# ReprRepresentation Predicates

Set Implicit Arguments.
From SLF Require Import LibSepReference.
Import ProgramSyntax DemoPrograms.
From SLF Require Import Basic.
Open Scope liblist_scope.

Implicit Types n m : int.
Implicit Types p q s c : loc.
Implicit Types x : val.

# First Pass

A representation predicate is a heap predicate that describes a mutable data structure. For example, the heap predicate MList L p describes a mutable linked list whose head cell is at location p, and whose elements are described by the Coq list L. In this chapter, we'll see how to define MList, and to use this predicate for specifying and verifying functions that operate on mutable linked lists. We will also study representation predicates for mutable trees, as well as for counter functions, which feature an internal state.
As explained in the Preface, this chapter, like all the following ones, is decomposed in three parts:
• The First Pass section presents the most important ideas only.
• The More Details section presents additional material explaining in more depth the meaning and the consequences of the key results. By default, readers would eventually read all this material.
• The Optional Material section contains more advanced material, for readers who can afford to invest more time in the topic.

## Formalization of the List Representation Predicate

The implementation of mutable lists and trees involves the use of records. For simplicity, field names are represented as natural numbers. For example, to represent a list cell with a head and a tail field, we define head as the constant zero, and tail as the constant one.
Definition head : field := 0%nat.
Definition tail : field := 1%nat.
The heap predicate p ~~~>`{ head := x; tail := q } describes a record allocated at location p, with a head field storing x and a tail field storing q. The arrow ~~~> is provided by the framework for describing records. The notation `{...} is a handy notation for a list of pairs of field names and values of arbitrary types. (Further details on the formalization of records are presented in chapter Struct.)
A mutable list consists of a chain of cells. Each cell stores a tail pointer that gives the location of the next cell in the chain. The last cell stores the null value in its tail field.
The heap predicate MList L p describes a list whose head cell is at location p, and whose elements are described by the list L. This predicate is defined recursively on the structure of L:
• if L is empty, then p is the null pointer,
• if L is of the form x::L', then p is not null, and the head field of p contains x, and the tail field of p contains a pointer q such that MList L' q describes the tail of the list.
This definition is formalized as follows.
Fixpoint MList (L:list val) (p:loc) : hprop :=
match L with
| nil\[p = null]
| x::L'\ q, (p ~~~> `{ head := x; tail := q}) \* (MList L' q)
end.

## Alternative Characterizations of MList

Carrying out proofs directly with MList can be slightly cumbersome, mainly due to Coq's limited support for folding back definitions. We find it more practical to explicitly state equalities that paraphrase the definition of MList. There is one equality for the nil case, and one for the cons case.
Lemma MList_nil : p,
(MList nil p) = \[p = null].
Proof using. auto. Qed.

Lemma MList_cons : p x L',
MList (x::L') p =
\ q, (p ~~~> `{ head := x; tail := q}) \* (MList L' q).
Proof using. auto. Qed.
In addition, it is also very useful in proofs to reformulate the definition of MList L p in the form of a case analysis on whether the pointer p is null or not. This corresponds to the programming pattern if p == null then ... else. This alternative characterization of MList L p asserts that:
• if p is null, then L is empty,
• otherwise, L decomposes as x::L', the head field of p contains x, and the tail field of p contains a pointer q such that MList L' q describes the tail of the list.
The corresponding lemma, shown below, is stated using the If P then X else Y construction, which generalizes Coq's construction if b then X else Y to discriminate over a proposition P as opposed to a boolean value b. The If construct leverages (strong) classical logic. It is provided by the TLC library, just like the tactic case_if which is convenient for performing the case analysis on whether P is true or false.
Lemma MList_if : (p:loc) (L:list val),
(MList L p)
==> (If p = null
then \[L = nil]
else \ x q L', \[L = x::L']
\* (p ~~~> `{ head := x; tail := q}) \* (MList L' q)).
The proof is a bit technical, it may be skipped for a first reading.
Proof using.
Let's prove this result by case analysis on L.
intros. destruct L as [|x L'].
Case L = nil. By definition of MList, we have p = null.
{ xchange MList_nil. intros M.
We have to justify L = nil, which is trivial. The TLC case_if tactic performs a case analysis on the argument of the first visible if.
case_if. xsimpl. auto. }
Case L = x::L'. One possibility is to perform a rewrite operation using MList_cons on its first occurrence. Then using CFML the tactic xpull to extract the existential quantifiers out from the precondition: rewrite MList_cons. xpull. intros q. A more efficient approach is to use the dedicated CFML tactic xchange, which is specialized for performing updates in the current state.
{ xchange MList_cons. intros q.
Because a record is allocated at location p, the pointer p cannot be null. The lemma hrecord_not_null allows us to exploit this property, extracting the hypothesis p null. We use again the tactic case_if to simplify the case analysis.
xchange hrecord_not_null. intros N. case_if.
To conclude, it suffices to correctly instantiate the existential quantifiers. The tactic xsimpl is able to guess the appropriate instantiations.
xsimpl. auto. }
Qed.
Note that the reciprocal entailment to the one stated in MList_if is also true, but we do not need it so we do not bother proving it here. In the rest of the course, we will never unfold the definition MList, but only work using MList_nil, MList_cons, and MList_if. So, we can make MList opaque, thereby avoiding undesired simplifications.
Global Opaque MList.

