Automated Verification of Shape, Size and Bag Properties via
User-Defined Predicates in Separation Logic ∗Wei-Ngan China , Cristina Davida , Huu Hai Nguyena , and Shengchao Qinb†a

Department of Computer Science, National University of Singapore, SingaporebSchool of Computing, University of Teesside, United Kingdom

Despite their popularity and importance, pointer-based programs remain a major challenge
for program verification. In recent years, separation logic has emerged as a contender for formal
reasoning of pointer-based programs. Recent works have focused on specialized provers thatare mostly based on fixed sets of predicates. In this paper, we propose an automated verification
system for ensuring the safety of pointer-based programs, where specifications handled are concise, precise and expressive. Our approach uses user-definable predicates to allow programmers
to describe a wide range of data structures with their associated shape, size and bag (multi-set)properties. To support automatic verification, we design a new entailment checking procedurethat can handle well-founded predicates (that may be recursively defined) using unfold/fold reasoning. We have proven the soundness and termination of our verification system and built a
prototype system to demonstrate the viability of our approach.
1. Introduction
Separation logic supports reasoning about shared mutable data structures, i.e., structures
where an updatable field can be referenced from more than one point. Using it, the specification of heap memory operations and pointer manipulations can be made more precise (withthe help of must-aliases) and concise (with the help of frame conditions). While the foundationsof separation logic have been laid in seminal papers by Reynolds [51] and Ishtiaq and O’Hearn[25], new automated reasoning tools based on separation logic [4,19] have gradually appeared.
Several recent works [3,16] have designed specialised solvers that work for a fixed set of predicates (e.g. the predicate lseg to describe a segment of linked-list nodes). This paper focuseson an automated reasoner that works for user-defined predicates.
When designing a static reasoning mechanism for programs, two key issues that we need toconsider are automation and expressivity. Automation comes in two main flavors based either
on automated verification or on automated inference. In automated verification for imperative
programs, pre/post conditions are typically specified for each method/procedure (and an invariant given for each loop) before the reasoning system automatically checks if each given programcode is correct with respect to the given pre/post/invariant annotations. In automated inference[53], these annotations are expected to be derived by the reasoning system. Intraprocedural∗†This paper is an expanded version of our VMCAI’07 and ICECCS’07 papers.
Author of Correspondence1inference is expected to derive loop invariants, while interprocedural inference is also expected
to derive pre/post conditions for methods/procedures. While inference can be said to be more
useful in general, it must be said that automated verification is of great importance too, andit can complement inference in several ways. Firstly, programmers’ insights may be captured
via annotations to handle difficult examples that inference system may be unable to handle.
Secondly, the verification system may act as an independent checker on the inference system.
Thirdly, the verification system plays a useful role within a “proof-carrying code” system [42],where annotations of untrusted components must always be verified prior to their actual execution. Furthermore, an automated verification system allows us to explore the boundary of whatis achievable in software verification which has been identified as a Grand Challenge [23,27]
for computing research.
Expressivity is another major issue for automated reasoning systems. By allowing more
properties to be easily captured, where possible, our verification tool can support better safety
and give higher assurance on program correctness. This paper’s main goal is to raise the levelof expressivity that is possible with an automated verification system based on separation logic,
so as to support the specification and verification of shape, size and bag properties of imperative
programs. We make the following technical contributions towards this overall goal:• We provide a user-specified predicate specification mechanism that can capture a wide
range of data structures with different kinds of shapes. By shapes, we mean the expected forms of some linked data structures, such as cyclic lists, doubly-linked list or evenheight-balanced trees and sorted lists/trees. Moreover, we provide a novel mechanism tosoundly approximate each predicate describing a data structure by a heap-independent
pure formula which plays an important role in entailment proving. This allows our proof
obligations to be eventually discharged by classical provers, such as Omega or Isabelle(Secs 2 and 4).
There are data structures that are beyond the capability of the current system. This is dueto the fact that, in our approach, references between the objects of a data structure arecaptured by passing object references and fields as parameters to predicate invocations.
Consequently, our predicates cannot precisely capture data structures with non-local references, which do not have a direct relationship with fields of surrounding objects, butrather are determined by some global constraint.
• We improve the expressiveness of our automated verification tool by allowing it to capture
shape, size and bag properties from each predicate that is being used to define some data
structure. The size properties may capture sophisticated data structure invariants, suchas orderedness (for sorted list/trees) and also balanced height properties (for AVL-trees).
The bag constraints enable expressing reachability properties, as they can capture thenodes (or values) reachable inside a heap predicate. For instance, our specification cancapture all elements of a list, our verification system can then prove the preservation of
the elements inside the list after sorting. These abstract properties are important as they
are easily specified by users, but are not automatically handled by existing verification
systems based on separation logic (Sec 3).
• We design a new procedure to prove entailment of separation heap constraints. This procedure uses unfold/fold reasoning to deal with predicate definitions that describe somedata structures with sophisticated shapes/properties. While the unfold/fold mechanism
may not be totally new, we have identified sufficient conditions for soundness and termination of the procedure in the presence of user-defined recursive predicates (Sec 4).
• We have implemented a prototype verification system with the above features and havealso proven both its soundness and termination (Secs 5 and 6).
We briefly survey the state-of-the-art on research that focuses on using separation logic foreither program analysis or verification. The general framework of separation logic [51,25] ishighly expressive but undecidable. In the search for a decidable fragment of separation logic
for automated verification, Berdine et al. [3] support only a limited set of predicates without
size properties, disjunctions and existential quantifiers. Similarly, Jia and Walker [26] postpone
the handling of recursive predicates in their recent work on automated reasoning of pointerprograms. Our approach is more pragmatic as we aim for a sound and terminating formulationof automated verification via separation logic, but do not aim for completeness in the expressive
fragment that we handle. In VMCAI’07 [44], we present the proposal for a verification systemthat supports user-defined predicates with size properties. In ICECCS’07 [9], we extend the
proposal with a bag/set specification mechanism. The current paper is a journal version ofthese two papers. We have added clarification regarding the role and mechanism of implicit vsexplicit instantiation, and provided proofs on the soundness of our verification system.
On the inference front, Lee et al. [38] conduct an intraprocedural analysis for loop invariants
using grammar approximation under separation logic. Their analysis can handle a wide rangeof shape predicates with local sharing but is restricted to predicates with two parameters andwithout size properties. Gotsman et al. [19] also formulate an interprocedural shape inference
which is restricted to just the list segment shape predicate. Sims [54] extends separation logic
with fixpoint connectives and postpones substitution to express recursively defined formulae tomodel the analysis of while-loops. However, it is unclear how to check for entailment in theirextended separation logic. While our work does not address the inference/analysis challenge,
we have succeeded in providing direct support for automated verification via an expressive
specification mechanism through user-specified predicates with shape, size and bag properties.
In the following sections, we provide some details on the symbolic mechanisms used to provide
automated program verification for a procedural language with support for pointers to heapbased data structures.
This work is organized in eight sections. After the introduction, Sec 2 presents the language
and specifications. Sec 3 and Sec 4 describe the forward verification and entailment rules,
respectively, whereas their soundness is proved in Sec 5. Sec 6 summarizes the experimental
results, Sec 7 reviews some related works, and Sec 8 concludes our work. The proofs for oursoundness rules are given in the Appendix.
2. Language and Specifications
In this section, we first introduce a core imperative language and then depict our specification
language which supports user-defined shape predicates with shape, size and bag properties.
2.1. Language
We provide a simple imperative language in Figure 1. A program comprises a list of typedeclarations (tdecl∗ ) and a list of method declarations (meth∗ ). We use the supscript ∗ to help

P
tdecl
datat
field
type
τ
meth
e

::= tdecl∗ meth∗::= datat | spred::= data c { field∗ }::= type v::= c | τ::= int | bool | float | void::= type mn ((ref type v)∗ , (type v)∗ ) where mspec {e}::= null | k τ | v | v.f | v:=e | v1 .f :=v2 | new c(v ∗ )| e1 ; e2 | type v; e | mn(v ∗ ) | if v then e1 else e2

Figure 1. A Core Imperative Language
denote a list of items, for example v ∗ denotes a list of variables, v1 , .., vn . With regard to the usedterminals, c denotes the name of a user-defined data type, v, v1 , v2 stand for variable names, mn
represents a method name, k is a numeric constant, and f denotes a field name. For simplicity,
we shall assume that programs and specification formulas we use are well-typed. To simplify
the presentation but without loss of expressiveness, we allow only one-level field access like v.f(rather than v.f1 .f2 ...), and we allow only boolean variables (but not expressions) to be used as
the test conditions for conditionals. (for brevity, we use the variable v in the test condition for
conditionals to denote a boolean variable). The language supports data type declaration via
datat, and shape predicate3 definition via spred. The syntax for shape predicates is given in thenext subsection.
The following data node declarations can be expressed in our language and will be used as
examples throughout the paper. Note that they are recursive data declarations with different
numbers of fields.
data node { int val; node next }data node2 { int val; node2 prev; node2 next }data node3 { int val; node3 left; node3 right; node3 parent }Each method meth is associated with a pre/post specification mspec, the syntax of which will
be given in the next subsection. For simplicity, we assume that variable names declared insideeach method are all distinct.
Pass-by-reference parameters are marked with ref. For formalization convenience, they aregrouped together. This pass-by-reference mechanism is useful for supporting reference parameters of languages such as C♯ . As an example of pass-by-reference parameters, the following
function allows the actual parameters of {x, y} to be swapped at its callers’ sites.
void swap(ref node2 x, ref node2 y) where · · · { node2 z:=x ; x:=y ; y:=z }Furthermore, these parameters allow each iterative loop to be directly converted to an equivalent tail-recursive method, where mutation on parameters are made visible to the caller via
pass-by-reference. This technique of translating away iterative loops is standard and is helpful
in further minimising our core language.
3

Shape predicates are predicates specifying data structure shapes. Our shape predicates can also specify size andbag properties of data structures.

The standard insertion sort algorithm can be written in our language as follows:
node insert(node x, node vn) where · · ·{ if (vn.val≤x.val)
then { vn.next:=x; vn }else if (x.next=null) then
{ x.next:=vn; vn.next:=null; x }else { x.next:=insert(x.next, vn); x }}node insertion sort(node y)where · · ·{ if (y.next=null) then yelse {
y.next:=insertion sort(y.next);
insert(y.next, y)}}The insert method takes a sorted list x and a node vn that is to be inserted in the correct
location of its sorted list. The insertion sort method recursively applies itself (sorting) to
the tail of its input list, namely y.next, before inserting the first node, namely y, into its now
sorted tail. Note that we use an expression-oriented language where the last subexpression (e.g.
e2 from e1 ;e2 ) denotes the result of an expression. The missing method specifications (to be
filled in the place of · · · ), denoted by mspec, are described in the next section.
2.2. The Specification Language
Separation logic [51,25] extends Hoare logic [21] to support reasoning about shared mutabledata structures. One connective that it adds to classical logic is separation conjunction ∗. The
separation formula p1 ∗ p2 means that the heap can be split into two disjoint parts in which p1
and p2 hold, respectively. Our work will make use of this connective in our specifications. In
our approach, the verifier takes as input a command and a precondition. It then derives thestrongest postcondition upon termination of the command and checks if the strongest postcondition implies the declared postcondition.
We propose a mechanism based on predicates (that may be recursively defined) to allow
user specification of data structure shapes with size and reachability properties. Our shapespecification is based on separation logic with support for disjunctive heap states. Furthermore,each shape predicate may have pointer, integer or bag parameters to capture relevant propertiesof data structures.
Separation logic [51,25] uses the notation 7→ to denote singleton heaps, e.g. the formulap7→[val : 3, next : l] represents a singleton heap referred to by p, where [val : 3, next : l]is a data record containing fields val and next. On the other hand, separation logic also uses
predicate formulas to denote more complicated shapes, e.g. lseg(p, q) represents list segments
starting from the head pointer p and containing all the data nodes until the q pointer is reached.
In our system, we unify these two different representations into one form: p::chv∗ i. When c isa data type name, p::chv∗ i stands for a singleton heap p7→[(f:v)∗ ] where f∗ are fields of datadeclaration c. When c is a predicate name, p::chv∗ i stands for the predicate formula c(p, v∗ ) .
The reason we distinguish the first parameter from the rest is that each predicate has an implicit
parameter root as its first parameter. Effectively, this is a “root” pointer to the specified
data structure that guides data traversal and facilitates the definition of well-founded predicates(given later in this section). As an example, an acyclic linked list (that terminates with a nullreference) can be described by:root :: llhni
inv n≥0≡(root=null∧n=0)∨(∃i, m, q · root::nodehi, qi∗q::llhmi∧n=m+1)

The parameter n captures a derived value that denotes the length of the acyclic list startingfrom root pointer. The above definition asserts that an ll list can be empty (the base caseroot=null) or consists of a head data node (specified by root::nodehi, qi) and a separate taildata structure which is also an ll list (q::llhmi). The ∗ connector ensures that the head nodeand the tail reside in disjoint heaps. We also specify a default invariant n≥0 that holds for all lllists. (This invariant can be verified by checking that each disjunctive branch of the predicate
definition always implies its stated invariant. In the case of ll predicate, the disjunctive branchwith n = 0 implies the given invariant n≥0. Similarly, the n = m + 1 branch together withm≥0 from the invariant of q::llhmi also implies the given invariant n≥0.) Our predicate uses
existential quantifiers for local values and pointers, such as i, m, q. The syntax for inductive
shape predicates is given in Figure 2. For each shape definition spred, the heap-independent
invariant π over the parameters {root, v ∗ } holds for each instance of the predicate. Types neednot be given in our specification as we have an inference algorithm to automatically infer nonempty types for specifications that are well-typed. For the ll predicate, our type inference candetermine that m, n, i are of int type, while root, q are of the node type. As the constructionof type inference algorithm is quite standard for a language without polymorphism [47], its
description is omitted in the current paper. Note that arbitrary recursive shape relation can leadto non-termination in our reasoning. We avoid this problem by proposing a notion of wellfounded shape predicates, which will be discussed later in the current section.
The use of separation logic enables more precise and concise reasoning for heap memory, as it
can easily support must-aliasing and local reasoning. Regarding must-aliasing, when we specify
that x::nodeh3, yi∗y::nodeh5, xi to be a precondition of some method, we can immediately
determine that x, y are non-aliased, namely x6=y due to the use of the separation conjunction,
while x.next = y and y.next = x are must-aliases for the two fields from the heap formula.
In contrast, if we had used the formula x::nodeh3, yi∧y::nodeh5, xi, we may not be able todetermine if x, y are aliased with each other, or not. Regarding local reasoning about heapallocated data structures [16,46], it means that reasoning about a command concerns only thepart of the heap that the command reads or writes, i.e. the commands footprint. Note that local
reasoning is also present in the original formulation of Hoare logic [21] with the substitution
treatment of assignment, but is lost if heap-based data structure, and thus aliasing, is introduced
to the programming language. This loss of locality is noted as the pointer swing problem
by Hoare and He [22]. Due to local reasoning, in our system, a precondition guarantees theexistence of all memory locations that the procedure accesses. Hence, we can assume that
only the heap memory specified in the precondition of each method may be modified by themethod’s body. This makes specifications using separation logic shorter by omitting the needto write modifies clauses that are necessary in traditional specification languages, such as JML[37] or Spec♯ [1].
A more complex shape, doubly linked-list with length n, is described by:
dllhp, ni≡(root=null∧n=0)∨(root::node2h , p, qi∗q::dllhroot, n−1i)
inv n≥0

