible with each other and with the current state must commute. The type of lawCompatCommut
expresses that the operation-compatibility predicate compat must also be commutative. The imple-
mentor of a VRDT instance must provide definitions for the compat and compatS predicates that
lawCommut and lawCompatCommut use. The notion of operation compatibility will depend on the
RDT in question, and compat and compatS should encode any necessary assumptions. For example,
in our TwoPMap key-value map RDT implementation given in Figure 6, we express the assumption

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 class VRDT t where                                            data Max a = Max a
   type Op t
   apply :: t → Op t → t                                       instance Ord a ⇒ VRDT (Max a) where
   compat :: Op t → Op t → Bool                                  type Op (Max a) = Max a
   compatS :: t → Op t → Bool
                                                                 apply (Max a) (Max b) | a > b = Max a
    lawCommut :: x:t → op1:Op t → op2:Op t                       apply (Max a) (Max b)         = Max b
        → {(compat op1 op2 &&
             compatS x op1 && compatS x op2)                     compat op1 op2 = True
               ⇒ (apply (apply x op1) op2                        compatS max op = True
                 = apply (apply x op2) op1
                 && compatS (apply x op1) op2)}                  lawCommut max op1 op2 = ()
                                                                 lawCompatCommut op1 op2 = ()
    lawCompatCommut :: op1:Op t → op2:Op t
        → {compat op1 op2 = compat op2 op1}                    get (Max a) = a     -- read operation

 (a) The VRDT typeclass for verified RDTs                      (b) An instance for Max a of VRDT

                         Fig. 5. Definition of the VRDT typeclass and its Max instance

of unique keys in compat and compatS by deeming two insertion operations incompatible if they
specify the same key, and deeming an insertion incompatible with a state that already contains the
specified key. In the Max RDT implementation described next, compat and compatS both return
True , meaning that no assumptions are needed in order to prove that Max operations commute.

   Example VRDT. Figure 5(b) gives an example VRDT instance, Max . It contains a polymorphic value
with a defined ordering (specified with an Ord instance) and tracks the maximum value of that type.
Its corresponding operation’s type Op is itself. The apply function updates Max ’s state by taking
the greatest value of the two arguments. The get method is Max ’s read operation. Because Max is
such a simple RDT, all pairs of operations are compatible, so compat and compatS always return
True . Proofs of lawCommut and lawCompatCommut for Max are automated by Liquid Haskell.

  Strong Convergence. All VRDT instances enjoy strong convergence, the key safety property re-
quired by strong eventual consistency [Shapiro et al. 2011b], expressed extrinsically as follows:
  strongConvergence ::
    (Eq (Op a), VRDT a) ⇒
     s0:a → ops1:[Op a] → ops2:[Op a] →
      { (isPermutation ops1 ops2 && allCompatible ops1 && allCompatibleState s0 ops1)
         ⇒ (applyAll s0 ops1 = applyAll s0 ops2)}

The property states that if two lists of operations are permutations of one another, then applying
either one to the same input VRDT state will produce the same output state, assuming the list
contains mutually compatible operations, and that all of these operations are compatible with
the initial state. The Liquid Haskell proof of this property is by induction over the operation lists
and makes use of the lawCommut and lawCompatCommut laws of VRDT . Importantly, the proof is
independent of any particular VRDT instance, and thus applies to all of them.
   Unlike existing work [Shapiro et al. 2011b; Gomes et al. 2017], we do not rely on any assumptions
about so-called causal deliveryÐthe given laws are sufficient for proving strong convergence. We
discuss further in Section 4.5.

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216:18                       Yiyun Liu, James Parker, Patrick Redmond, Lindsey Kuper, Michael Hicks, and Niki Vazou