## In-place Concatenation of Two Mutable Lists

The function append expects two arguments: a pointer p1 on a nonempty list, and a pointer p2 on another list (possibly empty). The function updates the last cell from the first list in such a way that its tail points to the head cell of p2. After this operation, the pointer p1 points to a list that corresponds to the concatenation of the two input lists.
let rec append p1 p2 =
if p1.tail == null
then p1.tail <- p2
else append p1.tail p2
The append function is specified and verified as shown below. The proof pattern is representative of that of many list-manipulating functions, so it is essential that the reader follow through every step of this proof.
Lemma triple_append : (L1 L2:list val) (p1 p2:loc),
p1 null
triple (append p1 p2)
(MList L1 p1 \* MList L2 p2)
(fun _MList (L1++L2) p1).
Proof using.
The induction principle provides an hypothesis for the tail of L1, i.e., for the list L1', such that the relation list_sub L1' L1 holds.
introv K. gen p1. induction_wf IH: list_sub L1. introv N. xwp.
To begin the proof, we reveal the head cell of p1. We let q1 denote the tail of p1, and L1' the tail of L1.
xchange (MList_if p1). case_if. xpull. intros x q1 L1' →.
We then reason about the case analysis.
xapp. xapp. xif; intros Cq1.
If q1' is null, then L1' is empty.
{ xchange (MList_if q1). case_if. xpull. intros →.
In this case, we set the pointer, then we fold back the head cell.
xapp. xchange <- MList_cons. }
Otherwise, if q1' is not null, we reason about the recursive call using the induction hypothesis, then we fold back the head cell.
{ xapp. xchange <- MList_cons. }
Qed.

## Smart Constructors for Linked Lists

We next introduce two smart constructors for linked lists, called mnil and mcons. The operation mnil() creates an empty list. Its implementation simply returns the value null. Its specification asserts that the return value is a pointer p such that MList nil p holds.
Definition mnil : val :=
<{ fun 'u
null }>.

Lemma triple_mnil :
triple (mnil ())
\[]
(funloc p MList nil p).
The proof uses the tactic xchanges*, which is a shorthand for xchange followed with xsimpl*.
Proof using. xwp. xval. xchanges* <- (MList_nil null). Qed.

Hint Resolve triple_mnil : triple.
Observe that the specification triple_mnil does not mention the null pointer anywhere. This specification may thus be used to specify the behavior of operations on mutable lists without having to reveal low-level implementation details that involve the null pointer.
The operation mcons x q creates a fresh list cell, with x in the head field and q in the tail field. Its implementation allocates and initializes a fresh record made of two fields. The allocation operation leverages the allocation construct written `{ head := 'x; tail := 'q } in the code. This construct is in fact a notation for an operation called val_new_hrecord_2, which we here view as a primitive operation.
Definition mcons : val :=
<{ fun 'x 'q
`{ head := 'x; tail := 'q } }>.
The operation mcons admits two specifications. The first one describes only the production of a heap predicate for the freshly allocated record.
Lemma triple_mcons : x q,
triple (mcons x q)
\[]
(funloc p p ~~~> `{ head := x ; tail := q }).
Proof using. xwp. xapp triple_new_hrecord_2; auto. xsimpl*. Qed.
The second specification assumes that the argument q comes with a list representation of the form Mlist q L, and it specifies that the function mcons produces the representation predicate Mlist p (x::L). This second specification is derivable from the first one, by folding the representation predicate MList using the tactic xchange.
Lemma triple_mcons' : L x q,
triple (mcons x q)
(MList L q)
(funloc p MList (x::L) p).
Proof using.
intros. xapp triple_mcons.
intros p. xchange <- MList_cons. xsimpl*.
Qed.
In practice, this second specification is more often useful than the first one, hence we register it in the database for xapp. It remains possible to invoke xapp triple_mcons for exploiting the first specification, where needed.
Hint Resolve triple_mcons' : triple.