The dll shape predicate has a parameter p that represents the prev field of the first node ofthe doubly linked-list. It captures a chain of nodes that are to be traversed via the next fieldstarting from the current node root. The nodes accessible via the prev field of the root node
are not part of the dll list. This example also highlights some shortcuts we may use to make

shape specifications shorter. We use underscore to denote an anonymous variable. All thevariables (including anonymous variables) in the RHS of the shape definition, which are notparameters of the given predicate, such as q, are existentially quantified. Furthermore, terms
may be directly written as arguments of shape predicate or data node, while the root parameteron the LHS can be omitted as it is an implicit parameter that must be present for each of ourpredicate definitions.
User-definable shape predicates provide us with more flexibility than some recent automatedreasoning systems [3,5] that are designed to work with only a small set of fixed predicates. Furthermore, our shape predicates can describe not only the shape of data structures, but also theirsize and bag properties. (Examples with bag properties will be described later in Sec 2.2.1.)
This capability enables many applications, including those requiring the support for data structures with more complex invariants. For example, we may define a non-empty sorted list as
below. The predicate also tracks the length, the minimum and maximum elements of the list.
sortlhn, min, maxi ≡ (root::nodehmin, nulli ∧ min=max ∧ n=1)
∨ (root::nodehmin, qi ∗ q::sortlhn−1, k, maxi ∧ min≤k)
inv min≤max ∧ n≥1
The constraint min≤k guarantees that sortedness property is adhered between any two adjacentnodes in the list. We may now specify (and then verify) the insertion sort algorithm mentioned
earlier (see Sec 2.1 for the code) :node insert(node x, node vn) where
x::sortlhn, mi, mai ∗ vn::nodehv, i ∗→res::sortlhn+1, min(v, mi), max(v, ma)i

node insertion sort(node y)where y::llhni ∧ n>0 ∗→res::sortlhn, , i

Note that we use Φpr ∗→ Φpo to capture a precondition Φpr and a postcondition Φpo of amethod, as an abbreviation of the standard representation requires Φpr ; ensures Φpo [1,37].
A special identifier res is used in the postcondition to denote the result of the method. Laterin the verification system, we also use it to denote the value of the latest expression. The precondition of insertion sort ensures that y points to a non-empty singly linked list (the factthat the list is non-empty is given by the constraint n>0), whereas the postcondition shows thatthe output list is sorted and has the same number of nodes, n, as the input list. Regarding theinsert method, the precondition assumes that the method takes a sorted list of size n pointed
by x, and a node vn that is to be inserted in the correct location in the sorted list. The postcondition asserts that the method returns a pointer to a sorted list of size n+1, whose minimum
stored value is the minimum between the smallest value before the insertion, mi, and the newly
inserted value, v. Similarly, the maximum stored value is the maximum between the largest
value before the insertion, ma, and the newly inserted value, v.
The separation formulas we use are in a disjunctive normal form (eg. Φ, Φpr , Φpo in Figure 2).
Each disjunct consists of a ∗-separated heap constraint κ, referred to as heap part, and a heapindependent formula π, referred to as pure part. The pure part does not contain any heap nodes
and is presently restricted to pointer equality/disequality γ, Presburger arithmetic s, φ ([49])and bag constraint ϕ, φ. Furthermore, ∆ denotes a composite formula that could always besafely translated into the Φ form which captures a disjunct of heap states, denoted by κ, thatare in separation conjunction. 4 ∆ will be used in the rest of the paper for denoting an abstract
4

This translation is elaborated later in Figure 5.

spred
mspec
Φ
π
γ
κ
∆
φ
a
b
s
ϕ
B

::= chv ∗ i ≡ Φ inv π
::= Φ
Φpo
Wpr ∗→∗::= (∃v ·κ∧π)∗::= γ∧φ::= v1 =v2 | v=null | v1 6=v2 | v6=null | γ1 ∧γ2::= emp | v::chv ∗ i | κ1 ∗ κ2::= Φ | ∆1 ∨∆2 | ∆∧π | ∆1 ∗∆2 | ∃v·∆::= ϕ | b | a | φ1 ∧φ2 | φ1 ∨φ2 | ¬φ | ∃v · φ | ∀v · φ::= s1 =s2 | s1 ≤s2::= true | false | v | b1 = b2::= k int | v | k int ×s | s1 +s2 | −s | max(s1 ,s2 ) | min(s1 ,s2 ) | |B|::= v∈B | B1 =B2 | B1 ⊏B2 | ∀v∈B·φ | ∃v∈B·φ::= B1 ⊔B2 | B1 ⊓B2 | B1 −B2 | {} | {v}

Figure 2. The Specifications
state. The constraint domains φ for properties are currently chosen, due to the availability of
the corresponding solvers. However, we envisage the use of more complex constraint domains
in the future, with the adoption of new constraint solvers/provers in our system. In the rest of
the paper, we will use the following bag operators [55]: bag union ⊔, bag intersection ⊓, bag
subsumption ⊏, and bag cardinality |B|.
As we have already seen, separation formulas are used in pre/post conditions and shape definitions. In order to handle them correctly without running into unmatched residual heap nodes,
we require each separation constraint to be well-formed, as given by the following definitions:Definition 2.1 (Accessible) A variable is accessible if it is a method parameter, or it is a special
variable, either root or res.
Definition 2.2 (Reachable) Given a heap constraint κ and a pointer constraint γ, the heap
nodes in κ that are reachable from a set of pointers S can be computed by the following function.
reach(κ, γ, S)=df

reach(κ, γ, S)=df

p::chv∗ i∗reach(κ−(p::chv∗ i), γ, S∪{v|v ∈ {v∗ }, IsPtr(v)})
if ∃q ∈ S · (γ =⇒ p=q) ∧ p::chv∗ i ∈ κemp, otherwise

Note that κ−(p::chv∗ i) removes a term p::chv∗ i from κ, while IsPtr(v) determines if v is ofpointer type.
For illustration, consider the example given below:
reach(p::nodeh , qi∗q::llhni, true, {p})

=df
=df

p::nodeh , qi∗reach(q::llhni, true, {p, q})
p::nodeh , qi∗q::llhni

Definition 2.3 (Well-Formed Formulas) A separation formula is well-formed if

W• it is in a disjunctive normal form (∃v ∗ · κi ∧ γi ∧ φi )∗ where κi is for heap formula, andγi ∧ φi is for pure, i.e. heap-independent, formula, and• all occurrences of heap nodes are reachable from its accessible variables, S. That is, wehave ∀i · κi = reach(κi , γi , S), modulo associativity and commutativity of the separation
conjunction ∗.
For example, consider the separation formula p1 ::nodeh , nulli∗p2 ::nodeh , nulli and theset of accessible variables S={p1 }. The formula is not well-formed as p2 is not reachable fromp1 .
reach(p1 ::nodeh , nulli∗p2 ::nodeh , nulli, true, {p1 })=df

p1 ::nodeh , nulli

In our specifications, we allow root to appear only in predicate bodies, and res in postconditions. The primary significance of the well-formed condition is that all heap nodes of aheap constraint are reachable from accessible variables. This allows the entailment checking
procedure to correctly match nodes from the consequent with nodes from the antecedent of anentailment relation.
Arbitrary recursive shape relations can lead to non-termination in unfold/fold reasoning. To
avoid that problem, we propose to use only well-founded shape predicates in our framework.
Definition 2.4 (Well-Founded Predicates) A shape predicate is said to be well-founded if itsatisfies the following conditions:• its body is a well-formed formula,• for all heap nodes p::chv ∗ i occurring in the body, c is a data type name iff p = root.
Note that the definitions above are syntactic and can easily be enforced. An example ofwell-founded shape predicates is avl - binary tree with near balanced heights, as follows :avlhn, hi ≡ (root=null ∧ n=0 ∧ h=0)
∨ (root::node2h , p, qi ∗ p::avlhn1 , h1 i∗q::avlhn2 , h2 i∧n=1+n1 +n2 ∧ h=1+max(h1 , h2 ) ∧ −1≤h1 −h2 ≤1) inv n, h≥0In contrast, the following three shape definitions are not well-founded.
foohni ≡ root::foohmi ∧ n=m+1
goohi ≡ root::nodeh , i ∗ q::goohi
toohi ≡ root::nodeh , qi ∗ q::nodeh , i
For foo, the root identifier is bound to a shape predicate. For goo, the heap node pointed by q
is not reachable from variable root. For too, an extra data node is bound to a non-root variable. The first example may cause infinite unfolding, while the second example captures an
unreachable (junk) heap that cannot be located by our entailment procedure. The last example
illustrates the syntactic restriction imposed to facilitate termination of proof reasoning, which
can be easily overcome by introducing intermediate predicates. For example, we may use:toohi ≡ root::nodeh , qi ∗ q::tmphi
tmphi ≡ root::nodeh , iwhere tmp is the intermediate predicate added to satisfy our well-founded condition.
Our specification language allows bag/multiset properties to be specified in shape predicatesand method specifications. This extra expressivity will be illustrated next by some examples.

2.2.1. Bag of Values/Addresses
The earlier specification of sorting captures neither the in-situ reuse of memory cells nor
the fact that all the elements of the list are preserved by sorting. The reason is that the shapepredicate captures only pointers and numbers but does not capture the set of reachable nodes ina heap predicate. A possible solution to this problem is to extend our specification mechanism
to capture either a set or a bag of values. For generality and simplicity, we propose to only
use the bag (or multi-set) notation that permits duplicates, though set notation could also be
supported. The shape specifications from the previous section are revised as follows:
ll2hn, Bi ≡ (root=null∧n=0∧B={})∨(root::nodeh , qi∗q::ll2hn−1, B1 i∧B=B1 ⊔{root}) inv n≥0∧|B|=nsortl2hB, mi, mai ≡ (root::nodehmi, nulli∧mi=ma∧B={root})∨ (root::nodehmi, qi∗q::sortl2hB1 , k, mai∧B=B1 ⊔{root} ∧ mi≤k)
inv mi≤ma ∧ B6={}
Each predicate of the form ll2hn, Bi or sortl2hB, mi, mai now captures a bag of addresses
B for all the data nodes of its data structure (or heap predicate). With this extension, we canprovide a more comprehensive specification for in-situ sorting, as follows :node insert(node x, node vn) where
x::sortl2hB, mi, mai ∗ vn::nodehv, i ∗→res::sortl2hB ⊔{vn}, min(v, mi), max(v, ma)i {· · · }node insertion sort(node y) where
y::ll2hn, Bi ∧ B6={} ∗→ res::sortl2hB, , i {· · · }The precondition of insert assumes that the method takes a sorted list pointed by x and a nodevn that is to be inserted in the correct location in the sorted list. The addresses of all nodes
stored in the list pointed by x are contained in the bag B, whereas the minimum and maximum
values are represented by mi and ma, respectively. The postcondition asserts that the method
returns a pointer to a sorted list containing all nodes from the initial list, B, union with the newnode inserted, vn. In the resulted list, the minimum value stored is the minimum between thesmallest value before the insertion, mi, and the newly inserted value, v. Similarly, the maximum
value stored is the maximum between the largest value before the insertion, ma, and the newly
inserted value, v. The precondition of insertion sort ensures that y points to a non-empty
singly linked list (the fact that the list is non-empty is given by the constraint B6={}), whereas
the postcondition shows that the output list is sorted and contains the same nodes B as the input
list. We stress that this bag mechanism to capture the reachable nodes in a shape predicate isquite general. For example, instead of heap addresses, we may also revise our linked list viewto capture a bag of reachable values, and its length, as follows:
ll3hn, Bi ≡ (root=null∧n=0∧B={})∨(root::nodeha, qi∗q::ll3hn−1, B1 i∧B=B1 ⊔{a})inv n≥0 ∧ |B|=nCapturing a bag of values allows us to reason about the collection of values in a data structure,and permits relevant properties to be specified and automatically verified (when equipped with

an appropriate constraint solver), as highlighted by two examples below:
data pair{node v1; node v2}
pair partition(node x, int p) wherex::ll3hn, Ai ∗→ res::pairhr1 , r2 i ∗ r1 ::ll3hn1 , B1 i∗r2 ::ll3hn2 , B2 i
∧A=B1 ⊔B2 ∧ n=n1 + n2 ∧ (∀a∈B1 ·a≤p)∧(∀a∈B2 ·a>p){ if (x=null) then new pair(null, null)else { pair t; t:=partition(x.next, p);
if (x.val≤p) then { x.next:=t.v1; t.v1:=x }else { x.next:=t.v2; t.v2:=x };t}}bool allPos(node x) wherex::ll3hn, Bi ∗→ x::ll3hn, Bi ∧ ((∀a∈B·a≥0)∧res ∨ (∃a∈B·a<0)∧¬res){ if (x=null) then trueelse if (x.val<0) then false else allPos(x.next) }Note that both universal and existential properties over bags can be expressed. The first example returns a pair of lists that have been partitioned from a single input list according to an
integer pivot. This partition function and its pre/post specification can be used to prove the total
correctness of the quicksort algorithm. The second example uses existentially and universallyquantified formulae to determine if at least one negative number is present in an input list, or not.
Note that the postcondition of allPos preserves the fact that x is still pointing to a singly-linked
list with the length n and the bag/set of values B: x::ll3hn, Bi. These expressive specifications
can be handled by our separation logic prover in conjunction with relevant classical provers,
such as MONA [45] and Isabelle [30].
3. Automated Verification
An overview of our automated verification system is given in Figure 3.
The front-end of the system is a stanUser Supplied Items
dard Hoare-style forward verifier, which
Shape
Program
Pre/Post
PredicatesCodeinvokes the entailment prover. In thissection, we present the forward verifier
which comprises a set of forward verEntailment
Hoare-styleification rules to systematically check
ProverForward Verifierthat the precondition is satisfied at each
Automated Verification Systemcall site, and that the declared postconFigure 3: Our Approach to Verification
dition is successfully verified (assuming
the given precondition) for each method
definition. Note that we allow the precondition of a method to be false. The body of any
such method can always be successfully verified. However, such a method must not be invokedby a program at locations that are possibly reachable, as otherwise such program can never beverified. This relaxation does not affect the soundness of our verification system. The back-end
entailment prover will be given in Sec 4.