4.3 Implemented VRDTs
In addition to Max , we have implemented and mechanically verified four more primitive VRDTs:
    • Min v is the dual of Max v, and tracks the smallest value seen.
    • Sum v is an implementation of Shapiro et al. [2011a]’s Counter RDT. Ops are numbers, and
      the RDT’s state is their sum.
    • LWW t v is an implementation of Shapiro et al.’s Last Writer Wins Register RDT. When a
      replica writes to a register, it attaches a (polymorphic) timestamp to the value. A receiving
      replica only updates its value if the timestamp is greater than the current timestamp. LWW
      assumes that all timestamps are unique.
    • MultiSet v maintains a collection of values, like a Set , but each member has an associated
      count; a non-positive count indicates logical non-membership. Ops include value insertion
      and removal, each with an associated count.
   Causal Trees. More substantially, we have also implemented Grishchenko’s causal trees [2010],
which maintain an ordered sequence of values. In a CausalTree , each value is assumed to have
a unique identifier, and each value knows the identifier of the previous value. The relationship
to the previous value creates a tree data structure that can be traversed (in preorder) to recover a
converging ordering. Causal trees, like other RDTs representing ordered sequences (e.g., Roh et al.
[2011]; Oster et al. [2006]; Preguica et al. [2009]; Weiss et al. [2009]), are useful for implementing
collaborative text editing applications, but their behavior is considered especially challenging to
specify and verify [Attiya et al. 2016; Gomes et al. 2017]. We discuss our proof in Section 4.4.
  A Compound RDT: Two-phase Map. We can conveniently define new VRDT s by reusing existing
VDRT s. In doing so, proofs of a compound RDT’s required properties can be carried out (in part) by
using the properties of the RDTs they build on. As an example compound VRDT, Figure 6 defines
a two-phase map. TwoPMap implements a map from keys to values, where values are themselves
VRDT s. TwoPMap is named after the 2P-Set of [Shapiro et al. 2011a] because similarly, a key may be
inserted or removed, but cannot be reinserted once it has been removed.
    A TwoPMap ’s operations are given by the datatype TwoPMapOp . Operation TwoPMapInsert k v
inserts a k→v mapping; operation TwoPMapDelete k deletes a key; and TwoPMapApply k (Op v)
applies a VRDT operation Op on k’s value v.
    The restriction of TwoPMap which gives its name, that a key can only be inserted once and after
it is deleted it can never be re-added, is expressed in the definitions of compat and compatS . These
methods of the TwoPMap VRDT instance reiterate the requirement that the operations of the value
VRDT are compatible, connecting the proofs into one. A few additional cases are omitted from the
compatibility predicates for brevity.
    The state of a TwoPMap has three components: the key-value map itself ( twoPMap ), a tombstone
set of deleted keys ( twoPMapTombstone ) [Shapiro et al. 2011a], and a buffer for pending operations
( twoPMapPending ). The apply code for TwoPMapApply stores operations on the value of a key
k in the pending buffer if k does not yet exist in the map. The apply code for TwoPMapInsert
checks the pending buffer for the key to be inserted and applies any operations on the given value
before inserting the key/value pair. The apply code for TwoPMapDelete clears k from the map and
operations buffer, and adds it to the tombstone set; future attempted insertions of k will be ignored
due to its presence there.
  Automatically Deriving Compound VRDTs. Generally speaking, we might like to collect together
several VRDT s to create an aggregate whose operations delegate to the operations of the components.
For example, the shared event planner we discuss in Section 4.7 represents a calendar event as a
record with a title, description, time, and guest RSVP tally. To implement such a record as a VRDT ,

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Verifying Replicated Data Types with Typeclass Refinements in Liquid Haskell                                  216:19

 data TwoPMap k v = TwoPMap {
                                                                data TwoPMapOp k v =
     twoPMap          :: Map k v
                                                                    TwoPMapInsert k v
   , twoPMapTombstone :: Set k
                                                                  | TwoPMapDelete k
   , twoPMapPending   :: Map k [Op v]
                                                                  | TwoPMapApply k (Op v)

 instance (VRDT v, Ord k, Ord (Op v)) ⇒ VRDT (TwoPMap k v) where
  type Op (TwoPMap k v) = TwoPMapOp k v

  compat (TwoPMapInsert k v) (TwoPMapInsert k' v') | k == k' = False
  compat (TwoPMapApply k op) (TwoPMapApply k' op') | k == k' = compat op op'
  compat _                   _                               = True

  compatS (TwoPMap m p t) (TwoPMapInsert k v) = Map.lookup k m == Nothing
  compatS (TwoPMap m p t) (TwoPMapApply k o) | Just v ← Map.lookup k m = compatS v o
  compatS _               _                   = True

  apply (TwoPMap m p t) (TwoPMapInsert k v) | Set.member k t = TwoPMap m p t
  apply (TwoPMap m p t) (TwoPMapInsert k v) =
     -- Apply pending operations.
     let (opsM, p') = Map.updateLookupWithKey (const (const Nothing)) k p in
     let v' = maybe v (foldr (\op v → apply v op) v) opsM in
     let m' = Map.insert k v' m in
     TwoPMap m' p' t

  apply (TwoPMap m p t) (TwoPMapApply k op) | Set.member k t = TwoPMap m p t
  apply (TwoPMap m p t) (TwoPMapApply k op) =
    let (updatedM, m') = Map.updateLookupWithKey (\_ v → Just (apply v op)) k m in
     -- Add to pending if ¬inserted.
     let p' = if isJust updatedM then p else insertPending k op p in
     TwoPMap m' p' t

    apply (TwoPMap m p t)      (TwoPMapDelete k) =
      let m' = Map.delete      k m in
                                                                 lawCommut _ _ _ = ...
      let p' = Map.delete      k p in
                                                                 lawCompatCommut _ _ = ...
      let t' = Set.insert      k t in
      TwoPMap m' p' t'