## Copy Function for Lists

The function mcopy takes a mutable linked list and builds an independent copy of it, with help of the functions mnil and mcons.
let rec mcopy p =
if p == null
then mnil ()
The precondition of mcopy requires a linked list described as MList L p. The postcondition asserts that the function returns a pointer p' and a list described as MList L p', in addition to the original list MList L p . The two lists are totally disjoint and independent, as captured by the separating conjunction symbol (the star).
The proof is structure is like the previous ones. While playing the script, try to spot the places where:
• mnil produces an empty list of the form MList nil p',
• the recursive call produces a list of the form MList L' q',
• mcons produces a list of the form MList (x::L') p'.
Proof using.
intros. gen p. induction_wf IH: list_sub L.
xwp. xapp. xchange MList_if. xif; intros C; case_if; xpull.
{ intros →. xapp. xsimpl*. subst. xchange* <- MList_nil. }
{ intros x q L' →. xapp. xapp. xapp. intros q'.
xapp. intros p'. xchange <- MList_cons. xsimpl*. }
Qed.

## Length Function for Lists

The function mlength computes the length of a mutable linked list.
let rec mlength p =
if p == null
then 0
else 1 + mlength p.tail

#### Exercise: 3 stars, standard, especially useful (triple_mlength)

Prove the correctness of the function mlength.
Lemma triple_mlength : L p,
triple (mlength p)
(MList L p)
(fun r\[r = val_int (length L)] \* (MList L p)).
Proof using. (* FILL IN HERE *) Admitted.

## Alternative Length Function for Lists

In this section, we consider an alternative implementation of mlength that uses an auxiliary reference cell, called c, to keep track of the number of cells traversed so far. The list is traversed recursively, incrementing the contents of the reference once for every cell.
let rec listacc c p =
if p == null
then ()
else (incr c; listacc c p.tail)

let mlength' p =
let c = ref 0 in
listacc c p;
!c

#### Exercise: 3 stars, standard, especially useful (triple_mlength')

Prove the correctness of the function mlength'. Hint: start by stating a lemma triple_acclength expressing the specification of the recursive function acclength. Make sure to generalize the appropriate variables before applying the well-founded induction tactic. Then, complete the proof of the specification triple_mlength', using xapp triple_acclength to reason about the call to the auxiliary function.
(* FILL IN HERE *)

Lemma triple_mlength' : L p,
triple (mlength' p)
(MList L p)
(fun r\[r = val_int (length L)] \* (MList L p)).
Proof using. (* FILL IN HERE *) Admitted.

## In-Place Reversal Function for Lists

The function mrev takes as argument a pointer on a mutable list, and returns a pointer on the reverse list, that is, a list with elements in the reverse order. The cells from the input list are reused for constructing the output list. The operation is said to be "in place".
let rec mrev_aux p1 p2 =
if p2 == null
then p1
else (let p3 = p2.tail in
p2.tail <- p1;
mrev_aux p2 p3)

let mrev p =
mrev_aux p null

#### Exercise: 5 stars, standard, optional (triple_mrev)

Prove the correctness of the functions mrev_aux and mrev. Hint: here again, start by stating a lemma mtriple_rev_aux expressing the specification of the recursive function mrev_aux. Make sure to generalize the appropriate variables before applying the well-founded induction tactic. Then, complete the proof of triple_mrev, using xapp triple_mrev_aux.
(* FILL IN HERE *)

Lemma triple_mrev : L p,
triple (mrev p)
(MList L p)
(funloc q MList (rev L) q).
Proof using. (* FILL IN HERE *) Admitted.