[FV−IF]⊢ {∆∧¬v ′ } e2 {∆2 }1 {∆1 }⊢ {∆} if v then e1 else e2 {∆1 ∨∆2 }⊢ {∆∧v ′ } e[FV−LOCAL]⊢ {∆} e {∆1 }⊢ {∆} {t v; e} {∃ v, v ′ ·∆1 }[FV−VAR]∆1 =(∆∧res=v ′ )⊢ {∆} v {∆1 }[FV−CONST]∆1 = (∆∧eqτ (res, k))⊢ {∆} k τ {∆1 }[FV−SEQ]⊢ {∆} e1 {∆1 } ⊢ {∆1 } e2 {∆2 }⊢ {∆} e1 ; e2 {∆2 }[FV−ASSIGN]⊢ {∆} e {∆1 } ∆2 =∃res·(∆1 ∧{v} v ′ =res)⊢ {∆} v:=e {∆2 }[FV−FIELD−READ]
type(v) = chf1 , .., fn i
∆ ⊢v ′ ::chv1 , .., vn i ∗ ∆1 fresh v1 ..vn
∆2 = ∃v1 ..vn ·(∆1 ∗ v ′ ::chv1 , .., vn i∧res=vi )⊢ {∆} v.fi {∆2 }[FV−NEW]∆1 =(∆ ∗ res::chv1′ , .., vn′ i)⊢ {∆} new c(v1 , .., vn ) {∆1 }[FV−FIELD−UPDATE]
type(v) = chf1 , .., fn i
∆ ⊢v ′ ::chv1 , .., vn i ∗ ∆1 fresh v1 ..vn
∆2 = ∃v1 ..vn ·(∆1 ∗ v ′ ::[v0′ /vi ]chv1 , .., vn i)⊢ {∆} v.fi :=v0 {∆2 }Figure 4. Some Forward Verification Rules
3.1. Forward Verifier
We use P to denote the program being checked. With pre/post conditions declared for eachmethod in P , we can apply modular verification to a method’s body using Hoare-style triples⊢ {∆1 } e {∆2 }. These are forward verification rules that expect ∆1 to be given before computing ∆2 . Note that in our system, each abstract state (e.g. ∆1 , ∆2 ) may contain both unprimed
and primed versions of program variables (e.g. x, x′ ), where unprimed version (x) denotes theinitial value and primed version (x′ ) represents the latest value of the variable. Auxiliary logicalvariables only appear as unprimed.
We now explain the operators/functions used in our verification rules. We first define a composition with update operator. Given a state ∆1 , a state change ∆2 , and a set of variables to be
updated X={x1 , . . . , xn }, the composition operator opX is defined as:∆1 opX ∆2 =df ∃ r1 ..rn · (ρ1 ∆1 ) op (ρ2 ∆2 )where r1 , . . . , rn are fresh variables; ρ1 = [ri /x′i ]ni=1 ; ρ2 = [ri /xi ]ni=1
Note that ρ1 and ρ2 are substitutions that link each latest value of x′i in ∆1 with the corresponding
initial value xi in ∆2 via a fresh variable ri . The binary operator op is either ∧ or ∗. To illustrate
the operator, consider the following example. Suppose variable x is initialized by a program to
1, which is represented by
x=1 ∧ x′ =1
The program executes the assignment x:=x+2. The updated state is computed by(x=1 ∧ x′ =1) ∧{x} (x′ =x+2) ≡ (∃r1 .x=1 ∧ r1 =1 ∧ x′ =r1 +2) ≡ (x=1 ∧ x′ =3)

which correctly reflects both the initial state and the updated state. Instances of this operator will
be used in the verification rule for assignment (as ∧{v} in [FV−ASSIGN]) and in the verification
rule for method invocation (as ∗V∪W in [FV−CALL]).
An equality operator eqτ (to be used in the rule for constant expressions [FV−CONST]) converts boolean constants and null to their corresponding integer values, but ignores floating
point constants. The function prime(V ) returns the primed form of all variables in V . The function nochange(V ) returns a formula asserting that the unprimed and primed versions of eachvariable in V are equal. These two functions will be used in the verification rule for a method
declaration ([FV−METH]). The notation [e∗ /v ∗ ] used in a few rules represents substitutions ofv ∗ by e∗ . A special case is [0/null], which denotes replacement of null by 0. We will use
the variable P later in the verification rule for method invocation ([FV−CALL]) to denote theentire program and it is used primarily to retrieve method declarations. As mentioned in lastsection, we use the special identifier res to denote the value of the latest expression during theverification.
Normalization rules for separation formulae are given in Figure 5. Note that the separation
conjunction operator ∗ is commutative, associative, and distributive over disjunction. In separation logic, the separation conjunction between a formula and a pure (i.e. heap independent)
formula is logically equivalent to a normal conjunction, i.e., ∆ ∗ π = ∆ ∧ π [51]. This justifies
the third translation rule.
(∆1 ∨ ∆2 ) ∧ π(∆1 ∨ ∆2 ) ∗ ∆(κ1 ∧π1 ) ∗ (κ2 ∧π2 )(γ1 ∧φ1 ) ∧ (γ2 ∧φ2 )(κ1 ∧π1 ) ∧ (π2 ); (∆1 ∧ π) ∨ (∆2 ∧ π)
; (∆1 ∗ ∆) ∨ (∆2 ∗ ∆); (κ1 ∗κ2 )∧(π1 ∧π2 ); (γ1 ∧γ2 ) ∧ (φ1 ∧φ2 ); κ1 ∧(π1 ∧π2 )(∃x · ∆) ∧ π; ∃y · ([y/x]∆ ∧ π)
where variable y is fresh not present in π(∃x · ∆1 ) ∗ ∆2 ; ∃y · ([y/x]∆1 ∗ ∆2 )where variable y is fresh not present in ∆2

Figure 5. Normalization Rules to the Φ-form
A part of the forward verification rules are given in Figure 4. They are used to track heap
states as precisely as possible using path-sensitivity (the conditional rule [FV−IF]), flow-sensitivity(the sequencing rule [FV−SEQ]), and context sensitivity (the method invocation rule [FV−CALL]).
Methods are verified using the rule [FV−METH], given below.
[FV−METH]
V ={vm ..vn } W=prime(V ) ∆=Φpr ∧nochange(V ) ⊢ {∆} e {∆1 } (∃W·∆1 ) ⊢Φpo ∗ ∆2
⊢ t0 mn(ref t1 v1 , .., ref tm−1 vm−1 , tm vm , .., tn vn ) where Φpr ∗→ Φpo {e}In order to verify a method’s body, the verifier assumes the method’s precondition. Furthermore, the nochange function initializes the current values of parameters to their initial (unprimed) values, since each abstract state in our verification uses primed variables to denote thelatest (current) values of program variables and the precondition Φpr is given only in terms of
unprimed variables. The initial assumption ∆ is then propagated through the body e of theprocedure. At the end of the procedure, the current (primed) values of the pass-by-value parameters are existentially quantified from the poststate ∆1 , so that their values are not visibleby the postcondition, hence by callers of the procedure. A method postcondition may captureonly part of the heap at the end of the method, leaving some leaked heap nodes in ∆2 , if any.
For the case of a programming language with garbage collector, these leaked memory nodes do
not pose any problem, as they can be automatically recovered at runtime. For a programming
language without garbage collector, the information contained in the formula ∆2 would be useful for memory leaks detection, which, as an orthogonal issue to the properties we verify in thispaper, has not been incorporated into our current system. We can disallow such memory leaks
by requiring the heap part of the formula ∆2 to be emp.
When a procedure is called, the rule [FV−CALL] ensures that its precondition is satisfied at
the call site. The pass-by-value parameters, V, are equated to their initial values through thenochange function, as their final values are not visible to the method’s callers. Afterwards, theresidual heap state, ∆1 , from checking procedure’s precondition is composed with its postcondition to become the poststate, ∆2 , of the procedure call.
[FV−CALL]m−1
t mn(ref (ti vi )i=1
, (tj vj )nj=m ) where Φpr ∗→ Φpo {e} ∈ P
V={vm ..vn }′W={v1 ..vm−1 } ρ=[vk /vk ]nk=1 ∆⊢ρΦpr ∗ ∆1 ∆2 =((∆1 ∧ nochange(V)) ∗V∪W Φpo )⊢ {∆} mn(v1 , .., vm−1 , vm , .., vn ) {∆2 }For each shape definition, [FV−PRED] checks that its given invariant is a consequence of thewell-founded heap formula.
[FV−PRED]
XPure0 (Φ) =⇒ [0/null](π)⊢ chv ∗ i = Φ inv π
As it will be explained in Sec 4, the entailment between separation formulae is reduced
to entailment between pure formulae by successively removing heap nodes from the consequent. When the consequent is pure, the heap formula in the antecedent can be soundly approximated by function XPuren , which translates a given separation formula to its pure counterpart. By an extra unfolding of its predicates, XPuren+1 function could give a more precise approximation than XPuren . The formalization for XPuren will be presented in Sec 4(Fig. 6). For illustration, we explain how [FV−PRED] rule is used to justify the invariantn≥0 for the predicate ll given in Sec 2. Let Φ be the body of the ll predicate, i.e. Φ ≡(root=null∧n=0) ∨ (root::nodeh , ri∗r::llhn−1i). Briefly, for n=0, XPure0 uses the definition of the ll predicate, replaces all occurrences of null by 0 (so that the implication checkcan be passed to a pure logic solver):XPure0 (Φ) =df ex j · (root=0∧n=0)∨(root=j∧j>0 ∧ XPure0 (r::llhn−1i))≡
ex j · (root=0∧n=0)∨(root=j∧j>0∧n−1≥0)Note that the construct ex j captures a symbolic address j that has been abstracted from theheap node root::nodeh , ri. Now, that we computed XPure0 (Φ), we can check that the invariant
is a consequence of the heap formula.
(XPure0 (Φ)⇒ [0/null]n≥0)≡ (ex j · ((root=0∧n=0) ∨ (root=j∧j>0∧n−1≥0))⇒ n≥0)
The soundness of the forward verification is formulated in Sec 5.

4. Entailment Prover
Proof obligations generated by software verification systems are typically discharged by atheorem prover, or a combination of theorem provers. For instance, ESC/Java [18] uses Simplify[15]; Spec♯ [1] is compiled to Boogie [1], which in turn uses Simplify and, recently, Z3 [14];Jahob [32,33] uses combinations of multiple theorem provers by its own combination approach.
Verification conditions generated by software verifiers typically involve multiple theories.
There are a number of different approaches to processing logical formulas involving multiple
theories. Nelson-Oppen is a well-known approach for combining quantifier-free formulas instably infinite theories over disjoint signatures (theories not sharing function or predicate symbols) [43]. Simplify [15] and CVC [56] are two widely used implementations of the approach.
Another approach is Satisfiability Modulo Theories (SMT) [2]. This approach tries to decide
whether a formula φ is satisfiable with respect to background theories for which specialized
decision procedures exist. Z3 is an efficient implementation based on this approach [14].
Our formulas are a combination of separation logic and heap-independent logics. None ofthe popular existing approaches is tailored for combinations involving separation logic. Our
approach is designed to effectively handle an important fragment of the combined logic that
commonly arises in practical software verification problems. As shown in the verification rules
in Sec 3, our verification system generates the entailment relation of formulas, abbreviated asheap entailment, of the form
(4.1)

∆A ⊢κV ∆C ∗ ∆R

which is shortcut for(4.2)κ ∗ ∆A ⊢ ∃V ·(κ ∗ ∆C ) ∗ ∆R

Our entailment prover deals with such heap entailments. To prove the heap entailment (4.1)
is to check whether heap nodes in the antecedent ∆A are sufficiently precise to cover all nodes
from the consequent ∆C , and (in case they are) to compute a residual heap state ∆R (alsoknown as “frame” in the frame inference [7]), which represents what was not consumed fromthe antecedent after matching up with the formula from the consequent. κ is the history of nodes
from the antecedent that have been used to match nodes from the consequent, V is the list of
existentially quantified variables from the consequent. Note that κ and V are derived during
the entailment proof. The entailment checking procedure is initially invoked with κ = emp andV = ∅. The entailment proving rules are explained in the rest of the section. In Section 5, we
will show that our entailment checking procedure is sound, in the sense that, if we can find a
proof (and a residual heap state ∆R ) for (4.1), then the LHS of (4.2) semantically entails the
RHS of (4.2), that is, all models of the LHS are also models of RHS. Our heap entailment may
fail in that it can not find a residual heap state ∆R for (4.1) after trying all possible entailment
proving rules. In many cases, this will indicate that there does not exist ∆R such that LHS of(4.2) semantically entails RHS of (4.2). However, since the completeness of our entailment
prover is open, we cannot rule out the possibility that there is such ∆R but our prover cannot
discover it using the current set of rules. This will be addressed in future work.
We now briefly discuss the key steps that we may use in such an entailment proof. Firstly, we
present the reduction from entailment between disjunctive formulas with existential quantifiers
to entailment between quantifier-free conjunctive formulas.

Disjunction
An entailment with a disjunctive antecedent succeeds if both disjuncts entail the consequent.
On the other hand, entailment with disjunctive consequent succeeds if either of the disjuncts
succeeds.
[ENT−LHS−OR]κ∆1 ⊢V ∆3 ∗ ∆4
∆2 ⊢κV ∆3 ∗ ∆5
∆1 ∨∆2 ⊢κV ∆3 ∗ (∆4 ∨∆5 )[ENT−RHS−OR]∆1 ⊢κV ∆i ∗ ∆Rii∈{2, 3}
∆1 ⊢κV (∆2 ∨∆3 ) ∗ ∆Ri

Existential Quantifiers
Existentially quantified variables from the antecedent are simply lifted out of the entailment
relation by replacing them with fresh variables. On the other hand, we keep track of the existential variables coming from the consequent by adding them to V .
[ENT−RHS−EX]κ∆1 ⊢V ∪{w} ([w/v]∆2 ) ∗ ∆fresh w
∆1 ⊢κV (∃ v · ∆2 ) ∗ ∆[ENT−LHS−EX][w/v]∆1 ⊢κV ∆2 ∗ ∆fresh w∃v · ∆1 ⊢κV ∆2 ∗ ∆Next, we now present reduction of entailment between two quantifier-free conjunctive formulae to entailment between two pure formulae.
Consequent with Empty Heap
The base case for our entailment checker occurs when the consequent is a pure formula, inwhich case the [ENT−EMP] rule is applied. The rule first approximates the antecedent of theentailment, including the heap formulae that have been matched previously and kept in κ. It
then invokes an off-the-shelf theorem prover to check if the approximation of the antecedent
implies the heap-independent consequent. This strategy offers us the flexibility to use different
logics for the pure part.
[ENT−EMP]
ρ=[0/null]
XPuren (κ1 ∗κ)∧ρπ1=⇒ρ∃V·π2
κ1 ∧π1 ⊢κV π2 ∗ (κ1 ∧π1 )Matching up heap nodes from the antecedent and the consequent
The rule [ENT−MATCH] works by successively matching up heap nodes that can be provenaliased.
[ENT−MATCH]∗
XPuren (p1 ::chv1 i∗κ1 ∗π1 )=⇒p1 =p2 ρ=[v1∗ /v2∗ ]κ∗p1 ::chv1∗ i
κ1 ∧π1 ∧freeEqn(ρ, V )⊢V −{vρ(κ2 ∧π2 ) ∗ ∆∗2}
p1 ::chv1∗ i∗κ1 ∧π1 ⊢κV (p2 ::chv2∗ i∗κ2 ∧π2 ) ∗ ∆XPuren (p1 ::chv1∗ i∗κ1 ∗π1 )=⇒p1 =p2 checks if p1 and p2 can be proved to be aliased based on
information in the antecedent of an entailment. If two aliased atomic heap formulas have thesame name, which means they are two objects of the same type, or two instances of the same
predicate, we require their components to be the same. The unification of the two aliased heap

formula is accomplished by the application of substitution ρ to the remaining of the consequent.
We also remove v2∗ from the set of existentially quantified variables since variables v2∗ have been
substituted away.
When a match occurs and an argument of the heap node coming from the consequent is free,the entailment procedure binds the argument to the corresponding variable from the antecedentand moves the equality to the antecedent. In our system, free variables in consequent are variables from method preconditions. These bindings play the role of parameter instantiations
during forward reasoning, and can be accumulated into the antecedent to allow the subsequent
program state (from residual heap state) to be aware of their instantiated values. This process isformalized by the function freeEqn, where V is the set of existentially quantified variables:V
freeEqn([ui /vi ]ni=1 , V ) =df let πi = (if vi ∈V then true else vi =ui ) in ni=1 πi
For soundness, we perform a preprocessing step to ensure that variables appearing as argumentsof heap nodes and predicates are i) distinct and ii) if they are free, they do not appear in theantecedent by adding (existentially quantified) fresh variables and equalities. This guarantees
that the formula generated by freeEqn does not introduce any additional constraints over existing
variables in the antecedent, as one side of each equation does not appear anywhere else in theantecedent.
As the matching process is incremental, we keep the successfully matched nodes from antecedent in κ for better precision. For example, consider the following entailment proof:(((p=null ∧ n=0) ∨ (p6=null ∧ n>0))∧n>0 ∧ m=n) =⇒ p6=null(input XPure1 )(XPure1 (p::llhni) ∧ n>0 ∧ m=n =⇒ p6=null)∆R = (n>0 ∧ m=n)(by [ENT−EMP])
p::llhnin>0 ∧ m=n ⊢p6=null ∗ ∆R
(by [ENT−MATCH])
p::llhni ∧ n>0 ⊢ p::llhmi ∧ p6=null ∗ ∆R
Had the predicate p::llhni not been kept and used, the proof would not have succeeded since werequire this predicate and n>0 to determine that p6=null. Such an entailment would be usefulwhen, for example, a list with positive length n is used as input for a function that requires anon-empty list. Note the transfer of m=n to the antecedent (and subsequently to the residual
heap state ∆R ).
Apart from the matching operation, two other essential operations that may be required in an
entailment proof are (1) unfolding a shape predicate and (2) folding some data nodes back to ashape predicate.
Unfolding a shape predicate in the antecedent
If a predicate instance in the antecedent is aliased with an object in the consequent, we unfold
it. Unfolding basically replaces the predicate instance by its predicate definition, normalizes theresulting formula, and resumes entailment checking.
Each unfolding either exposes an object that matches the object in the consequent, or reduces
the atomic heap formula in the antecedent p1 ::c1 hv1∗ i to a pure formula. The former case results
in a reduction of the consequent by using [ENT−MATCH]. In the latter case, the entailment
either (i) fails immediately since the checker is unable to find an aliased heap node or, (ii) if theresulted pure formula reveals additional aliasing information, the entailment checker continues
with a new aliased heap node from the antecedent. If the new aliased heap node is an object, amatch occurs and thus a reduction of the consequent. Otherwise a new unfolding is called on.