                                      Fig. 6. Implementation of TwoPMap.

we can represent the first three fields as LWW registers and the last as a MultiSet , as shown in the
upper left of Figure 7. However, just collecting separate VRDT s together is not enough to show
that the result is a VRDT : we need to define a corresponding Op data type and a VRDT instance for
Event . Fortunately, since the fields of Event are VRDT instances, it is possible to derive the Event
operation and VRDT instance automatically. We use Template Haskell [Sheard and Peyton Jones
2002] to, at compile time, generate operations and VRDT instances for data types that are composed
of other VRDT s. The rest of Figure 7 shows the automatically generated code for Event . EventOp is
its operation data type and each instance method, including the lawCommut and lawCompatCommut
laws, is defined recursively for each field. Liquid Haskell automatically verifies that the generated
code satisfies the VRDT properties.

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216:20                       Yiyun Liu, James Parker, Patrick Redmond, Lindsey Kuper, Michael Hicks, and Niki Vazou

 data Event = Event {   -- user's type                      data EventOp =   -- auto-generated op
     eventTitle :: LWW Timestamp Text                         EventTitleOp (Op (LWW Timestamp Text))
   , eventDescription :: LWW Timestamp Text                 | EventDescriptionOp (Op (LWW Timestamp Text))
   , eventStartTime :: LWW Timestamp UTCTime                | EventStartTimeOp (Op (LWWU UTCTime))
   , eventRSVPs :: MultiSet Text }                          | EventRSVPsOp (Op (MultiSet Text))

instance VRDT Event where           -- auto-generated VRDT instance
  type Op Event = EventOp

  apply e (EventTitleOp op) = e {eventTitle = apply (eventTitle e) op}
  apply e ...               = ...

  compat (EventTitleOp op1) (EventTitleOp op2) = compat op1 op2
  compat ...                ...                = ...

  compatS e (EventTitleOp op) = compatS (eventTitle e) op
  compatS e ...               = ...

  lawCommut e (EventTitleOp op1) (EventTitleOp op2) = lawCommut (eventTitle e) op1 op2
  lawCommut e ...                ...                = ...

  lawCompatCommut (EventTitleOp op1) (EventTitleOp op2) = lawCompatCommut op1 op2
  lawCompatCommut ...                ...                = ...

Fig. 7. Data type Event for a calendar event (upper left), and the automatically generated operation data
type (EventOp) and VRDT instance for Event.

4.4      Verification Effort
A VRDT instance enjoys strong convergence as long as it satisfies lawCommut and lawCompatCommut ,
which must be proved for its particular implementation. Table 3 summarizes the lines of proof
and verification time for the VRDT instances we built. The development totals 5536 lines of code.
These also include duplicate definitions of Haskell functions to be amenable to verification. For
example, we redefined Data.Map to prove it satisfies the invariant that its keys are sorted, while
common list functions were redefined to be reflected, as required by extrinsic proofs. As expected,
Liquid Haskell’s PLE and SMT automation over intrinsic properties (e.g., sortedness invariant on
Data.Map ) aided proof generation. That said, there are still some issues to iron out. For example, we
had difficulties proving properties of code that makes use of typeclasses that have SMT-interpreted
theories in Liquid Haskell, e.g., set theory used by the verified Data.Map .
   The proof of TwoPMap also ran very slowly; because of the large search space (nine case splits
between the three operations), the verification took more than three hours. The long verification
time can be attributed to PLE’s expansion and the discharging of verification conditions by the
SMT solver. The bloated verification conditions consume a significant amount of memory space
as well; when verifying the insert/apply case of TwoPMap , Liquid Haskell exhausted the 16 GiB
physical memory and consumed no less than 1 GiB of the swap space. Interestingly, the proof
for TwoPMap relies on the strongConvergence property that holds for all VRDT instances. This
property is needed since the values in a TwoPMap are inhabitable by any VRDT instance. We used
 strongConvergence to show that operations in the pending buffer can be applied in any order.
   The commutativity proof for CausalTree was tricky. Each operation depends on another opera-
tion: When a node is inserted, its dependent nodes in the pending queue are recursively applied.

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Verifying Replicated Data Types with Typeclass Refinements in Liquid Haskell                                  216:21

  type DMultiSet a = (a → Integer)

  toDenotation    :: Ord a ⇒ MultiSet a → DMultiSet a
  toDenotation    (MultiSet p n) t | Just v ← Map.lookup t p = v
  toDenotation    (MultiSet p n) t | Just v ← Map.lookup t n = v
  toDenotation    _              _                           = 0

  dApply :: Eq a ⇒ DMultiSet a → MultiSetOp a → DMultiSet a
  dApply f (MultiSetOpAdd    v c) t = if t == v then f t + c else f t
  dApply f (MultiSetOpRemove v c) t = if t == v then f t - c else f t

  simulation :: x:MultiSet a → op:{MultiSetOp a | enabled x op} → t:a
             → {toDenotation (apply x op) t == dApply (toDenotation x) op t}

                                  Fig. 8. Denotational semantics of Multiset.