# More Details

## Sized Stack

Module SizedStack.
In this section, we consider the implementation of a mutable stack featuring a constant-time access to the size of the stack. This stack structure consists of a 2-field record, storing a pointer on a mutable linked list, and an integer storing the length of that list. The implementation includes a function create to allocate an empty stack, a function sizeof for reading the size, and three functions push, top and pop for operating at the top of the stack.
type 'a stack = { data : 'a list; size : int }

let create () =
{ data = nil; size = 0 }

let sizeof s =
s.size

let push p x =
s.data <- mcons x s.data;
s.size <- s.size + 1

let top s =
let p = s.data in

let pop s =
let p = s.data in
let q = p.tail in
delete p;
s.data <- q in
s.size <- s.size - 1;
x
The representation predicate for the stack takes the form Stack L s, where s denotes the location of the record describing the stack, and where L denotes the list of items stored in the stack. The underlying mutable list is described as MList L p, where p is the location p stored in the first field of the record. The definition of Stack is as follows.
Definition data : field := 0%nat.
Definition size : field := 1%nat.

Definition Stack (L:list val) (s:loc) : hprop :=
\ p, s ~~~>`{ data := p; size := length L } \* (MList L p).
Observe that the predicate Stack does not expose the location of the mutable list; this location is existentially quantified in the definition. The predicate Stack also does not expose the size of the stack, as this value can be deduced by computing length L. Let's start with the specification and verification of create and sizeof.
Definition create : val :=
<{ fun 'u
`{ data := null; size := 0 } }>.

Lemma triple_create :
triple (create ())
\[]
(funloc s Stack nil s).
Proof using.
xwp. xapp triple_new_hrecord_2; auto. intros s.
unfold Stack. xsimpl*. xchange* <- (MList_nil null).
Qed.
The sizeof operation returns the contents of the size field of a stack.
Definition sizeof : val :=
<{ fun 'p
'p`.size }>.

Lemma triple_sizeof : L s,
triple (sizeof s)
(Stack L s)
(fun r\[r = length L] \* Stack L s).
Proof using.
xwp. unfold Stack. xpull. intros p. xapp. xsimpl*.
Qed.
The push operation extends the head of the list, and increments the size field.

#### Exercise: 3 stars, standard, especially useful (triple_push)

Prove the following specification for the push operation.
Lemma triple_push : L s x,
triple (push s x)
(Stack L s)
(fun uStack (x::L) s).
Proof using. (* FILL IN HERE *) Admitted.
The pop operation extracts the element at the head of the list, updates the data field to the tail of the list, and decrements the size field.

#### Exercise: 4 stars, standard, especially useful (triple_pop)

Prove the following specification for the pop operation.
Lemma triple_pop : L s,
L nil
triple (pop s)
(Stack L s)
(fun x\ L', \[L = x::L'] \* Stack L' s).
Proof using. (* FILL IN HERE *) Admitted.
The top operation extracts the element at the head of the list.
Definition top : val :=
<{ fun 's
let 'p = 's`.data in

#### Exercise: 2 stars, standard, optional (triple_top)

Prove the following specification for the top operation.
Lemma triple_top : L s,
L nil
triple (top s)
(Stack L s)
(fun x\ L', \[L = x::L'] \* Stack L s).
Proof using. (* FILL IN HERE *) Admitted.
End SizedStack.

## Formalization of the Tree Representation Predicate

In this section, we generalize the ideas presented for linked lists to binary trees. For simplicity, let us consider binary trees that store integer values in the nodes. Just like mutable lists are specified with respect to Coq's purely functional lists, mutable binary trees are specified with respect to Coq trees. Consider the following inductive definition of the type tree. A leaf is represented by the constructor Leaf, and a node takes the form Node n T1 T2, where n is an integer and T1 and T2 denote the two subtrees.
Inductive tree : Type :=
| Leaf : tree
| Node : int tree tree tree.

Implicit Types T : tree.
In a program manipulating a mutable tree, an empty tree is represented using the null pointer, and a node is represented in memory using a three-cell record. The first field, named "item", stores an integer. The other two fields, named "left" and "right", store pointers to the left and right subtrees, respectively.
Definition item : field := 0%nat.
Definition left : field := 1%nat.
Definition right : field := 2%nat.
The heap predicate p ~~~>`{ item := n; left := p1; right := p2 } describes a record allocated at location p, storing the integer n and the two pointers p1 and p2.
The representation predicate MTree T p, of type hprop, asserts that the mutable tree structure with root at location p describes the logical tree T. The predicate is defined recursively on the structure of T:
• if T is a Leaf, then p is the null pointer,
• if T is a node Node n T1 T2, then p is not null, and at location p one finds a record with field contents n, p1 and p2, with MTree T1 p1 and MTree T2 p2 describing the two subtrees.
Fixpoint MTree (T:tree) (p:loc) : hprop :=
match T with
| Leaf\[p = null]
| Node n T1 T2\ p1 p2,
(p ~~~>`{ item := n; left := p1; right := p2 })
\* (MTree T1 p1)
\* (MTree T2 p2)
end.