This process cannot go forever as every time it happens, one predicate from the antecedent isremoved and no new predicate instance is generated. Overall, the termination of the entailment
checking procedure is not compromised, as we prove in Theorem 5.5.
[ENT−UNFOLD]∗
XPuren (p1 ::c1 hv1 i∗κ1 ∗π1 )=⇒p1 =p2 IsPred(c1 )∧IsData(c2 )unfold(p1 ::c1 hv1∗ i)∗κ1 ∧π1 ⊢κV (p2 ::c2 hv2∗ i∗κ2 ∧π2 ) ∗ ∆p1 ::c1 hv1∗ i∗κ1 ∧π1 ⊢κV (p2 ::c2 hv2∗ i∗κ2 ∧π2 ) ∗ ∆[UNFOLDING]
chv ∗ i≡Φ inv π ∈ P
unfold(p::chv ∗ i) =df [p/root]Φ
The function IsPred(c) (resp. IsData(c)) returns true if c is a shape predicate (resp. a datanode). For illustration, consider the following example.
x::ll3hn, Bi∧n>2 ⊢ (∃r·x::nodehr, yi∧y6=null∧r ∈ B) ∗ ∆R
where ∆R captures the residual heap state of entailment (to be computed). Note that a predicate x::ll3hn, Bi from the antecedent and a data node x::nodehr, yi from the consequent are
co-related via the same variable x. For the entailment to succeed, we would first unfold thell3hn, Bi predicate in the antecedent ([ENT−UNFOLD]):∃q1 , v ·x::nodehv, q1 i∗q1 ::ll3hn−1, B1 i∧n>2∧B=B1 ⊔ {v}⊢ (∃r·x::nodehr, yi∧y6=null ∧ r ∈ B) ∗ ∆R
After removing the existential quantifiers ([ENT−RHS−OR], [ENT−LHS−OR]), we obtain:
x::nodehv, q1 i∗q1 ::ll3hn−1, B1 i∧n>2 ∧ B=B1 ⊔ {v}⊢ (x::nodehr, yi∧y6=null ∧ r ∈ B) ∗ ∆R
The data node in the consequent is then matched up ([ENT−MATCH]), giving:
q1 ::ll3hn−1, B1 i∧n>2∧B=B1 ⊔ {v}∧q1 =y ⊢ (q1 6=null ∧ v ∈ B) ∗ ∆R
Folding against a shape predicate in the consequent
If a predicate instance in the consequent does not have a matching predicate instance in theantecedent, we attempt to generate one by folding the antecedent.
[ENT−FOLD]
IsPred(c2 )∧IsData(c1 ) (∆ , κr , π r )∈foldκ (p1 ::c1 hv1∗ i∗κ1 ∧π1 , p2 ::c2 hv2∗ i)r{v ∗ }
XPuren (p1 ::c1 hv1∗ i∗κ1 ∗π1 )=⇒p1 =p2 (π a , π c )=splitV 2 (π r ) ∆r ∧π a ⊢κV (κ2 ∧π2 ∧π c ) ∗ ∆p1 ::c1 hv1∗ i∗κ1 ∧π1 ⊢κV (p2 ::c2 hv2∗ i∗κ2 ∧π2 ) ∗ ∆r[FOLDING]
chv i≡Φ inv π ∈ P Wi =Vi −{v ∗ , p}′κ∧π⊢κ{p,v∗ } [p/root]Φ ∗ {(∆i , κi , Vi , πi )}ni=1′foldκ (κ∧π, p::chv ∗ i) =df {(∆i , κi , ∃ Wi ·πi )}ni=1∗

When a fold against a predicate p2 ::c2 hv2∗ i is performed, the constraints related to variables
The split function projects these constraints out and differentiates those
constraints based on free variables. These constraints on free variables can be transferred to theantecedent to support the variables’ instantiations.
v2∗ are significant.

{v ∗ }
splitV 2 (

n
^
i=1

πir ) ≡

let πia , πic = if FV(πir ) ∩ v2∗ = ∅ then (true, true)
r
r
r
else
Vn ifa FV(πVn i ) c∩ V = ∅ then (πi , true) else (true, πi )in ( i=1 πi , i=1 πi )A formal definition of folding is specified by the rule [FOLDING]. Some heap nodes fromκ are removed by the entailment procedure so as to match with the heap formula of the predicate p::chv ∗ i. This requires a special version of entailment that returns three extra things: (i)consumed heap nodes, (ii) existential variables used, and (iii) final consequent. The final consequent is used to return a constraint for {v ∗ } via ∃ Wi ·πi . A set of answers is returned by thefold step as we allow it to explore multiple ways of matching up with its disjunctive heap state.
Our entailment also handles empty predicates correctly with a couple of specialised rules.
For illustration consider the following example.
x::nodeh1, q1 i∗q1 ::nodeh2, nulli∗y::nodeh3, nulli ⊢ (x::ll3hn, Bi∧n>1∧1 ∈ B) ∗ ∆R
The data node x::nodeh1, q1 i from the antecedent and the predicate x::ll3hn, Bi from the consequent are co-related by the variable x. In this case, we apply the folding operation to the first
two nodes from the antecedent against the shape predicate from the consequent. After that, amatching operation is invoked since the folded predicate now matches with the predicate in theconsequent.
The fold step may be recursively applied but is guaranteed to terminate for well-foundedpredicates as it will reduce a data node in the antecedent for each recursive invocation. This
reduction in the antecedent cannot go on forever. Furthermore, the fold operation may introduce
bindings for the parameters of the folded predicate. In the above, we obtain ∃n1 , n2 · n=n1 +1 ∧n1 =n2 +1 ∧ n2 =0 and ∃B1 , B2 · B=B1 ∪{2} ∧ B1 ={1}∪B2 ∧ B2 ={}, where n1 , n2 , B1 , B2 areexistential variables introduced by the folding process, and are subsequently eliminated. Thesebinding formulae may be transferred to the antecedent if n and B are free (for instantiation).
Otherwise, they will be kept in the consequent. Since n and B are indeed free, our folding
operation would finally derive:
y::nodeh3, nulli ∧ n = 2 ∧ B = {1, 2}⊢ (n>1 ∧ 1 ∈ B) ∗ ∆R
The effects of folding may seem similar to unfolding the predicate in the consequent. However, there is a subtle difference in their handling of bindings for free derived variables. If wechoose to use unfolding on the consequent instead, these bindings may not be transferred to theantecedent. Consider the example below where n is free :z=null ⊢ z::ll3hn, Bi ∧ n>−1 ∗ ∆R
By unfolding the predicate ll3hni in the consequent, we obtain :z=null ⊢ (z=null∧n=0∧B = {}∧n>−1)∨(∃q, v·z::nodehv, qi∗q::ll3hn−1, B1 i∧B = B1 ∪ {v}∧n>−1) ∗ ∆R

There are now two disjuncts in the consequent. The entailment fails for the second one because
it mismatches. The first one matches but the entailment still fails as the derived binding n=0
was not transferred to the antecedent.
W
W
XPuren ( (∃v ∗ ·κ∧π)∗ ) =df (∃v ∗ ·XPuren (κ)∧[0/null]π)∗XPuren (emp) =df true
XPuren (κ1 ∗ κ2 ) =df XPuren (κ1 ) ∧ XPuren (κ2 )IsData(c) fresh i
XPuren (p::chv ∗ i) =df ex i·(p=i∧i>0)

IsPred(c) freshWi∗
Invn (p::chv ∗ i) = ex j ∗ · (∃u∗ ·π)∗W
XPuren (p::chv ∗ i) =df ex i∗ · [i∗ /j ∗ ] (∃u∗ ·π)∗Figure 6. XPure : Translating to Pure FormApproximating separation formula by pure formulaIn our entailment proof, the entailment between separation formulae is reduced to entailment
between pure formulae by successively removing heap nodes from the consequent until only apure formula remains. When this happens, the heap formula in the antecedent can be soundly
approximated by function XPuren . The index n is a parameter that indicates how precise thecaller wants the approximation to be. A related function that XPuren uses is the Invn function.
This function, along with XPuren , computes and updates shape predicate invariants with more
precise invariants. The definition of Invn is given by the following rules:(chv ∗ i ≡ Φ inv π0 ) ∈ P
Inv0 (p::chv ∗ i) =df [p/root, 0/null]π0(chv ∗ i ≡ Φ inv π0 ) ∈ P
Invn (p::chv ∗ i) =df [p/root]XPuren−1 (Φ)

In the base case, when n = 0, Invn returns the user-supplied invariant. All occurrences ofnull are replaced by 0 so that we can pass the returned formula to a pure logic solver. Parameters of the predicates are replaced by the corresponding actuals. When n > 0, Invn invokes
XPuren−1 to compute a more precise invariant based on the body of the predicate.
The function XPuren (Φ), whose definitionin Fig 6, returns a sound approximation
W ∗ is given∗of Φ as a formula of the form: β ::= (( (∃v ·π) ) | (ex i · β)) 5 , where ex i construct is being
used to capture a distinct symbolic address i that has been abstracted from a heap node or
predicate Φ. XPure differentiates between symbolic addresses coming from disjoint regions of
the heap described by formulas conjoined by the separating conjunction ∗ :XPuren (κ1 ∗ κ2 ) =df XPuren (κ1 ) ∧ XPuren (κ2 )where ∧ is further normalized as follows:(ex I · φ1 ) ∧ (ex J · φ2 ) ; ex I ∪ J · φ1 ∧ φ2 ∧5Here β is defined as eitherW ∗ ∗(∃v ·π) or recursively as ex i · β.

Vi∈I,j∈J i 6= j

We illustrate how the approximation functions work by computing XPure1 (p::llhni). Let Φ
be the body of the ll predicate, i.e. Φ ≡ (root=null∧n=0) ∨ (root::nodeh , ri∗r::llhn−1i).
Inv0 (p::llhni)
XPure0 (Φ)

=df
=df
=
Inv1 (p::llhni)=df=
XPure1 (p::llhni) =df=n≥0
ex j · (root=0∧n=0)∨(root=j∧j>0 ∧ Inv0 (r::llhn−1i))
ex j · (root=0∧n=0)∨(root=j∧j>0∧n−1≥0)[p/root]XPure0 (Φ)
ex j · (p=0∧n=0) ∨ (p=j∧j>0∧n−1≥0)
ex i · [i/j]Inv1 (p::llhni)
ex i · (p=0∧n=0) ∨ (p=i∧i>0∧n−1≥0)

The following normalization rules are also used to propagate ex to the leftmost :(ex I · φ1 )∨(ex J · φ2 ) ; ex I∪J · (φ1 ∨ φ2 )∃ v · (ex I · φ)
; ex I · (∃ v · φ)
The ex i∗ construct is converted to ∃ i∗ when the formula is used as a pure formula. For instance,
the above XPure1 (p::llhni) is converted to ∃i · (p=0∧n=0) ∨ (p=i∧i>0∧n−1≥0), which isfurther reduced to (p=0∧n=0)∨(p>0∧n−1≥0).
The soundness of the heap approximation (given in the next section) ensures that it is safe
to approximate an antecedent by using XPure, starting from a given sound invariant (checkedby [FV−PRED] in Sec 3). The heap approximation also allows the possibility of obtaining amore precise invariant by unfolding the definition of a predicate one or more times, prior toapplying the XPure0 approximation with the predicate’s invariant. For example, when given
a pure invariant n≥0 for the predicate llhni, the XPure0 approximation is simply the pureinvariant n≥0 itself. However, the XPure1 approximation would invoke a single unfold before
the XPure0 approximation is applied, yielding ex i·(root=0∧n=0 ∨ root=i∧i>0∧n−1≥0),which is sound and more precise than n≥0, since the former can relate the nullness of the root
pointer with the size n of the list.
The invariants associated with shape predicates play an important role in our system. Without
the knowledge m≥0, the proof search for the entailment x::nodeh , yi ∗ y::llhmi ⊢ x::llhni ∧n≥1 would not have succeeded (failing to establish n≥1). Without a more precisely derived
invariant using XPure1 on predicate ll, the proof search for the entailment x::llhni ∧ n>0 ⊢x6=null would not have succeeded either.
Implicit vs Explicit Instantiations
In the preceding subsections, we have presented a technique for the implicit instantiationof free variables during the matching and the folding operations. This technique allows thebindings of free variables to be transferred to the antecedent during entailment proving, but
kept the substitutions for existential variables within the consequent itself. This dual treatmentof free and existential variables is meant to restrict the instantiation mechanism to only thosewhich are strictly required for entailment proving.
In this subsection, we shall provide an alternative technique for the explicit instantiation offree variables. Our main purpose is to clarify the role of the instantiation mechanism and toprovide a justification for the implicit instantiation technique being used in our current versionof entailment proving. To clarify the instantiation technique, let us consider a simple data type