The resulting tree of dependencies induces significant case-based reasoning. For example, suppose
we want to show that op1 and op2 commute, where the parent of op1 is in the CausalTree and the
parent of op2 is pending. If op2 is applied first, then clearly op2 will be inserted into the pending
queue. If op1 is applied first, we cannot be sure that op2 will still be pending since descendants of
the pending operations of op1 could be the parent of op2 . Handling each case where one operation
may be the ancestor of another operation was difficult.
   Just because a data type satisfies the required VRDT typeclass laws does not mean its implementa-
tion is correct. An advantage of Liquid Haskell is that it is also possible to specify and verify higher
level properties about RDTs. To demonstrate this, we prove that the behavior of our Multiset
VRDT simulates the mathematical (denotational) semantics of multisets (Figure 8). Multiset main-
tains a positive map p and a negative map n; the former contains members with positive counts
while the latter contains members with non-positive counts. The Ops are MultiSetOpAdd and
 MultiSetOpRemove ; they shift a value between maps as its count crosses 0. We define the deno-
tation of a MultiSet to be a function from an element of the Multiset to the number of copies
of that element. This is represented by the type alias DMultiSet . The toDenotation function
is a straightforward mapping from a MultiSet to a DMultiSet that looks up the element in the
positive and negative Map ’s of the MultiSet . dApply defines how to run a MultiSet operation on
a DMultiSet by adding the number of new copies to the existing count. The DMultiSet denotation
serves as a simple specification of how we expect Multiset to operate. We prove that Multiset and
DMultiSet have the same behavior: The simulation theorem states that for all of a MultiSet ’s
enabled operations, looking up an element when you apply the operation on the MultiSet and
then convert it to its denotation returns the same result as when you first convert it to a DMultiSet
and run the operation on the denotation.
   In summary, the verification effort was strenuous, which was expected as the first real-world
case study of refined typeclasses. Nevertheless, this case study increases our confidence that Liquid
Haskell’s automation reduces proof effort and, since most of the implementation limitations we
faced are already addressed, that refined typeclasses in Liquid Haskell can actually be used to verify
sophisticated properties of real-world applications.

4.5   Discussion: Causal Delivery of RDT Operations
In typical developments of RDTs, only concurrent operations are required to commute, where
concurrent operations are those not ordered by the happens-before partial order [Lamport 1978],

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216:22                         Yiyun Liu, James Parker, Patrick Redmond, Lindsey Kuper, Michael Hicks, and Niki Vazou

Table 3. Total lines of proofs for each typeclass instance and the average verification time in seconds.
Verification times for lawCommut and lawCompatCommut are combined.

 VRDT      Property             # Lines       Verif. Time     VRDT         Property          # Lines         Verif. Time
                                  Proof        (Std. dev.)                                     Proof          (Std. dev.)
 -         strongConvergence       320    777.079 (344.937)   LWW          lawCommut              1          4.125 (1.742)
                                                                           lawCompatCommut        1
 Max       lawCommut                 1        2.565 (1.046)   Multiset     lawCommut            315       136.639 (30.309)
           lawCompatCommut           1                                     lawCompatCommut        1
 Min       lawCommut                 1       2.8194 (1.106)                simulation            72       121.849 (45.478)
           lawCompatCommut           1                        TwoPMap      lawCommut           1253    11487.275 (197.175)
 Sum       lawCommut                 1        2.185 (0.676)                lawCompatCommut        3
           lawCompatCommut           1                        CausalTree   lawCommut           2402    11242.311 (111.787)
                                                                           lawCompatCommut        1