## Alternative Characterization of MTree

Just like for MList, it is very useful for proofs to state three lemmas that paraphrase the definition of MTree. The first two lemmas correspond to the folding/unfolding rules for leaves and nodes.
Lemma MTree_Leaf : p,
(MTree Leaf p) = \[p = null].
Proof using. auto. Qed.

Lemma MTree_Node : p n T1 T2,
(MTree (Node n T1 T2) p) =
\ p1 p2,
(p ~~~>`{ item := n; left := p1; right := p2 })
\* (MTree T1 p1) \* (MTree T2 p2).
Proof using. auto. Qed.
The third lemma reformulates MTree T p using a case analysis on whether p is the null pointer. This formulation matches the case analysis typically perform in the code of functions that operates on trees.
Lemma MTree_if : (p:loc) (T:tree),
(MTree T p)
==> (If p = null
then \[T = Leaf]
else \ n p1 p2 T1 T2, \[T = Node n T1 T2]
\* (p ~~~>`{ item := n; left := p1; right := p2 })
\* (MTree T1 p1) \* (MTree T2 p2)).
Proof using.
intros. destruct T as [|n T1 T2].
{ xchange MTree_Leaf. intros M. case_if. xsimpl*. }
{ xchange MTree_Node. intros p1 p2.
xchange hrecord_not_null. intros N. case_if. xsimpl*. }
Qed.

Opaque MTree.

For reasoning about recursive functions over trees, it is useful to exploit the well-founded order associated with "immediate subtrees". Concretely, tree_sub T1 T asserts that the tree T1 is either the left or the right subtree of the tree T. This order may be exploited for verifying recursive functions over trees using the tactic induction_wf IH: tree_sub T.
Inductive tree_sub : binary (tree) :=
| tree_sub_1 : n T1 T2,
tree_sub T1 (Node n T1 T2)
| tree_sub_2 : n T1 T2,
tree_sub T2 (Node n T1 T2).

Lemma tree_sub_wf : wf tree_sub.
Proof using.
intros T. induction T; constructor; intros t' H; inversions¬H.
Qed.

Hint Resolve tree_sub_wf : wf.
For allocating fresh tree nodes as a 3-field record, we introduce the operation mnode n p1 p2, defined and specified as follows.
Definition mnode : val :=
val_new_hrecord_3 item left right.
A first specification of mnode describes the allocation of a record.
Lemma triple_mnode : n p1 p2,
triple (mnode n p1 p2)
\[]
(funloc p p ~~~> `{ item := n ; left := p1 ; right := p2 }).
Proof using. intros. applys* triple_new_hrecord_3. Qed.
A second specification, derived from the first, asserts that, if provided the description of two subtrees T1 and T2 at locations p1 and p2, the operation mnode n p1 p2 builds, at a fresh location p, a tree described by Mtree [Node n T1 T2] p. Compared with the first specification, this second specification is said to perform "ownership transfer".

#### Exercise: 2 stars, standard, optional (triple_mnode')

Prove the specification triple_mnode' for node allocation.
Lemma triple_mnode' : T1 T2 n p1 p2,
triple (mnode n p1 p2)
(MTree T1 p1 \* MTree T2 p2)
(funloc p MTree (Node n T1 T2) p).
Proof using. (* FILL IN HERE *) Admitted.

Hint Resolve triple_mnode' : triple.

## Tree Copy

The operation tree_copy takes as argument a pointer p on a mutable tree and returns a fresh copy of that tree. It is defined in a similar way to the function mcopy for lists.
let rec tree_copy p =
if p = null
then null
else mnode p.item (tree_copy p.left) (tree_copy p.right)

#### Exercise: 3 stars, standard, optional (triple_tree_copy)

Prove the specification of tree_copy.
Lemma triple_tree_copy : p T,
triple (tree_copy p)
(MTree T p)
(funloc q (MTree T p) \* (MTree T q)).
Proof using. (* FILL IN HERE *) Admitted.