which carries a pair of integer values:
data pair { int x; int y}
Let us also provide a simple method which checks if the sum of the two fields from the given
pair is positive, before returning the second field as the method’s result.
int foo(pair p) where∃a · p::pairha, bi ∧ a+b>0 ∗→ res = b{if (p.x + p.y)≤0 then error() else p.y}
If the expected precondition does not hold, the above method raises an error by calling a
special error() primitive. Furthermore, we shall assume that the pair object is leaked (orgarbage collected for some programming languages) after invoking this method. Take notethat logical variables (other than program variables) that are used by both precondition andpostcondition shall be marked as free variables, while those that are used in either preconditionor postcondition alone, shall be existentially bound. For our example, logical variable b is
free since it is used in both precondition and postcondition. In contrast, logical variable a isexistentially bound since it is only used in the precondition. Our entailment prover distinguishes
free from bound variables in order to decide which bindings may be propagated to the residual
heap state. Let us re-visit our earlier implicit instantiation technique by examining the following
entailment proof.
(c = 2 ∧ b = 3 =⇒ ∃a · a = c ∧ a+b>0) ∆R = (c = 2 ∧ b = 3)
p::pairh2,3ic = 2 ∧ b = 3 ⊢{a}(a = c ∧ a+b>0) ∗ ∆R
(by [ENT−EMP])
p::pairhc, 3i ∧ c = 2 ⊢ ∃a · p::pairha, bi ∧ a+b>0 ∗ ∆R
(by [ENT−RHS−OR], [ENT−MATCH])
During matching of the pair data nodes, the binding b = 3 is moved to the antecedent due tofree variable b, while the binding a = c for bound variable a is kept in the consequent. Hence,
only the instantiation of b = 3 is propagated to the residual heap state which can then be linked
with the postcondition res = b.
We shall now propose an alternative technique for the instantiation of free variables. To dothat, we introduce a new notation (∃v:I · ∆) that explicitly marks v as a variable to be instantiated. This new notation is meant for each consequent that has been taken from a method’s
precondition for entailment proving. For our earlier method’s precondition, we can mark thefree variable b, as follows: (∃a∃b:I · p::pairha, bi ∧ a+b>0).
With this new notation, free variables are being treated as existential variables, except that
their bindings in the consequent may be transferred to the residual heap state. To incorporate
this effect, we modify the rule for emp consequent of entailment prover to:ρ=[0/null][ENT−EMP′ ](XPuren (κ1 ∗κ)∧ρπ1=⇒ρ∃V·π2 ) B = V −{v|v:I ∈ V } πI = (∃B·π2 )κ1 ∧π1 ⊢κV π2 ∗ (κ1 ∧(π1 ∧πI ))Note that the residual heap state will now explicitly capture the bindings for free variables
that have been generated in the consequent via πI . Using this modified rule, we can perform
entailment proving for our earlier example, as follows:(c = 2 =⇒ ∃a, b · a = c ∧ b = 3 ∧ a+b>0) πI = ((∃a · (a = c ∧ b = 3 ∧ a+b>0))∆R = (c = 2 ∧ (b = 3 ∧ c>−3))p::pairh2,3ic = 2 ⊢{a,(b:I)} (a = c ∧ b = 3 ∧ a+b>0) ∗ ∆R
(by [ENT−EMP′ ])p::pairhc, 3i ∧ c = 2 ⊢ ∃a∃b:I · p::pairha, bi ∧ a+b>0 ∗ ∆R
(by [ENT−RHS−OR], [ENT−MATCH])
This technique allows any free variables to be explicitly instantiated, and is slightly moregeneral than the implicit technique which can only instantiate free variables that are present asarguments of data nodes or predicates. Nevertheless, both techniques have a similar objectiveof performing parameter instantiation for the precondition at each method call. Our current
implementation uses implicit instantiation which is simpler and incremental, but is slightly lessgeneral than the explicit instantiation technique. As a future work, we will implement also theexplicit instantiation technique and compare both techniques in more detail.
4.1. Forward Verification Example
We present the detailed verification of the first branch of the insert method from Sec 2.
While code is in bold face, program states are inside {}. Note that program variables appear
primed in formulae to denote the latest values, whereas logical variables are always unprimed.
(1). {x′ ::sortlhn, mi, mai ∗ vn′ ::nodehv, i} // [FV−METH](initialize precondition)if (vn.val ≤ x.val) then {(2). {(x′ ::nodehmi, nulli ∗ vn′ ::nodehv, i ∧ mi=ma ∧ n=1∧ v≤mi)∨ (∃q, k · x′ ::nodehmi, qi∗q::sortlhn−1, k, mai∗vn′ ::nodehv, i∧mi≤k ∧ mi≤ma ∧ n≥2 ∧ v≤mi)} // [FV−IF], [UNFOLDING]
vn.next := x;(3). {(x′ ::nodehmi, nulli ∗ vn′ ::nodehv, x′ i ∧ mi=ma ∧ n=1∧ v≤mi)∨ (∃q, k · x′ ::nodehmi, qi ∗ q::sortlhn−1, k, mai∗vn′ ::nodehv, x′ i∧mi≤k ∧ mi≤ma ∧ n≥2 ∧ v≤mi)} // [FV−FIELD−UPDATE]
vn(4). {(x′ ::nodehmi, nulli ∗ vn′ ::nodehv, x′ i ∧ mi=ma ∧ n=1∧ v≤mi ∧ res=vn′ )∨ (∃q, k · x′ ::nodehmi, qi∗ q::sortlhn−1, k, mai ∗ vn′ ::nodehv, x′ i∧mi≤k ∧ mi≤ma∧n≥2 ∧ v≤mi ∧ res=vn′ )} // [FV−VAR]}(5). {res::sortlhn+1, min(v, mi), max(v, ma)i}// [FV−METH](checking postcondition), [FOLDING]
To facilitate the illustration, we label the abstract states by (1), .., (5). The state (1) is obtainedby initialising the precondition using the nochange operation in the [FV−METH] rule. This isnecessary because all abstract program states in our system contain both unprimed and primed
variables, where primed variables denote the latest values of program variables and unprimed
variables denote either initial values of program variables or values of logical variables. The
abstract state (2) is obtained by unfolding the predicate x′ ::sortlhn, mi, mai and then distributing the formula vn′ ::nodehv, i∧v≤mi over the two disjunctions obtained by unfolding. Notethat v≤mi is obtained from the if-condition. The rule [UNFOLDING] replaces the predicate
x′ ::sortlhn, mi, mai by its definition.
The effect of the field update vn.next := x; is recorded in state (3) by changing the heap
node vn′ ::nodehv, i to vn′ ::nodehv, x′ i using the [FV−FIELD−UPDATE] rule. By the [FV−VAR]
rule, the effect of the last expression vn in the branch is recorded in state (4) using the formula
res=vn′ . The verification of this branch finishes by proving that state (4) entails the postcondition (5) according to the [FV−METH] rule. The rule [FOLDING] used in this last step folds a
formula which matches with a predicate’s definition back to the predicate. In this case, it folds
state (4) to state (5).

5. Soundness
In this section we formalize the soundness properties for both the forward verifier and theentailment prover.
5.1. Semantic Model
The semantics of our separation heap formula is similar to the model given for separation
logic [51], except that we have extensions to handle our user-defined shape predicates.
To define the model we assume sets Loc of locations (positive integer values), Val of primitive
values, with 0 ∈ Val denoting null, Var of variables (program and logical variables), andObjVal of object values stored in the heap, with c[f1 7→ν1 , .., fn 7→νn ] denoting an object value ofdata type c where ν1 , .., νn are current values of the corresponding fields f1 , .., fn . Let s, h |= Φdenote the model relation, i.e. the stack s and heap h satisfy the constraint Φ, with h, s from thefollowing concrete domains:h ∈ Heaps =df Loc ⇀fin ObjVals ∈ Stacks =df Var → Val ∪Loc

Note that each heap h is a finite partial mapping while each stack s is a total mapping, as in theclassical separation logic [51,25]. Function dom(f ) returns the domain of function f . Note that
we use 7→ to denote mappings, not the points-to assertion in separation logic, which has been
replaced by p::chv ∗ i in our notation. The model relation for separation heap formulas is defined
below. The model relation for pure formula s |= π denotes that the formula π evaluates to truein s.

Definition 5.1 [Model for Separation Constraint]s, h |= Φ1 ∨Φ2
iff s, h |= Φ1 or s, h |= Φ2s, h |= ∃v1..n ·κ∧π iff (∃ν1..n ·s[v1 7→ν1 , .., vn 7→νn ], h |= κ) and (s[v1 7→ν1 , .., vn 7→νn ] |= π)s, h |= κ1 ∗κ2
iff ∃h1 , h2 · h1 # h2 and h = h1 ·h2 ands, h1 |= κ1 and s, h2 |= κ2s, h |= emp
iff dom(h) = ∅s, h |= p::chv1..n i iff data c {t1 f1 , .., tn fn }∈P, h=[s(p)7→r],
and r=c[f1 7→s(v1 ), .., fn 7→s(vn )]or (chv1..n i≡Φ inv π)∈P and s, h |= [p/root]Φ
Note that h1 #h2 indicates h1 and h2 are domain-disjoint, i.e. dom(h1 )∩dom(h2 )=∅. h1 ·h2
denotes the union of disjoint heaps h1 and h2 . The definition for s, h |= p::chv ∗ i is split into twocases: (1) c is a data node defined in the program P; (2) c is a shape predicate defined in the
program P. In the first case, h has to be a singleton heap. In the second case, the shape predicate
c may be inductively defined. Note that the semantics for an inductively defined shape predicate
denotes an implicit notion of the least fixpoint for the set of states (s, h) satisfying the predicate[54]. The monotonic nature of our shape predicate definition guarantees the existence of thedescending chain of unfoldings, W
thus the existence of the least solution.
The heap abstraction β ::= (( (∃v ∗ ·π)∗ ) | (ex i · β)) given in last section has the following
model:
Definition 5.2 (Model for Heap Approximation)WWs, h |= (∃v ∗ ·π)∗ iff s |= (∃v ∗ ·π)∗s, h |= ex i · β
iff (p=i∧i>0)∈β and s, h−{s(p)}|=[p/i]βFurthermore, we may soundly relate a separation formula Φ and its abstraction β by the(semantic entailment) relation Φ |= β defined as follows :∀s, h · (s, h |= Φ =⇒ s, h |= β)5.2. Soundness of Verification
The soundness of our verification rules is defined with respect to a small-step operationalsemantics, which is defined using the transition relation hs, h, ei֒→hs1 , h1 , e1 i, which means if
e is evaluated in stack s, heap h, then e reduces in one step to e1 and generates new stack s1
and new heap h1 . Full definition of the relation can be found in the Appendix A. We use therelation ֒→∗ to denote the transitive closure of the transition relation ֒→. We also need to extract
the post-state of a heap constraint by:
Definition 5.3 (Poststate) Given a constraint ∆, Post(∆) captures the relation between primed
variables of ∆. That is :Post(∆) =df ρ (∃V·∆),where
V = {v1 , .., vn } denotes all unprimed program variables in ∆
ρ = [v1 /v1′ , .., vn /vn′ ]For example, given ∆ = x′ ::nodeh3, nulli∧y=5∧y′ >y+1, Post(∆) = x::nodeh3, nulli∧y>6.

Theorem 5.1 (Preservation) If⊢ {∆} e {∆2 } s, h |= Post(∆)

hs, h, ei֒→hs1 , h1 , e1 i

Then there exists ∆1 , such that s1 , h1 |= Post(∆1 ) and ⊢ {∆1 } e1 {∆2 }.
Proof: By structural induction on e. Details are in the Appendix.
Theorem 5.2 (Progress) If ⊢ {∆} e {∆2 }, and s, h |= Post(∆), then either e is a value, orthere exist s1 , h1 , and e1 , such that hs, h, ei֒→hs1 , h1 , e1 i.
Proof: By structural induction on e. Details are in the Appendix.
Theorem 5.3 (Safety) Consider a closed term e (i.e. a term with no free variables 6 ) in which
all methods have been successfully verified. Assuming unlimited stack/heap spaces and that⊢ {true} e {∆}, then either h[], [], ei֒→∗ h[], h, vi terminates with a value v that is subsumed
by the postcondition ∆, or it diverges (i.e. never terminates) h[], [], ei6֒→∗ .
Proof: Follows from Theorems 5.2 and 5.1 and an auxiliary lemma given in the appendix(Lemma B.1). Details are in the Appendix.
5.3. Soundness of Entailment
The following theorems state that our entailment proving procedure (given in Sec. 4) is soundand always terminates. Proofs are given in the Appendix.
Theorem 5.4 (Soundness) If entailment check ∆1 ⊢∆2 ∗ ∆ succeeds, we have: for all s, h, if
s, h |= ∆1 then s, h |= ∆2 ∗ ∆.
Proof: Given in the Appendix.
Theorem 5.5 (Termination) The entailment check ∆1 ⊢∆2 ∗ ∆ always terminates.
Proof: Given in the Appendix.
The soundness of the heap approximation procedure XPuren is formalized as follows:
Definition 5.4 (Sound Invariant) Given a shape predicate chv ∗ i ≡ Φ inv π, the invariant π issound if XPure0 (Φ) =⇒ [0/null]π.
Lemma 5.6 (Sound Abstraction) Given a separation constraint Φ where the invariants of thepredicates appearing in Φ are sound, we have Φ ⊢ XPuren (Φ).
Proof: Given in the Appendix.
Lemma 5.6 ensures that if sound invariants are given, it is safe to approximate an antecedentby using XPuren . It also allows the possibility of obtaining a more precise invariant by applying
XPure one or more times (i.e. using XPuren+1 instead of XPuren ).

Programs

LOCNo
size/bag

9
13

0.02
0.02

13
29

0.01
0.04

22
32

0.05
0.08

20
32
45
37
78
70

0.02
0.07
0.10
0.16
0.11
0.19

22
48

0.08
0.06

39
104

0.15
0.55

insertdeleteRed-Black Tree

114
239

2.77
2.48

insertdelete

161
278

0.97
0.95

Linked List
deletereverseCircular List
delete (first)
count
Double List
appendflatten (from tree)
Sorted List
deleteinsertion sortselection sortbubble sortmerge sortquick sort
Binary Search TreeinsertdeletePriority Queueinsertdelete max
AVL TreeOmega Isabelle MONA
Calculator Prover Prover
size/length
0.06
17.23
0.12
0.09
13.27
0.1
size + cyclic structure
0.06
14.71
0.12
0.15
31.94
0.22
size + double links
0.1
23.35
0.22
0.5
87.3
11.85
size + min + max + sortedness
0.19
34.09
1.01
0.31
80.9
5.22
0.46
135.1
1.5
0.78
127.7
1.16
0.61
142.9
8.63
0.84
14.8
15.92
min + max + sortedness
0.37
72.82
11.92
0.53
97.5
11.62
size+ height+ max-heap
0.45
192.8
2.69
11.09
648.3
642
height+ height-balanced
15.25
85.47
14
106.1
size + black-height+ height-balanced
1.64
307
7.72
653.3

15.05
14.24

4.51
26.62

Isabelle
MONA
ProverProverbag/set
16.56
0.12
12.1
0.11
bag/set + cyclic structure
17.96
0.17
39.16
0.29
bag/set + double links
22.33
0.12
54.23
0.47
bag/set+ sortedness
13.12
0.25
27.3
0.21
35.17
0.39
65.37
0.82
72.53
1.3
28.43
0.71
bag/set + sortedness
24.37
0.54
24.39
0.7
bag/set+ size+ max-heap
39.59
2.93
77.57
failedbag/set+ height+ height-balanced
119.1429.96
failed53.22
bag/set+ black-height+ height-balanced
211.56
8.63
309.3
7.51

Figure 7. Verification Times (in seconds) for Data Structures with Arithmetic and Bag/Set
Constraints

6. Implementation
We have built a prototype system using Objective Caml. The proof obligations generated
by our verification are discharged using either a constraint solver or a theorem prover. This isorganised as an option in our system and currently covers automatic provers, such as Omega
Calculator [49], Isabelle [45], and MONA [30].
Figure 7 summarizes a suite of programs tested. Tests were performed on an Intel Pentium D
6In other words, it indicates that all variables in e are locally declared in e.