also known as the causal order. Such an assumption may place additional requirements on the
underlying communication mechanism. For example, Shapiro et al. [2011b] assume that updates will
be delivered to operation-based RDT replicas in causal order, e.g., by employing vector clocks [Fidge
1988; Mattern 1989] and buffering received operations until all operations that causally precede
them have been applied [Birman et al. 1991]. Shapiro et al. further assume that any preconditions
that must be satisfied to enable an operation’s execution (e.g., that a key must be present in a map
if its value is to be updated) are already ensured by causal delivery.
    As mentioned earlier, our VRDT convergence proof does not rely on any assumption of causal
delivery, but in turn requires a proof that all operations commute, not just concurrent ones, as
stated by lawCommut and lawCompatCommut . We took this approach because requiring causally
ordered delivery of updates is neither necessary nor sufficient for strong convergence [Nagar
and Jagannathan 2019], and removing the assumption frees the communication mechanism from
implementing causal delivery. In turn, this shrinks the size of our trusted computing base compared
to other RDT verification work, e.g., Gomes et al. [2017].
    On the other hand, not assuming causal delivery may add complication to the RDT implemen-
tations, and increase the proof burden, in some cases. For simpler VRDT instances such as Max ,
this is not so: all operations commute anyway, so taking away the assumption of causal delivery
posed no problem for verification. For other instances, such as TwoPMap , proving lawCommut and
 lawCompatCommut required us to put more care into how the apply operation and the compat and
compatS predicates were implemented. Indeed, the pending buffer used by apply in our TwoPMap
implementation is reminiscent of the buffering mechanism that an implementation of causal deliv-
ery would use. The additional burden of these challenges can be reduced by reusing abstractions
like pending buffers and vector clocks (and their corresponding proofs) across multiple RDTs.

4.6      Performance
One advantage of a Liquid Haskell-based verification of RDTs is that the object of verification is
executable Haskell code. As such, there are no hidden costs in the translation from the verified
artifact to the executable one. To demonstrate this, we benchmark the median runtime of applying
a single operation on our VRDT implementations versus the number of operations that have been
applied to the data structure. For each VRDT , we compare against a corresponding baseline, non-
replicated Haskell data structure, and we randomly generate operations to apply. The baseline
for TwoPMap and MultiSet is the canonical Data.Map (Figure 9a). For these data types, 60% of
operations are insertions, 20% of operations are updates, and 20% of operations are deletions.
CausalTree ’s baseline is Data.List where 70% of operations are insertions and 30% of operations
are deletions (Figure 9b). We ran 11 trials, and all measurements were performed on the same

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Verifying Replicated Data Types with Typeclass Refinements in Liquid Haskell                                                                               216:23

                          Median Runtime per Operation                                               Median Runtime per Operation
                   0.8                                                                          20
                         TwoPMap   MultiSet   Baseline (Data.Map)                                      CausalTree           Baseline (Data.List)

                   0.6                                                                          15
 Runtime (ms/op)

                                                                              Runtime (ms/op)
                   0.4                                                                          10

                   0.2                                                                           5

                   0.0                                                                           0
                           10000     20000       30000        40000   50000                          10000          20000           30000          40000     50000

                                    Number of Operations                                                        Number of Operations

(a) Comparison between the TwoPMap and MultiSet (b) Comparison between the CausalTree VRDT imple-
VRDT implementations and the Data.Map baseline. mentation and the Data.List baseline.

Fig. 9. Median time per operation vs. the number of operations applied to the data structure, over 11 trials.

machine as described previously in Section 2.3. The slowdown from the Haskell baseline amounts
to the cost of the extra bookkeeping operations required by the RDTs, e.g., the maintenance of the
tombstone and pending queue in TwoPMap . We also ran experiments on Min, Max, Sum, and LWW,
and the per-operation costs were a constant 80ns; no extra bookkeeping is required for these RDTs.

4.7                Applications
We built two realistic applications that are backed by VRDT instances: a shared event planner and a
collaborative text editor. We close out this section by briefly describing these applications and some
of the other infrastructure we built beyond VRDTs to put them together.
   Message delivery and UI components. Both of our applications build on Haskell libraries we
developed for message delivery and user interfaces. In particular, we developed a message delivery
client and server to broadcast un-ordered messages from each client to all other clients. We
implemented the server using the servant library with HTTP endpoints for sending messages to
broadcast and for listening to a stream of messages from other clients. For the client, we developed
an application programming interface (API) which transparently handles network disconnections
by buffering and re-sending outgoing messages, abstracting away network interruptions.
        type Recv a = (Either String a → IO ())
        type Send a = (a → IO ())
        run :: Serialize op ⇒ ServerSettings → Recv op → IO (Send op)
The (simplified) client API entry point is run , which establishes and manages the connection to
a server. Applications provide a function that is called whenever a message is received, and run
produces a function that applications use to send messages.
   Our user interface library is based around functional reactive programming (FRP), a programming
paradigm that models values that change over time [Elliott and Hudak 1997]. FRP values are either
continuous, called behaviors, or discrete, called events. We treat replicated data types as FRP values
whose state changes as a result of actions by the local user or update messages from a remote
replica. We use Reflex3 , a Haskell FRP library, to integrate FRP applications with our message
delivery system. Any VRDT instance whose operations can be marshalled and sent over a network,
e.g., as JSON, can be used as the state of these distributed applications. We provide the below library
function, which internally calls the client API to connect a FRP application client to the server.