## Computing the Sum of the Items in a Tree

The operation mtreesum takes as argument the location p of a mutable tree, and it returns the sum of all the integers stored in the nodes of that tree. Consider the implementation that traverses the tree, using an auxiliary reference cell to maintain the sum of all the items visited so far.
let rec treeacc c p =
if pnull then (
c := !c + p.item;
treeacc c p.left;
treeacc c p.right)

let mtreesum p =
let c = ref 0 in
treeacc c p;
!c
The specification of mtreesum is expressed in terms of the Coq function treesum, which computes the sum of the node items stored in a logical tree. This operation is defined by recursion over the tree.
Fixpoint treesum (T:tree) : int :=
match T with
| Leaf ⇒ 0
| Node n T1 T2n + treesum T1 + treesum T2
end.

#### Exercise: 4 stars, standard, optional (triple_mtreesum)

Prove the correctness of the function mlength'. Hint: to begin with, state and prove the specification lemma triple_treeacc.
(* FILL IN HERE *)

Lemma triple_mtreesum : T p,
triple (mtreesum p)
(MTree T p)
(fun r\[r = treesum T] \* (MTree T p)).
Proof using. (* FILL IN HERE *) Admitted.

## Verification of a Counter Function with Local State

This section is concerned with the verification of counter functions, which feature internal, mutable state. A counter function f is a function that, each time it is called, returns the next integer. Concretely, the first call to f() returns 1, the second call to f() returns 2, the third call to f() returns 3, and so on.
A counter function can be implemented using a reference cell, named p, that stores the integer last returned by the counter. Initially, the contents of the reference p is zero. Each time the counter function is called, the contents of p is increased by one unit, and the new value of the contents is returned to the caller.
The function create_counter produces a fresh counter function. Concretely, create_counter() returns a counter function f independent from any other previously existing counter function.
let create_counter () =
let p = ref 0 in
(fun () → (incr p; !p))
Definition create_counter : val :=
<{ fun 'u
let 'p = ref 0 in
(fun_ 'u (incr 'p; !'p)) }>.
In this section, we present two specifications for counter functions. The first specification is the most direct, however it exposes the existence of the reference cell, revealing implementation details about the counter function. The second specification is more abstract: it hides from the client the internal representation of the counter, by means of using an abstract representation predicate.
Let us begin with the simple, direct specification. The proposition CounterSpec f p asserts that f is a counter function f whose internal state is stored in a reference cell at location p. Thus, invoking f in a state p ~~> m updates the state to p ~~> (m+1), and produces the output value m+1.
Definition CounterSpec (f:val) (p:loc) : Prop :=
m, triple (f ())
(p ~~> m)
(fun r\[r = m+1] \* p ~~> (m+1)).

Implicit Type f : val.
The function create_counter creates a fresh counter. Its precondition is empty. Its postcondition asserts that the function f being returned satisfies CounterSpec f p, and the output state contains a cell p ~~> 0 for some existentially quantified location p.
Lemma triple_create_counter :
triple (create_counter ())
\[]
(fun f\ p, (p ~~> 0) \* \[CounterSpec f p]).
Proof using.
xwp. xapp. intros p.
The proof involves the use of a new tactic, called xfun, for reasoning about local function definitions. Here, xfun gives us the hypothesis Hf that specifies the code of f
xfun. intros f Hf.
xsimpl.
{ intros m.
To reason about the call to the function f, we can exploit Hf, either explicitly by calling applys Hf, or automatically by simply calling xapp .
xapp.
xapp. xapp. xsimpl. auto. }
Qed.
Consider now a call to a counter function f, under the assumption CounterSpec f p. Assume the input state to be of the form p ~~> n for some n. Then, the call produces a state p ~~> (n+1), and returns the value n+1. The specification shown below captures this logic, for any f.
Lemma triple_apply_counter : f p n,
CounterSpec f p
triple (f ())
(p ~~> n)
(fun r\[r = n+1] \* (p ~~> (n+1))).
The specification above is in fact nothing but a reformulation of the definition of CounterSpec. Thus, its proof is straightforwards. (The proof may actually be reduced to just auto, however in general one needs to use xapp for reasoning about an abstract function.)
Proof using. introv Hf. unfolds in Hf. xapp. xsimpl*. Qed.