3.00 GHz. For each example we report:• the number of code lines (the second column)• the timings for verifying pointer safety, where only separation/shape information is taken
into account, but not size or bag properties (the third column). These timings reveal how
much of the verification time is due to the entailment proving of pure formulas.
• the time taken by the verification process when considering separation/shape and size
properties. The pure proof obligations were discharged with either Omega (the fourthcolumn), Isabelle (the fifth column), or MONA (the sixth column). Verification time of afunction includes the time to verify all functions that it calls.
• the time taken by the verification process when considering separation/shape properties
and the bag/set of reachable values inside the data structures. The pure proof obligations
were discharged with either Isabelle (the seventh column), or MONA (the eighth column).
The average annotation cost (lines of annotations/lines of code ratio) for our examples isaround 7%. Regarding the properties we capture for each data structure, they are summarized
below:• For single-linked list, circular list and doubly-linked list, the specifications capture thesize of the list (the total number of nodes). Additionally, for circular list and doubly-linked
list, they also capture the cyclic structure and the double links, respectively. The last twocolumns contain the verification timings when capturing the set of reachable values as abag/set.
• For sorted list, we track the size of the list, the minimum (min) and the maximum (max)elements from the list. The sortedness property is expressed using the min element, asshown in Section 2.2. For the case when the specification contains the entire bag/set ofreachable values, we can directly express the sortedness property over the bag/set, without
explicitly capturing the min value (the sixth and seventh columns):
sortl3hBi ≡ (root=null∧B={})∨ (root::nodehv, qi∗q::sortl3hB1 i∧B=B1 ⊔{v} ∧ ∀ x∈B1 ·v≤x)• Binary search tree requires the elements within the tree to be in sorted order (the sortedness property). Our specification captures this property by tracking either the min/max
values within the tree (the third, fourth and fifth columns), or the entire bag/set of reachable values (the sixth and seventh columns).
• For the case of priority queue, we track the size, the height and the highest priority of theelements inside the heap, max-heap. The last two columns contain the timings obtained
when the specification captures the bag/set of reachable values.
• The specification for the AVL tree tracks the total number of nodes in the tree, denotedby the size property, and its height. Additionally, it has an invariant that ensures theheight-balanced property, meaning that the left and right subtrees are nearly balanced,

as illustrated earlier in Sec 2.2. When tracking the reachable values inside the tree withbag/set (the sixth and seventh column), in order to maintain the height-balanced invariant,
we still need to track the height of the AVL tree.
• For the red-black tree, we track the size (the total number of nodes) and the black-height(the height when considering only the black nodes). The specification also ensures theheight-balanced property, meaning that for all the nodes, each pair of left and right subtrees have the same black-height. In the last two columns we capture the set of reachable
values as a bag/set.
Next, we summarize our experience regarding the verification of arithmetic constraints andbag/set constraints, respectively. Regarding arithmetic constraints, the time required for shapeand size verification is mostly within a couple of seconds when using the Omega Calculator todischarge the proof obligations (the fourth column). In order to have a reference point for
the Omega timings, we tried solving the same constraints with two other theorem provers :Isabelle (the fifth column) and MONA (the sixth column). For the former, we only use an
automatic but incomplete tactic of the prover. The latter is an implementation of the weakmonadic second-order logics WS1S and WS2S ([17]). Therefore, first-order variables can becompared and be subjected only to addition with constants. As Presburger arithmetic ([50])allows the addition of arbitrary linear arithmetic terms, we converted its formulas into WS1S byencoding naturals as Base-2 bit strings. From our experiments we conclude that the verification
process is dominated by entailment proving of pure formulas, which is faster with a specialised
solvers, such as Omega Presburger constraints. The timings for verifying shapes only (without
size/bag proving) are benign, as reflected in the third column.
With concern to bag/set constraints, bag constraints were solved using the multiset theory of
Isabelle (the seventh column), while weak monadic second-order theory of 1 successor WS1S
from MONA was used to handle set constraints (the eighth column). Due to the incompletenessof the automatic prover that we used from Isabelle, the proof for the delete method from theavl tree failed. On the other hand, as Mona translates WS1S formulas into minimum DFAs
(Deterministic Finite Automata), the translation may cause a state-space explosion. In our case,we confronted such a problem when verifying the method for deleting the root of a priority
heap, delete max, for which the size of the corresponding automaton exceeded the available
memory space. From the experiments we can conclude that, when the verification succeeds, it
is faster with Mona than with Isabelle.
One remark regarding the verification of bag/set constraints is that, when using Mona fordischarging the proof obligations, the properties verified are less precise than with Isabelle.
This is due to the fact that from Isabelle we employ the bag (multiset) theory, whereas in Mona
we can only use WS1S for set constraints. For illustration, let us consider the specification of
the insert method for a singly-linked list:
root :: llhBi≡(root=null∧B={})∨(root::nodehv, qi∗q::llhB1 i∧B=B1 ⊔{v})

void insert(node x, int a) wherex::llhBi ∗→ x::llhB1 i ∧ B1 =B ⊔{a}For the predicate llhBi, B denotes the bag/list of values stored inside the corresponding list.
When verifying the method using Isabelle, the constraint B1 =B ⊔{a} specifies that only one
new node with value a was inserted into the list. However, after verifying the same method
using Mona, we can only conclude that at least one node with the value a was inserted into thelist.
To speed up the verification process, we have undertaken some performance engineering andrerun the tests. One direction was motivated by the observation that the verification process isdominated by the entailment proving of pure formulas. Consequently, in order to speed up theverification process, we have to speed up the calls to the external solvers. One technique of
simplifying these calls, is to replace a single such call with multiple calls corresponding to each
disjunct from the antecedent and each conjunct from the consequent, respectively. Following
from the [ENT−LHS−OR] rule in Section 4 for handling disjunction on the LHS during the entailment of separation logic formulas, we have applied the same idea for entailments between
pure formulas. Our experiments have showed that performing multiple calls to the solvers withsmaller formulas is faster than performing only one call with a bigger formula to be discharged.
Apart from the aforementioned speeding up technique, an important future work is to design asafe decomposition strategy for breaking larger predicates, into a number of smaller orthogonal
predicates for modular verification. We expect code modularity, decomposed shape views andmulti-core parallelism to be important techniques for performance engineering of automatedverification system.
The programs we have tested are written using data structures with sophisticated shape, sizeand bag properties, such as sorted lists, sorted trees, priority queues, balanced trees. Our approach is general enough to handle such interesting data structure properties in an uniform way.
Note that our system currently cannot handle map, sequence or non-linear properties as suchproperties would require specific provers for them. The examples we have tested so far in our experiments are small to medium size programs. The success in verifying such programs confirms
the viability of our approach, and allows us to use our system to verify data structure libraries.
For large-size programs, significant effort would be required, e.g. in providing user-annotations
on method specifications and loop invariants. We envisage that inference mechanisms would be
useful to help reduce user-annotations and improve level of automation.
There are also data structures that are beyond the capability of the current system. Since thereferences between the objects of a data structure are captured by passing object references andfields as parameters to predicate invocations, our predicates cannot precisely capture data structures with non-local references. For instance, certain data structures with fields described byfield constraints [59], or those with probabilistically determined fields, such as skip lists [48]are currently not captured by our predicates. These data structures have a common property:
certain pointer fields of the objects are non-local in that they do not have a direct relationshipwith fields of surrounding objects, but rather are determined by some global constraint.
7. Related Work7.1. Formalisms for Shape Checking/Analysis
Many formalisms for shape analysis are proposed for checking user programs’ intricate manipulations of shapely data structures. One well-known work is the Pointer Assertion Logic[41] by Moeller and Schwartzbach, which is a highly expressive mechanism to describe in-variants of graph types [31]. The Pointer Assertion Logic Engine (PALE) uses Monadic
Second-Order Logic over Strings and Trees as the underlying logic and the tool MONA [30] as
the prover. PALE invariants are not designed to handle arithmetic, hence it is not possible toencode height-balanced priority queue in PALE. Moreover, PALE is unsound in handling procedure calls [41], whereas we would like to have a sound verifier. Harwood et al. [20] describea UTP theory for objects and sharing in languages like Java or C++. Their work focuses ona denotational model meant to provide a semantical foundation for refinement-based reasoning or Hoare-style axiomatic reasoning. Our work focuses more on practical verification forheap-manipulating programs.
In an object-oriented setting, the Dafny language [39] uses dynamic frames (introduced by
Kassios [28]) in its specifications. The term frame refers to a set of memory locations, and an
expression denoting a frame is dynamic in the sense that as the program executes, the set of
locations denoted by the frame can change. A dynamic frame is thus denoted by a set-valued
expression (in particular, a set of object references), and this set is idiomatically stored in a field.
Methods in Dafny use modifies and reads clauses, which frame the modifications of methods
and dependencies of functions. By comparison, separation logic provides a reasoning logic thathides the explicit representation of dynamic frames.
For shape inference, Sagiv et al. [53] present a parameterized framework, called TVLA,
using 3-valued logic formulae and abstract interpretation. Based on the properties expected ofdata structures, programmers must supply a set of predicates to the framework which are thenused to analyse that certain shape invariants are maintained.
However, most of these techniques are focused on analysing shape invariants, and do not
attempt to track the size and bag properties of complex data structures. An exception is thequantitative shape analysis of Rugina [52] where a data flow analysis is proposed to computequantitative information for programs with destructive updates. By tracking unique points-toreference and its height property, their algorithm is able to handle AVL-like tree structures.
Even then, the author acknowledge the lack of a general specification mechanism for handling
arbitrary shape/size properties.
7.2. Size Properties
In another direction of research, size properties are mostly explored for declarative languages[24,60,10] as the immutability property makes their data structures easier to analyse statically.
Size analysis is also extended to object-based programs [11] but is restricted to tracking either
size-immutable objects that can be aliased and size-mutable objects that are unaliased, with nosupport for complex shapes.
The Applied Type System (ATS) [8] is proposed for combining programs with proofs. In
ATS, dependent types for capturing program invariants are extremely expressive and can capture many program properties with the help of accompanying proofs. Using linear logic, ATS
may also handle mutable data structures with sharing in a precise manner. However, users must
supply all expected properties, and precisely state where they are to be applied, with ATS playing the role of a proof-checker. In comparison, we use a more limited class of constraint forshape, size and bag analysis but support automated modular verification.
7.3. Set/Bag Properties
Set-based analysis is proposed to verify data structure consistency properties in the work of
Kuncak et al. [34], where a decision procedure is given for a first order theory that combinesset and Presburger arithmetic. This result may be used to build a specialised mixed constraint
solver but it currently has high algorithmic complexity.
Lahiri and Qadeer [35] report an intra-procedural reachability analysis for well-foundedlinked lists using first-order axiomatization. Reachability analysis is related to set/bag property that we capture but implemented by transitive closure at the predicate level.
7.4. Unfold/Fold Mechanism
Unfold/fold techniques are originally used for program transformation [6] on purely functional programs. A similar technique called unroll/roll is later used in alias types [58] to manually witness the isomorphism between a recursive type and its unfolding. Here, each unroll/roll
step must be manually specified by programmer, in contrast to our approach which applies thesesteps automatically during entailment checking.
An automated procedure that uses unroll/roll is given by Berdine et al. [3], but it is hardwired
to work for only lseg and tree predicates. Furthermore, it performs rolling by unfolding
a predicate in the consequent which may miss bindings on free variables. Our unfold/foldmechanism is general, automatic and terminates for heap entailment checking.
7.5. Classical Verifiers
Program verifiers that are based on Hoare-style logic have been around longer than those
based on separation logic. We describe some major efforts in this direction.
ESC/Java. Extended Static Checking for Java (ESC/Java) [18], developed at Compaq Systems Research Center, aims to detect more errors than “traditional” static checking tools, suchas type checkers, but is not designed to be a program verification system. The stated goals of
ESC/Java are scalability and usability. For that, it forgoes soundness for the potential benefits ofmore automation and faster verification time. Hence, ESC/Java suffers from both false negatives(programs that pass the check may still contain errors that ESC/Java is designed to handle), andfalse positives (programs flagged as erroneous are in fact correct programs). On the contrary,
our verifier is a sound program verifier as it does not suffer from false negatives: if a programis verified, it is guaranteed to meet its specifications for all possible program executions.
ESC/Java2. The ESC/Java effort is continued with ESC/Java2 [13], which adds support for
current versions of Java, and also verifies more JML [37] constructs. One significant additionis the support for model fields and method calls within annotations [12]. Since ESC/Java2 continues to use Simplify [15] as its underlying theorem prover which does not support transitive
closure operations, it may have difficulties in verifying properties of heap-based data structures that require reachability properties, such as collections of values stored in container data
structures.
Spec♯ . Spec♯ [1] is a programming system developed at Microsoft Research. It is an attempt
at verifying programs written for the C♯ programming language. It adds constructs tailored toprogram verification, such as pre- and post-conditions, frame conditions, non-null types, model
fields and object invariants. Spec♯ programs are verified by the Boogie verifier [1], which usesZ3 [14] to discharge its proof obligations. Spec♯ also supports runtime assertion checking.
Spec♯ supports object invariants but leaves the decision of when to enforce/assume object
invariants to the user. In order to verify object invariant modularly, Spec♯ employs an ownership
scheme that allows an object o to own its representation – objects that are reachable from o andare part of o’s abstract state. The ownership scheme in Spec♯ forces a top-down unpacking of
the objects for updates, and a bottom-up packing for re-establishing the object invariant. The

packing and unpacking of objects are done explicitly by having programmers writing special
commands in method bodies.
In our system, instead of using special fields in method contracts to indicate whether aninvariant should be enforced, users directly use predicates. Hence, there is no need for explicitly
packing and unpacking the objects in the method body. Consequently, users are shielded fromthe details of the verification methodology, which are largely irrelevant, from a user’s point ofview.
Jahob. The main focus of Jahob [32,33] is on reasoning techniques for data structure verification that combines multiple theorem provers to reason about expressive logical formulas. Jahob
uses a subset of the Isabelle/HOL [45] language as its specification language, and works on
instantiatable data structures, as opposed to global data structures used in its predecessor, Hob[36]. Like SPEC♯ , Jahob supports ghost variables and specification assignments which places
onus on programmers to help in the verification process by providing suitable instantiations ofthese specification variables.
EVE Proofs. EVE Proofs [57] is an automatic verifier for Eiffel [40]. The tool translates
Eiffel programs to Boogie [1]. EVE Proofs is integrated in the Eiffel Verification Environment.
The authors acknowledge the importance of frame conditions in modular verification. When aroutine is called, the verifier is invalidating all knowledge about the locations which may havechanged. Therefore it is essential to constrain the effect a routine has on the system to preserve
as much information as possible. As Eiffel does not offer a way to specify the frame condition,
the authors introduced an automatic extraction of modifies clauses. Their approach uses thepostcondition to extract a list of locations which constitute the modifies clause.
Although the approach uses the dynamic type for the pre- and postcondition of a routine call,it uses the static type for the frame condition. This can lead to unsoundness in the system. As
opposed to EVE Proofs, our approach does not have to infer frame conditions, courtesy to theframe rule of separation logic [51]. The crucial power of the frame rule is that it allows a global
property to be derived from a local one, without looking at other parts of the program.
Another restriction of EVE Proofs regards the methodology for invariants, which has to take
into account that objects can temporarily violate the invariant, but also that an object can callother objects while being in an inconsistent state. As this is not considered at the moment, thecurrent implementation of invariants can introduce unsoundness in the system.
As a comparison, we shall discuss some features in our current verification system that differ
from those used in traditional verifiers. Our use of user-defined predicates, which capture theproperties to be analysed, removes the need for model fields and having object invariants tied
to class/type declarations. Regarding ghost specification variables, they are not required since
we provide support for automatically instantiating the predicates’ parameters. Furthermore, we
make use of unfold/fold reasoning to handle the properties of recursive data structures. Thisobviates the need for specifying transitive closure relations that are used by classical verifier,such as Jahob, when tracking recursive properties. Lastly, as separation logic employs local
reasoning via a frame rule, our approach does not require a separate modifies clause to beprescribed.