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  connectToStore :: (VRDT a, Serialize (Op a), MonadIO m)
                 ⇒ ServerSettings → a → Event (Op a) → m (Dynamic a)
connectToStore takes the settings of the server to connect to and an initial state. It also receives
an Event of Ops. Any time the FRP client performs an operation, the event fires and this function
sends the operation to the server. Dynamic is a special Reflex type that is both an event and a
behavior. Whenever its value changes, an event fires as well. Since connectToStore returns a
Dynamic of the current state, the FRP application automatically updates its interface whenever an
operation is received and applied to the VRDT state.
   Event planner. Our shared event planner application allows multiple users to create and manage
calendar events and RSVP to event invitations. The planner’s state ( TwoPMap UniqueId Event ) is a
two-phase map where elements are the VRDT automatically derived from the Event type described
in Figure 7. UniqueId is a pair of ClientId and an integer that is always incremented locally by the
client application. It is used to ensure that the keys are unique as required by TwoPMap . The event
planner has a terminal interface that supports viewing the list of events, creating events, updating
events, and displaying event details. Since the application’s state is a VRDT instance, updates are
quickly displayed on all clients once they receive the corresponding operations. In this application,
12 lines of code define the types associated with the application’s state, and one line of code invokes
Template Haskell to generate the operation type for Event and its VRDT instance. The rest of the 400
lines of code in the application implement the user interface. The small amount of code necessary
for managing replicated data highlights how VRDT s make it easy to build a distributed application.
  Collaborative text editor. Our collaborative text editor represents the state of the text document
being edited as a CausalTree . The majority of the code in the text editor (278 lines, out of roughly
350) is the causalTreeInput function, which has the following signature:
      causalTreeInput :: Dynamic (CausalTree id Char)
                      → Widget (Event (Op (CausalTree id Char)))
 causalTreeInput creates a Reflex Widget that builds a text box in the terminal interface that
displays the contents of the CausalTree , handles scrolling, and processes keystrokes by the user. It
takes a Dynamic of the CausalTree as input so that the view is updated when operations from the
network are received. It returns an Event of CausalTree operations that fires whenever keystrokes
update the state of the document.

5.1      Class Specifications in Object Oriented Languages
Verification-friendly, object-oriented languages, including Spec# [Barnett et al. 2005], Larch [Guttag
et al. 1985], and Dafny [Leino 2010], permit specification and verification of class invariants. Such
invariants are encoded as postconditions to class constructors and as pre- and postconditions to
methods. Unlike with typeclasses, these languages offer no automated resolution of class instances.
   Scala, closer to our work, has typeclass-like features with expressive specifications at the cost
of potentially diverging resolution. Using the trait syntax one can define what amounts to a
typeclass, as a set of behaviors (akin to Haskell methods) and their type specifications. Using the
given syntax, one can define trait instances [Team 2020]. Implicit arguments are used to express
Haskell-style type class constraints and thus enable the usage of the trait behaviors. The Scala
compiler resolves the implicits to given instances via implicit resolution [Odersky et al. 2017].
Because Scala’s type system is so expressive, implicit resolution could be incoherent and diverging.
To enforce coherence the compiler uses a disambiguation scheme that relies on a user-provided
ranking (ğ3.5 of Odersky et al. [2017]). Implicit resolution can diverge in theory (ğ4 of Odersky

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et al. [2017]) but is prevented in practice by employing various heuristics (e.g., breaking infinite
loops after five steps), at the cost of potentially brittle results.

5.2   Verification of Haskell’s Typeclass Laws
Verification of inductive properties, including per-instance typeclass laws, is possible in Haskell
using dependently typed features [Eisenberg 2016; Weirich et al. 2019; Xie et al. 2019; McBride
2002]. In work closely related to ours, Scott and Newton [2019] verify algebraic laws of typeclasses
using a singletons encoding of dependent types [Eisenberg and Weirich 2012], and they employ
generic programming to greatly reduce the proof burden. Even though their generic boilerplate
technique is very effective for verifying typeclass instances, it is unclear how the encoding of
typeclass laws interacts with the Haskell code that uses those instances. In our approach, typeclass
laws are expressed as refinement types and smoothly co-exist with refinement type specifications
of clients of typeclass methods. In fact, Scott and Newton initially attempted to use Liquid Haskell,
but at the time it was impossible since there was no support for refinement types on typeclasses.
    Haskell researchers have developed various techniques outside of Haskell itself to increase their
confidence that typeclass laws actually hold. For example, Jeuring et al. [2012] and Claessen and
Hughes [2011] used QuickCheck, the property-based random testing tool, to falsify typeclass laws.
Zeno [Sonnex et al. 2012] and HERMIT [Farmer et al. 2015] generate typeclass law proofs by
term rewriting while HALO [Vytiniotis et al. 2013] uses an axiomatic encoding to verify Haskell
contracts. HipSpec [Arvidsson et al. 2016; Claessen et al. 2012] reduces typeclass laws to an
external, automated-over-induction theorem prover. hs-to-coq [Spector-Zabusky et al. 2018]
converts Haskell typeclasses and instances to equivalent ones in Coq which can then be proved to
satisfy the respective laws.
    Compared to these approaches, our technique has three main advantages. First, our proofs are
Haskell programs, highly automated by SMT and PLE; unlike the other approaches, when proof
automation fails, the user does not need to debug the external solver. Second, our proofs co-exist
and interact with non-typeclass-specific Haskell code, so Haskell functions can use class laws to
prove further properties (as in the assoc2 example of Section 2.2). Finally, our within-Haskell
verification approach gives the developer the ability to distinguish between verified and original
(i.e., non-verified) typeclasses (as in the Semigroup example of Section 2.2) and the flexibility to
only use verified methods on critical code fragments, thus saving verification time.