## Verification of a Counter Function with Local State, With Abstraction

Let us move on to the presentation of more abstract specifications. The goal is to hide from the client the existence of the reference cell used to represent the internal state of the counter functions. To that end, we introduce the heap predicate IsCounter f n, which relates a function f, its current value n, and the piece of memory state involved in the implementation of this function. This piece of memory is of the form p ~~> n, for some location p, such that CounterSpec f p holds.
Definition IsCounter (f:val) (n:int) : hprop :=
\ p, p ~~> n \* \[CounterSpec f p].
Using IsCounter, we can reformulate the specification of create_counter with a postcondition asserting that the output function f is described by the heap predicate IsCounter f 0.
This lemma is the same as the previous specification triple_create_counter , except that the reference cell p is no longer visible.
Proof using. unfold IsCounter. applys triple_create_counter. Qed.
We can also reformulate the specification of a call to a counter function. A call to f(), in a state satisfying IsCounter f n, produces a state satisfying IsCounter f (n+1), and returns n+1.

#### Exercise: 4 stars, standard, especially useful (triple_apply_counter_abstract)

Prove the abstract specification for a counter function. You will need to begin the proof using the tactic xtriple, for turning goal into a form on which xpull can be invoked to extract facts from the precondition.
Lemma triple_apply_counter_abstract : f n,
triple (f ())
(IsCounter f n)
(fun r\[r = n+1] \* (IsCounter f (n+1))).
Proof using. (* FILL IN HERE *) Admitted.
Opaque IsCounter.

# Optional Material

## Specification of a Higher-Order Repeat Operator

Consider the higher-order iterator repeat: a call to repeat f n executes n times the call f().
let rec repeat f n =
if n > 0 then (f(); repeat f (n-1))
For simplicity, let us assume for now n 0. The specification of repeat n f can be expressed in terms of an invariant, named I, describing the state in between every two calls to f. We assume that the initial state satisfies I 0. Moreover, we assume that, for every index i in the range from 0 (inclusive) to n (exclusive), a call f() can execute in a state that satisfies I i and produce a state that satisfies I (i+1). The specification below asserts that, under these two assumptions, after the n calls to f(), the final state satisfies I n. The specification takes the form:
n ≥ 0 →
Hypothesis_on_f
triple (repeat f n)
(I 0)
(fun uI n)
where Hypothesis_on_f is a proposition that captures the following specification:
i,
0 ≤ i < n
triple (f ())
(I i)
(fun uI (i+1))
The complete specification of repeat n f is thus as shown below.
Lemma triple_repeat : (I:inthprop) (f:val) (n:int),
n 0
( i, 0 i < n
triple (f ())
(I i)
(fun uI (i+1)))
triple (repeat f n)
(I 0)
(fun uI n).
Proof using.
introv Hn Hf.
To establish this specification, we carry out a proof by induction over a generalized specification, covering the case where there remains m iterations to perform, for any value of m between 0 and n inclusive.
m, 0 ≤ mn
triple (repeat f m)
(I (n-m))
(fun uI n))
We use the TLC tactics cuts, a variant of cut, to state show that the generalized specification entails the statement of triple_repeat.
cuts G: ( m, 0 m n
triple (repeat f m)
(I (n-m))
(fun uI n)).
{ applys_eq G. { fequals. math. } { math. } }
We then carry a proof by induction: during the execution, the value of m decreases step by step down to 0.
intros m. induction_wf IH: (downto 0) m. intros Hm.
xwp. xapp. xif; intros C.
We reason about the call to f
{ xapp. { math. } xapp.
We next reason about the recursive call.
xapp. { math. } { math. }
math_rewrite ((n - m) + 1 = n - (m - 1)). xsimpl. }
Finally, when m reaches zero, we check that we obtain I n.
{ xval. math_rewrite (n - m = n). xsimpl. }
Qed.

## Specification of an Iterator on Mutable Lists

The operation miter takes as argument a function f and a pointer p on a mutable list, and invokes f on each of the items stored in that list.
let rec miter f p =
if pnull then (f p.head; miter f p.tail)
The specification of miter follows the same structure as that of the function repeat from the previous section, with two main differences. The first difference is that the invariant is expressed not in terms of an index i ranging from 0 to n, but in terms of a prefix of the list L being traversed. This prefix ranges from nil to the full list L. The second difference is that the operation miter f p requires in its precondition, in addition to I nil, the description of the mutable list MList L p. This predicate is returned in the postcondition, unchanged, reflecting the fact that the iteration process does not alter the contents of the list.