8. Conclusion
We have presented in this paper an automated approach to verifying heap-manipulating imperative programs. Compared with other separation logic based automated verification systems[3,16,19,38], our approach has made the following advances: (1) other systems mainly focus on
only the separation domain, while we work on a combined domain where not only separation
properties (defining the shape of data structures), but also other properties (such as size andbag) can be specified; (2) other systems support only a few built-in predicates over the separation domain, while we allow arbitrary user-specified (inductive) predicates over the domain
combined with shape, size and bag properties, which greatly improves the expressiveness ofour specification mechanism; (3) most existing systems focus on the verification of the pointersafety, while our approach can verify, in addition to the pointer safety, other properties that
require the presence of numerical information such as size and bag. Our approach is built on
well-founded shape relations and well-formed separation constraints from which we have designed a novel sound procedure for entailment proofs in the combined domain. Our automateddeduction mechanism is based on the unfold/fold reasoning of user-definable predicates and has
been proven to be sound and terminating.
While this paper is focused on automated verification, we shall also look into automatedinference, in order to allow our system to work for substantial sizable software. Automatedinference aims to automatically derive program annotations such as method pre/post-conditions
and loop invariants, rather than reply on programmers/users to manually supply. Recently,
there has been noticeable advance on automated inference for the separation domain [61,7].
However, it is open how to systematically infer pre/post-conditions and loop invariants for thedomain combined with shape, size and bag information and in the presence of user-specified
inductive predicates. This remains our main future work.
Acknowledgement
This work was supported by the Singapore-MIT Alliance and the A*STAR grant R-252-000233-305. Shengchao Qin was supported by the UK Engineering and Physical Sciences ResearchCouncil [grant numbers EP/E021948/1, EP/G042322/1].
REFERENCES
1. M. Barnett, K. R. M. Leino, and W. Schulte. The Spec# programming system: An
overview. In Int’l Workshop on Construction and Analysis of Safe, Secure, and Interoperable Smart Devices, volume 3362 of Lecture Notes in Computer Science, pages 49–69.
Springer, 2004.
2. C. Barrett, A. Stump, and C. Tinelli. The SMT-LIB Standard: Version 2.0. In International
Workshop on Satisfiability Modulo Theories. Informal proceedings, 2010.
3. J. Berdine, C. Calcagno, and P. W. O’Hearn. Symbolic Execution with Separation Logic.
In Asian Symposium on Programming Languages and Systems, volume 3780 of Lecture
Notes in Computer Science, pages 52–68. Springer, 2005.
4. J. Berdine, C. Calcagno, and P. W. O’Hearn. Smallfoot: Modular automatic assertion
checking with separation logic. In International Symposium on Formal Methods for Components and Objects, volume 4111 of Lecture Notes in Computer Science, pages 115–137.
Springer, 2006.

5. J. Bingham and Z. Rakamaric. A Logic and Decision Procedure for Predicate Abstractionof Heap-Manipulating Programs. In Verification, Model Checking, and Abstract Interpretation, volume 3855 of Lecture Notes in Computer Science, pages 207–221. Springer, 2006.
6. R.M. Burstall and J. Darlington. A transformation system for developing recursive programs. Journal of ACM, 24(1):44–67, 1977.
7. C. Calcagno, D. Distefano, P. W. O’Hearn, and H. Yang. Compositional shape analysis bymeans of bi-abduction. In ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, pages 289–300. ACM, 2009.
8. C. Chen and H. Xi. Combining Programming with Theorem Proving. In ACM SIGPLAN
International Conference on Functional Programming, pages 66–77. ACM, 2005.
9. W.N. Chin, C. David, H.H. Nguyen, and S. Qin. Automated verification of shape, sizeand bag properties. In IEEE International Conference on Engineering Complex Computer
Systems, pages 307–320. IEEE, 2007.
10. W.N. Chin and S.C. Khoo. Calculating sized types. In ACM SIGPLAN Workshop on Partial
Evaluation and Semantics-Based Program Manipulation, pages 62–72. ACM, 2000.
11. W.N. Chin, S.C. Khoo, S.C. Qin, C. Popeea, and H.H. Nguyen. Verifying Safety Policies
with Size Properties and Alias Controls. In ACM SIGSOFT International Conference on
Software Engineering, pages 186–195. ACM, 2005.
12. D. R. Cok. Reasoning with specifications containing method calls and model fields. Journal of Object Technology, 4(8):77–103, 2005.
13. D. R. Cok and J. R. Kiniry. ESC/Java2: Uniting ESC/Java and JML. In International
Workshop on Construction and Analysis of Safe, Secure, and Interoperable Smart Devices,volume 3362 of Lecture Notes in Computer Science, pages 108–128. Springer, 2004.
14. L. M. de Moura and N. Bjørner. Efficient E-Matching for SMT Solvers. In International
Conference on Automated Deduction, volume 4603 of Lecture Notes in Computer Science,
pages 183–198. Springer, 2007.
15. D. Detlefs, G. Nelson, and J. B. Saxe. Simplify: A theorem prover for program checking.
Technical Report HPL-2003-148, HP Laboratories Palo Alto, 2003.
16. D. Distefano, P. W. O’Hearn, and H. Yang. A Local Shape Analysis based on Separation
Logic. In Tools and Algorithms for Construction and Analysis of Systems, volume 3920 ofLecture Notes in Computer Science, pages 287–302. Springer, 2006.
17. J. Elgaard, N. Klarlund, and A. Møller. MONA 1.x: new techniques for WS1S and WS2S.
In International Conference on Computer-Aided Verification, volume 1427 of Lecture Notes
in Computer Science, pages 516–520. Springer, 1998.
18. C. Flanagan, K. R. M. Leino, M. Lillibridge, G. Nelson, J. B. Saxe, and R. Stata. Extended
Static Checking for Java. In ACM SIGPLAN Conference on Programming Language Design and Implementation, pages 234–245. ACM, 2002.
19. A. Gotsman, J. Berdine, and B. Cook. Interprocedural Shape Analysis with Separated Heap
Abstractions. In Static Analysis Symposium, volume 4134 of Lecture Notes in Computer
Science, pages 240–260. Springer, 2006.
20. W. Harwood, A. Cavalcanti, and J. Woodcock. A Theory of Pointers for the UTP. In
International Colloquium on Theoretical Aspects of Computing, volume 5160 of Lecture
Notes in Computer Science, pages 141–155. Springer, 2008.
21. C. A. R. Hoare. An axiomatic basis for computer programming. Communications of theACM, 12(10):576–580, 1969.

22. C. A. R. Hoare and J. He. A Trace Model for Pointers and Objects. In European Conference
on Object-Oriented Programming, volume 1628 of Lecture Notes in Computer Science,
pages 1–17. Springer, 1999.
23. C.A.R. Hoare. The verifying compiler: A grand challenge for computing research. Journalof ACM, 50(1):63–69, 2003.
24. J. Hughes, L. Pareto, and A. Sabry. Proving the correctness of reactive systems using sized
types. In ACM Symposium on Principles of Programming Languages, pages 410–423.
ACM, 1996.
25. S. Ishtiaq and P.W. O’Hearn. BI as an assertion language for mutable data structures. In
ACM Symposium on Principles of Programming Languages, pages 14–26. ACM, 2001.
26. L. Jia and D. Walker. ILC: A foundation for automated reasoning about pointer programs.
In European Symposium on Programming, volume 3924 of Lecture Notes in Computer
Science, pages 131–145. Springer, 2006.
27. C. Jones, P. O’Hearn, and J. Woodcock. Verified Software: A Grand Challenge. IEEE
Computer, 39(4):93–95, 2006.
28. I. T. Kassios. Dynamic frames: Support for framing, dependencies and sharing without restrictions. In International Symposium on Formal Methods, volume 4085 of Lecture Notes
in Computer Science, pages 268–283. Springer, 2006.
29. D. König. Theorie der endlichen und unendlichen Graphen (in German). Akademische
Verlagsgesellschaft, 1936.
30. N. Klarlund and A. Moller. MONA Version 1.4 - User Manual. BRICS Notes Series,
January 2001.
31. N. Klarlund and M. I. Schwartzbach. Graph Types. In ACM Symposium on Principles ofProgramming Languages, pages 196–205. ACM, 1993.
32. V. Kuncak. Modular Data Structure Verification. PhD thesis, Massachusetts Institute ofTechnology, 2007.
33. V. Kuncak, P. Lam, K. Zee, and M. Rinard. Modular pluggable analyses for data structure
consistency. IEEE Transactions on Software Engineering, 32(12):988–1005, 2006.
34. V. Kuncak, H. H. Nguyen, and M. Rinard. An algorithm for deciding bapa: Boolean algebrawith presburger arithmetic. In International Conference on Automated Deduction, volume3632 of Lecture Notes in Computer Science, pages 260–277. Springer, 2005.
35. S. Lahiri and S. Qadeer. Verifying Properties of Well-Founded Linked Lists. In ACM
Symposium on Principles of Programming Languages, pages 115–126. ACM, 2006.
36. P. Lam. The Hob System for Verifying Software Design Properties. PhD thesis, Massachusetts Institute of Technology, 2007.
37. G. T. Leavens, A. L. Baker, and C. Ruby. Preliminary design of JML: a behavioral interface
specification language for java. SIGSOFT Software Engineering Notes, 31(3):1–38, 2006.
38. O. Lee, H. Yang, and K. Yi. Automatic verification of pointer programs using grammarbased shape analysis. In European Symposium on Programming, volume 3444 of Lecture
Notes in Computer Science, pages 124–140. Springer, 2005.
39. K. R. M. Leino. Specification and verification of object-oriented software. lecture notes.
Marktoberdorf international summer school, 2008.
40. Bertrand Meyer. Eiffel: the language. Prentice-Hall, Inc., Upper Saddle River, NJ, USA,1992.
41. A. Moeller and M. I. Schwartzbach. The Pointer Assertion Logic Engine. In ACM SIG-

PLAN Conference on Programming Language Design and Implementation, pages 221–231.
ACM, 2001.
42. G. Necula. Proof-carrying code. In ACM Symposium on Principles of Programming Languages, pages 106–119. ACM, 1997.
43. G. Nelson and D. C. Oppen. Simplification by cooperating decision procedures. ACM
Transactions on Programming Languages and Systems, 1(2):245–257, 1979.
44. H.H. Nguyen, C. David, S.C. Qin, and W.N. Chin. Automated Verification of Shape And
Size Properties via Separation Logic. In Verification, Model Checking, and Abstract Interpretation, volume 4349 of Lecture Notes in Computer Science, pages 251–266. Springer,
2007.
45. T. Nipkow, L. C. Paulson, and M. Wenzel. Isabelle/HOL — A Proof Assistant for HigherOrder Logic, volume 2283 of Lecture Notes in Computer Science. Springer, 2002.
46. P. W. O’Hearn, J. Reynolds, and H. Yang. Local Reasoning about Programs that Alter
Data Structures. In Conference of the European Association for Computer Science Logic,
volume 2142 of Lecture Notes in Computer Science, pages 1–19. Springer, 2001.
47. B. Pierce. Types and Programming Languages. The MIT Press, 2002.
48. W. Pugh. Skip lists: A probabilistic alternative to balanced trees. Communications of theACM, 33(6):668–676, 1990.
49. W. Pugh. A practical algorithm for exact array dependence analysis. Communications of
the ACM, 35(8):102–114, 1992.
50. C. R. Reddy and D. W. Loveland. Presburger arithmetic with bounded quantifier alternation. In ACM Symposium on Theory of Computing, pages 320–325. ACM, 1978.
51. J. Reynolds. Separation Logic: A Logic for Shared Mutable Data Structures. In IEEE
Symposium on Logic in Computer Science, pages 55–74. IEEE, 2002.
52. R. Rugina. Quantitative Shape Analysis. In Proceedings of the International Static Analysis Symposium, volume 3148 of Lecture Notes in Computer Science, pages 228–245.
Springer, 2004.
53. S. Sagiv, T. Reps, and R. Wilhelm. Parametric shape analysis via 3-valued logic. ACM
Transactions on Programming Languages and Systems, 24(3):217 – 298, 2002.
54. É-J. Sims. Extending separation logic with fixpoints and postponed substitution. Theoretical Computer Science, 351(2):258–275, 2006.
55. J. M. Spivey. The Z notation: a reference manual. Prentice-Hall, 1989.
56. A. Stump, C. Barrett, and D. Dill. CVC: a Cooperating Validity Checker. In International
Conference on Computer-Aided Verification, volume 2404 of Lecture Notes in Computer
Science, pages 500–504. Springer, 2002.
57. J. Tschannen. Automatic Verification of Eiffel Programs. Master’s thesis, ETH Zurich,2009.
58. D. Walker and G. Morrisett. Alias Types for Recursive Data Structures. In International
Workshop on Types in Compilation, volume 2071 of Lecture Notes in Computer Science,
pages 177–206. Springer, 2000.
59. T. Wies, V. Kuncak, P. Lam, A. Podelski, and M. Rinard. Field constraint analysis. In
Verification, Model Checking, and Abstract Interpretation, volume 3855 of Lecture Notes
in Computer Science, pages 157–173. Springer, 2006.
60. H. Xi. Dependent Types in Practical Programming. PhD thesis, Carnegie Mellon University, 1998.