5.3   Type System Expressiveness vs. Coherence of Elaboration
Typeclasses have been adopted by PureScript [Freeman 2017] and have inspired related abstractions
in many programming languages, including SML’s modules [MacQueen 1984; Dreyer et al. 2007]
and Rust’s traits [Team Mozilla Research 2017]. These languages (like vanilla Haskell) are not
designed for proving rich logical properties, e.g., by making use of dependent types. But such
simpler type systems make it possible to implement coherent typeclass resolution; for Haskell in
particular, Bottu et al. [2019] prove coherence of GHC’s elaboration by showing global uniqueness
of dictionary creations. Coherence means that decisions made by typeclass elaboration cannot
change the runtime semantics of the program, making it easier to reason about.
   Fully dependently-typed languages such as Coq [Sozeau and Oury 2008], Isabelle [Haftmann and
Wenzel 2006], Agda [Devriese and Piessens 2011], Lean [de Moura et al. 2015], and F⋆ [Martínez
et al. 2019] permit proofs of rich logical properties, and also support typeclasses. However, to
maximize expressiveness, their typeclass resolution procedures can end up being divergent or
incoherent. For example, in Coq’s typeclasses [Sozeau and Oury 2008], instantiation can diverge
and is not guaranteed to be coherent since it is not always possible to decide whether two instances
overlap [Lampropoulos and Pierce 2018].

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  In our work, we attempt to strike a balance between these two extremes. We use Liquid Haskell’s
expressiveness to prove typeclass properties, while we use GHC’s less expressive type system to
perform resolution. This design precludes having distinct typeclasses for two refined types that
have the same base type, but this limitation is easily overcome by using newtype to give distinct
names to the two refined types, as we saw at the end of Section 2.2. Moreover, our design confers
two nice benefits. First, we reuse GHC’s mature elaboration implementation. More importantly,
using elaboration on the coherent [Bottu et al. 2019], less expressive type system of Haskell, we
break the dilemma between expressiveness of the type system and coherence of elaboration.

5.4 Verifying Replicated Data Types
No verification can take place without a specification, and precisely specifying the behavior of
replicated data types is a significant challenge in itself. Most work proposing new designs and
implementations of replicated data structures (e.g., [Shapiro et al. 2011b,a; Roh et al. 2011]) does not
provide formal specifications. An exception is Attiya et al.’s work [2016], which precisely specifies
a replicated list object and gives a (non-mechanized) correctness proof.
   Burckhardt et al. [2014] proposed a comprehensive framework for formally specifying and
verifying the correctness of RDTs, using an approach inspired by axiomatic specifications of weak
shared-memory models. Although it is not obvious how to automate Burckhardt et al.’s verification
approach, the Quelea [Sivaramakrishnan et al. 2015] programming model uses the Burckhardt et al.
specification framework as a contract language embedded in Haskell that allows programmers
to attach axiomatic contracts to RDT operations; an SMT solver analyzes these contracts and
determines the weakest consistency level at which an operation can be executed while satisfying
its contract.4 Gotsman et al. [2016] develop an SMT-automatable proof rule that can establish
whether a particular choice of consistency guarantees for operations on RDTs is enough to ensure
preservation of a given application-level data integrity invariant. This approach is implemented in
Najafzadeh et al.’s CISE tool. Houshmand and Lesani’s Hamsaz system [2019] improves on CISE by
automatically synthesizing a conflict relation that specifies which operations conflict with each
other (whereas this conflict relation has to be provided as input to CISE).
   Unlike our approach, tools like Quelea, CISE, and Hamsaz do not, in and of themselves, prove
correctness properties of RDT implementations, e.g., strong convergence of replicas. Rather, they
determine whether or not it is safe to execute a given RDT operation under the assumption that that
replicas satisfy a given consistency policy (in the case of Quelea), or whether or not an application-
level invariant will be satisfied, given the consistency policies satisfied by individual operations (in
the case of CISE and Hamsaz). The goals of these lines of work are therefore complementary to
ours: we prove a property of RDT implementations (strong convergence) that such tools could then
leverage as an assumption to prove application-level properties, e.g., that a replicated bank account
never has a negative balance. Verification of these application-level properties is important because
CRDT correctness alone is not enough to ensure application correctness. (Of course, it would also
be possible to prove such application-level properties directly in Liquid Haskell as well.)
   Other works [Zeller et al. 2014; Nair et al. 2020; Gomes et al. 2017; Nagar and Jagannathan
2019] directly address proving the correctness of RDT implementations. Zeller et al. [2014] specify
and prove SC and SEC for a variety of state-based counter, register, and set CRDTs using the
Isabelle/HOL proof assistant. Nair et al. [2020] present an automatic, SMT-based verification tool