#### Exercise: 5 stars, standard, especially useful (triple_miter)

Prove the correctness of triple_miter.
Lemma triple_miter : (I:list valhprop) L (f:val) p,
( x L1 L2, L = L1++x::L2
triple (f x)
(I L1)
(fun uI (L1++(x::nil))))
triple (miter f p)
(MList L p \* I nil)
(fun uMList L p \* I L).
Proof using. (* FILL IN HERE *) Admitted.
For exploiting the specification triple_miter to reason about a call to miter, it is necessary to provide an invariant I of type list val hprop, that is, of the form fun (K:list val) .... This invariant, which cannot be inferred automatically, should describe the state at the point where the iteration has traversed a prefix K of the list L. Concretely, for reasoning about a call to miter, one should exploit the tactic xapp (triple_iter (fun K ...)). An example appears next.

## Computing the Length of a Mutable List using an Iterator

The function mlength_using_miter computes the length of a mutable list by iterating over that list a function that increments a reference cell once for every item.
let mlength_using_miter p =
let c = ref 0 in
miter (fun xincr c) p;
!c

#### Exercise: 4 stars, standard, especially useful (triple_mlength_using_miter)

Prove the correctness of mlength_using_iter. Hint: as explained earlier, use xfun; intros f Hf for reasoning about the function definition, then use xapp for reasoning about a call to f.
Definition mlength_using_miter : val :=
<{ fun 'p
let 'c = ref 0 in
let 'f = (fun_ 'x incr 'c) in
miter 'f 'p;
get_and_free 'c }>.

Lemma triple_mlength_using_miter : p L,
triple (mlength_using_miter p)
(MList L p)
(fun r\[r = length L] \* MList L p).
Proof using. (* FILL IN HERE *) Admitted.

## A Continuation-Passing-Style, In-Place Concatenation Function

This section presents an example verification of a function involving "continuations". The function cps_append is similar to the function append presented previously: it also performs in-place concatenation of two lists. The main difference is that it is implemented using an auxiliary recursive function in "continuation-passing style" (CPS).
The presentation of cps_append p1 p2 is also slightly different: this operation returns a pointer p3 that describes the head of the result of the concatenation. In the general case, p3 is equal to p1, but if p1 is the null pointer, meaning that the first list is empty, then p3 is equal to p2.
The code of cps_append involves the auxiliary function cps_append_aux p1 p2 k, which invokes the continuation function k on the result of concatenating the lists at locations p1 and p2. Its code appears at first quite puzzling, because the recursive call is performed inside the continuation. It takes a good drawing and at least several minutes to figure out how the function works.
let rec cps_append_aux p1 p2 k =
if p1 == null
then k p2
else cps_append_aux p1.tail p2 (fun r ⇒ (p1.tail <- r); k p1)

let cps_append p1 p2 =
cps_append_aux p1 p2 (fun rr)

#### Exercise: 5 stars, standard, optional (triple_cps_append)

Specify and verify cps_append_aux, then verify cps_append.
(* FILL IN HERE *)

Lemma triple_cps_append : (L1 L2:list val) (p1 p2:loc),
triple (cps_append p1 p2)
(MList L1 p1 \* MList L2 p2)
(funloc p3 MList (L1++L2) p3).
Proof using. (* FILL IN HERE *) Admitted.

## Historical Notes

The representation predicate for lists appears in the seminal papers on Separation Logic: the notes by Reynolds from the summer 1999, updated the next summer [Reynolds 2000], and the publication by [O’Hearn, Reynolds, and Yang 2001]. The function cps_append was proposed in Reynolds's article as an open challenge for verification.
Most presentations of Separation Logic target partial correctness, whereas this chapters targets total correctness. The specifications of recursive functions are established using the built-in induction mechanism offered by Coq.
The specification of higher-order iterators requires higher-order Separation Logic. Being embedded in the higher-order logic of Coq, the Separation Logic that we work with is inherently higher-order. Further information on the history of higher-order Separation Logic for higher-order programs may be found in section 10.2 of http://www.chargueraud.org/research/2020/seq_seplogic/seq_seplogic.pdf.
(* 2021-08-11 15:24 *)