4 Implementation-wise, in contrast to our approach which uses Liquid Haskell’s solver-aided type system, Quelea is
implemented in Haskell by directly querying the underlying SMT solver through the Z3 Haskell bindings at compile time,
via Template Haskell.

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Verifying Replicated Data Types with Typeclass Refinements in Liquid Haskell                                  216:27

for specifying state-based CRDTs and verifying application-level properties of them. Neither Zeller
et al. nor Nair et al. consider operation-based CRDTs, the focus of this paper.
   Gomes et al. [2017] also use Isabelle/HOL to prove SC and SEC; like us, they focus on operation-
based CRDTs. In addition to proving that RDT operations commute for three operation-based
CRDTsÐShapiro et al.’s counters and observed-remove sets, and Roh et al.’s replicated growable
arrays [2011]ÐGomes et al. formalize in Isabelle/HOL a network model in which messages may be
lost and replicas may crash, and prove that SC and SEC hold (under any behavior of the network
model). Although one can extract executable implementations from Isabelle, our semi-automated
approach has the advantage that the programmer can write, and use, mechanically verified RDT
implementations without ever leaving Haskell. Gomes et al. bake causal delivery of updates into
their network model (following Shapiro et al. [2011b], who assume causal delivery of updates in
their proof of SEC for operation-baesd CRDTs); however, we observe that causal delivery is neither
necessary nor sufficient to guarantee strong convergence [Nagar and Jagannathan 2019].
   Nagar and Jagannathan [2019] address the question of automatically verifying strong convergence
of various operation-based CRDTs (sets, lists, graphs) under different consistency policies provided
by the underlying data store. They develop an SMT-automatable proof rule to show that all pairs of
operations either commute or are guaranteed by the consistency policy to be applied in a given
order. Given a CRDT specification, their framework will determine which consistency policy is
required for that CRDT. Their CRDT specifications are written in an abstract specification language
designed to correspond to the first-order logic theories that SMT solvers support, whereas our
verified RDTs are running Haskell code, directly usable in real applications.

We have presented an extension of Liquid Haskell to allow refinement types on typeclasses. Clients
of a typeclass may assume its methods’ refinement predicates hold, while instances of the typeclass
are obligated to prove that they do. Implementing this extension was challenged by the fact that
Liquid Haskell verifies properties of Core, the intermediate representation of the Glasgow Haskell
Compiler, but typeclasses are replaced with dictionaries (records of functions) during translation to
Core. Our implementation expands the interaction between Liquid Haskell and GHC to carry over
refinements to those dictionaries during verification, and does so in a way that takes advantage of
Haskell’s typeclass resolution procedure being coherent. We have carried out two case studies to
demonstrate the utility of our extension. First, we have used typeclass refinements to encode the
algebraic laws for the Semigroup , Monoid , Functor , Applicative , and Monad standard typeclasses,
and verified these properties hold of many of their instances. Second, we have used our extension
to construct a platform for for distributed applications based on replicated data types. We define
a typeclass whose Liquid Haskell type captures the mathematical properties of RDTs needed to
prove the property of strong convergence; implement several instances of this typeclass; and use
them to build two substantial applications.

We thank José Calderón for connecting the UCSC and the UMD/IMDEA teams. This material is
based upon work supported by the National Science Foundation under Grant Nos. CNS-1563722
and CNS-1801545; by the Defense Advanced Research Projects Agency (DARPA) under Contract
No FA8750-16-C-0022; by Comunidad de Madrid BLOQUESCM project No S2018/TCS-4339 and
Attractión de Talento No 2019-T2/TIC-13455; and by gifts from Google and Amazon Web Services.
Any opinions, findings, conclusions or recommendations expressed in this material are those of
the authors and do not necessarily reflect the views of the sponsoring organizations.

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Verifying Replicated Data Types with Typeclass Refinements in Liquid Haskell

Authors James Parker Lindsey Kuper Michael Hicks Niki Vazou Patrick Redmond Yiyun Liu

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