61. H. Yang, O. Lee, J. Berdine, C. Calcagno, B. Cook, D. Distefano, and P. W. O’Hearn.
Scalable shape analysis for systems code. In International Conference on Computer Aided
Verification, volume 5123 of Lecture Notes in Computer Science, pages 385–398. Springer,
2008.
A. Dynamic Semantics
This section presents a small-step operational semantics for our language given in Fig. 1.
The machine configuration is represented by hs, h, ei, where s denotes the current stack, hdenotes the current heap, and e denotes the current program code. Each reduction step is formalized as a transition of the form: hs, h, ei֒→hs1 , h1 , e1 i. The full set of transitions is given in
Fig. 8. We have introduced an intermediate construct ret(v ∗ , e) to model the outcome of callinvocation, where e denotes the residual code of the call. It is also used to handle local blocks.
The forward verification rule for this intermediate construct is given as follows:[FV−RET]⊢ {∆} e {∆2 } ∆1 = (∃v ′∗ · ∆2 )⊢ {∆} ret(v ∗ , e) {∆1 }Note that whenever the evaluation yields a value, we assume this value is stored in a special
logical variable res, although we do not explicitly put res in the stack s.
We also have the following postcondition weakening rule:[FV−POST−WEAKENING]⊢ {∆} e {∆1 } ∆1 ≈>∆2⊢ {∆} e {∆2 }where ∆1 ≈>∆2 =df ∀s, h · s, h |= Post(∆1 ) =⇒ s, h |= Post(∆2 ). As discussed earlier, we
can view ∆1 and ∆2 as binary relations (as far as only program variables are concerned). Therefore, we use Post(∆) here to refer to the postcondition(i.e. the set of post-states) specified by∆. Note also that ∆1 and ∆2 share the same set of initial states (in which e start to execute).
We now explain the notations used in the operational semantics. We use k to denote a constant, ⊥ to denote an undefined value, and () to denote the empty expression (program). Notethat the runtime stack s is viewed as a ‘stackable’ mapping, where a variable v may occur several
times, and s(v) always refers to the value of the variable v that was popped in most recently.7
The operation [v7→ν]+s “pushes” the variable v to s with the value ν, and ([v7→ν]+s)(v) = ν.
The operation s−{v ∗ } “pops out” variables v ∗ from the stack s. The operation s[v7→ν] changes
the value of the most recent v in stack s to ν. The mapping h[ι7→r] is the same as h except thatit maps ι to r. The mapping h+[ι7→r] extends the domain of h with ι and maps ι to r.
B. Proofs
B.1. Theorem 5.1 – Preservation
Proof: By structural induction on e.
7

We can give a more formal definition for s, where different occurrences of the same variable can be labeled withdifferent ‘frame’ numbers. We omit the details here.

hs, h, vi֒→hs, h, s(v)ihs, h, ki֒→hs, h, ki

hs, h, v.f i֒→hs, h, h(s(v))(f )ihs, h, v:=ki֒→hs[v7→k], h, ()ihs, h, (); ei֒→hs, h, ei

hs, h, e1 i֒→hs1 , h1 , e3 ihs, h, e1 ; e2 i֒→hs1 , h1 , e3 ; e2 ihs, h, ei֒→hs1 , h1 , e1 ihs, h, v:=ei֒→hs1 , h1 , v:=e1 is(v)=truehs, h, if v then e1 else e2 i֒→hs, h, e1 is(v)=false
hs, h, if v then e1 else e2 i֒→hs, h, e2 ihs, h, {t v; e}i֒→h[v7→⊥]+s, h, ret(v, e)ihs, h, ret(v ∗ , k)i֒→hs−{v ∗ }, h, ki

hs, h, ei֒→hs1 , h1 , e1 ihs, h, ret(v ∗ , e)i֒→hs1 , h1 , ret(v ∗ , e1 )ir=h(s(v1 ))[f 7→s(v2 )] h1 =h[s(v1 )7→r]
hs, h, v1 .f := v2 i֒→hs, h1 , ()idata c {t1 f1 , .., tn fn }∈P ι∈dom(h)/r=c[f1 7→s(v1 ), .., fn 7→s(vn )]hs, h, new c(v1···n )i֒→hs, h+[ι 7→ r], ιim−1s1 =[wi 7→s(vi )]ni=m +s t0 mn((ref ti wi )i=1
, (ti wi )ni=m ) {e}m−1e)ihs, h, mn(v1···n )i֒→hs1 , h, ret({wi }ni=m , [vi /wi ]i=1

Figure 8. Small-Step Operational Semantics• Case v := e. There are two cases according to the dynamic semantics:– e is not a value. From dynamic rules, there is e1 s.t. hs, h, ei֒→hs1 , h1 , e1 i, andhs, h, v:=ei֒→hs1 , h1 , v:=e1 i. From verification rule [FV−ASSIGN], ⊢ {∆}e{∆0 },and ∆2 =∃res·∆0 ∧{v} v ′ =res. By induction hypothesis, there exists ∆1 , such thats1 , h1 |= Post(∆1 ) and ⊢ {∆1 }e1 {∆2 }. It concludes from the rule [FV−ASSIGN] that⊢ {∆1 } v:=e1 {∆2 }.
– e is a value. Straightforward.
• Case v1 .f := v2 . Take ∆1 = ∆. It concludes from rule [FV−FIELD−UPDATE] and the
dynamic rule.
• Case new c(v1···n ). From verification rule [FV−NEW], we have ⊢ {∆}new c(v1···n ){∆2 },where ∆2 = ∆∗res::chv1′ , .., vn′ i. Let ∆1 = ∆2 . From the dynamic semantics, we havehs, h, new c(v1···n )i֒→hs, h+[ι 7→ r], ιi, where ι ∈/ dom(h). From s, h |= Post(∆), we haves, h+[ι 7→ r] |= Post(∆1 ). Moreover, ⊢ {∆1 }ι{∆2 }.
• Case e1 ; e2 . We consider the case where e1 is not a value (otherwise it is straightforward). From the dynamic semantics, we have hs, h, e1 i֒→hs1 , h1 , e3 i. From verification
rule [FV−SEQ], we have ⊢ {∆}e1 {∆3 }. By induction hypothesis, there exists ∆1 s.t.
s1 , h1 |= Post(∆1 ), and ⊢ {∆1 }e3 {∆3 }. By rule [FV−SEQ], we have ⊢ {∆1 }e3 ; e2 {∆2 }.
• Case if v then e1 else e2 . There are two possibilities in the dynamic semantics:– s(v)=true. We have hs, h, if v then e1 else e2 i֒→hs, h, e1 i. Let ∆1 = (∆∧v ′ ).
It is obvious that s, h |= Post(∆1 ). By the rule [FV−IF], we have ⊢ {∆∧v ′ } e1 {∆1 }.

By the rule [FV−POST−WEAKENING], we have ⊢ {∆∧v ′ } e1 {∆1 ∨∆2 }. That is,⊢ {∆1 } e1 {∆2 }.
– s(v) = false. Analogous to the above.
• Case t v; e. Let ∆1 = ∆, we conclude immediately from the assumption and the rules[FV−LOCAL] and [FV−RET].
• Case mn(v1..n ). From rule [FV−CALL], we know ∆⊢ρ Φpr ∗ ∆0 . Take ∆1 = ρ Φpr ∗∆0 .
From the dynamic rule and the above heap entailment, we have s1 , h1 |= Post(∆1 ). From
rule [FV−METH], we have ⊢ {ρ Φpr ∗∆0 } e1 {∆0 ∗Φpo } which concludes.
• Case ret(v ∗ , e). There are two cases:– e is a value k. Let ∆1 = ∃v ′∗ · ∆. It concludes immediately.
– e is not a value. hs, h, ret(v ∗ , e)i֒→hs1 , h1 , ret(v ∗ , e1 )i. By [FV−RET] and induction hypothesis, there exists ∆1 s.t. s1 , h1 |= Post(∆1 ) and ⊢ {∆1 } e1 {∆3 }, and∆2 = ∃v ′∗ ·∆3 . By rule [FV−RET] again, we have ⊢ {∆1 } ret(v ∗ , e1 ) {∆2 }.
• Case null | k | v | v.f . Straightforward.
B.2. Theorem 5.2 – Progress
Proof: By structural induction on e.
• Case v := e. There are two cases:– e is a value k. Let s1 = s[v7→k], h1 = h, and e1 = (). We conclude.
– e is not a value. By [FV−ASSIGN], we have ⊢ {∆} e {∆1 }. By induction hypothesis, there exist s1 , h1 , e1 , such that hs, h, ei֒→hs1 , h1 , e1 i. We conclude immediatelyfrom the dynamic semantics.
• Case v1 .f := v2 . Take e1 = (), s1 = s, and h1 = h[s(v1 )7→r], wherer = h(s(v1 ))[f 7→s(v2 )]. It concludes immediately.
• Case new c(v1···n ). Let ι be a fresh location, r denotes the object valuec[f1 7→s(v1 ), .., fn 7→s(vn )]. Take s1 = s, h1 = h+[ι7→r], and e1 = ι. We conclude.
• Case e1 ; e2 . If e1 is a value (), we conclude immediately by taking s1 = s, h1 = h. Otherwise, by induction hypothesis, there exist s1 , h1 , e3 s.t. hs, h, e1 i֒→hs1 , h1 , e3 i. We thenhave hs, h, e1 ; e2 i֒→hs1 , h1 , e3 ; e2 i from the dynamic semantics.
• Case if v then e1 else e2 . It concludes immediately from a case analysis (based onvalue of v) and the induction hypothesis.
• Case t v; e. Let s1 = [v7→⊥]+s, h1 = h, and e1 = ret(v, e). We conclude immediately.
• Case mn(v1..n ). Suppose v1 , .., vm are pass-by-reference, while others are not. Takem−1e), where wi are froms1 = [wi 7→s(vi )]ni=m +s, h1 = h, and e1 = ret({wi }ni=m , [vi /wi ]i=1nmethod specification t0 mn((ref ti wi )m−1
,
(t
w
)
)
{e}.
We
concludeby the dyi i i=m
i=1namic semantics.

• Case ret(v ∗ , e). If e is a value k, let s1 = s − {v ∗ }, h1 = h, and e1 = k, we conclude.
Otherwise, by induction hypothesis, there exist s1 , h1 , e1 s.t.
hs, h, ei֒→hs1 , h1 , e1 i. We then have hs, h, ret(v ∗ , e)i֒→hs1 , h1 , ret(v ∗ , e1 )i.
• Case null | k | v | v.f . Straightforward.
B.3. Theorem 5.3 – Safety
Before we present the proof for Theorem 5.3, we state and prove the following lemma:
Lemma B.1 For any s, h, e, if hs, h, ei֒→∗ hŝ, ĥ, νi for some ŝ, ĥ, ν, where ν is a value, andall free variables of e are already in the domain of the stack s, i.e. free-vars(e)⊆dom(s), thendom(ŝ) = dom(s).
Proof: By structural induction over e.
Basic cases: e is null | k | v | v.f | v.f = v1 . The conclusion is obvious as the stack remains
unchanged during the evaluation of e.
Inductive cases:• e is v := e1 . By the operational semantics, we know that hs, h, e1 i֒→∗ hs1 , h1 , ν1 i for somes1 , h1 , ν1 , and hs1 , h1 , v := ν1 i֒→hŝ, ĥ, νi. Note that free-vars(e1 )⊆free-vars(e)⊆dom(s),by induction hypothesis, we have dom(s1 ) = dom(s). The conclusion follows sincedom(ŝ) = dom(s1 ).
• e is e1 ; e2 . By the operational semantics, there are s1 , h1 such that hs, h, e1 i֒→∗ hs1 , h1 , ()i,hs1 , h1 , (); e2 i֒→hs1 , h1 , e2 i, hs1 , h1 , e2 i֒→∗ hŝ, ĥ, νi. Note that, for i=1, 2, we havefree-vars(ei )⊆free-vars(e)⊆dom(s). By induction hypothesis, we havedom(ŝ) = dom(s1 ) = dom(s).
• e is t v; e1 . By the operational semantics, we have hs, h, ei֒→h[v7→ ]+s, h, ret(v, e1 )i,and h[v7→ ]+s, h, e1 i֒→∗ hs1 , h1 , νi for some s1 , h1 , and hs1 , h1 , ret(v, ν)i֒→hŝ, ĥ, νi, whereŝ = s1 −{v}. Note that free-vars(e1 )⊆dom([v7→ ]+s), by induction hypothesis, we havedom(s1 ) = dom([v7→ ]+s). So dom(ŝ) = dom(s1 )−{v} = dom([v7→ ]+s)−{v} = dom(s).
• e is mn(u∗ ; v ∗ ), where v ∗ are arguments for call-by-value parameters w∗ . By the operational semantics, we have (1) hs, h, ei֒→h[w∗ 7→v ∗ ]+s, h, ret(w∗ , emn )i, where emn is the
body of the method mn, and (2) h[w∗ 7→v ∗ ]+s, h, emn i֒→∗ hs1 , h1 , νi for some s1 , h1 , and(3) hs1 , h1 , ret(w∗ , ν)i֒→hŝ, ĥ, νi, where ŝ = s1 −{w∗ }. Note also that we havefree-vars(emn )⊆dom([w∗ 7→v ∗ ]+s), by induction hypothesis, we havedom(s1 ) = dom([w∗ 7→v ∗ ]+s). So dom(ŝ) = dom(s1 )−{w∗ } = dom(s).
2Proof of Theorem 5.3: If the evaluation of e does not diverge (is not infinite), it will terminate
in a finite number of steps (say n): h[], [], ei֒→hs1 , h1 , e1 i֒→· · ·֒→hsn , hn , en i, and there are nofurther reductions possible. By Theorem 5.1, there exist ∆1 , .., ∆n such that, si , hi |= Post(∆i ),and ⊢ {∆i } ei {∆}. By Theorem 5.2, The final result en must be some value v (or it will makeanother reduction). The conclusion that the stack sn in the final state is empty is drawn from
Lemma B.1 in the above.
2

B.4. Soundness and Termination of Heap Entailment
Definition B.1 (length) We define the length of a separation constraint inductively as follows:
length(emp)
length(p::chv ∗ i)length(κ1 ∗κ2 )length(∃v ∗ ·κ∧γ∧φ)length(Φ1 ∨Φ2 )=0=1= length(κ1 )+length(κ2 )= length(κ)= length(Φ1 )+length(Φ2 )Definition B.2 (Entailment Transition) A transition of the form E1 → E2 is called an entailment transition where Ei is either an entailment of the form ∆1 ⊢κV ∆2 ∗ ∆ or a fold operation
foldκ (∆, p::chv ∗ i). The set of possible entailment transitions are specified inductively by theentailment rules and the fold operation defined in Section 4.
• Rule [ENT−MATCH]: There is one possible transition:(p1 ::chv1∗ i∗κ1 )∧π1 ⊢κV ((p2 ::chv2∗ i∗κ2 )∧π2 ) ∗ ∆→κ∗p1 ::chv1∗ i
κ1 ∧(π1 ∧freeEqn(ρ, V ))⊢V −dom(ρ)ρ(κ2 ∧π2 ) ∗ ∆• Rule [ENT−EMP]: There is no entailment transition.
• Rule [ENT−UNFOLD]: There is one transition:(p1 ::c1 hv1∗ i∗κ1 )∧π1 ⊢κV ((p2 ::c2 hv2∗ i∗κ2 )∧π2 ) ∗ ∆→unfold(p1 ::c1 hv1∗ i)∗κ1 ∧π1 ⊢κV ((p2 ::c2 hv2∗ i∗κ2 )∧π2 ) ∗ ∆• Rule [ENT−FOLD]: There are 2 possible transitions:(p1 ::c1 hv1∗ i∗κ1 )∧π1 ⊢κV ((p2 ::c2 hv2∗ i∗κ2 )∧π2 ) ∗ ∆→foldκ ((p1 ::c1 hv1∗ i∗κ1 )∧π1 , p1 ::c2 hv2∗ i)
and(p1 ::c1 hv1∗ i∗κ1 )∧π1 ⊢κV ((p2 ::c2 hv2∗ i∗κ2 )∧π2 ) ∗ ∆→κr∆i ∧πia ⊢Vi κ2 ∧(π2 ∧πic ) ∗ ∆ for some i∈1, .., n• Rule [ENT−LHS−OR]: There are two possible transitions:∆1 ∨∆2 ⊢κV ∆3 ∗ (∆4 ∨∆5 ) → ∆1 ⊢κV ∆3 ∗ ∆4
∆1 ∨∆2 ⊢κV ∆3 ∗ (∆4 ∨∆5 ) → ∆2 ⊢κV ∆3 ∗ ∆5
• Rule [ENT−RHS−OR]: There are two possible transitions:→ ∆1 ⊢κV ∆2 ∗ ∆R
∆1 ⊢κV (∆2 ∨∆3 ) ∗ ∆R
2i
∆1 ⊢κV (∆2 ∨∆3 ) ∗ ∆R
→ ∆1 ⊢κV ∆3 ∗ ∆Ri3

• Rule [ENT−RHS−EX]: There is one possible transition:∆1 ⊢κV (∃v · ∆2 ) ∗ ∆ → ∆1 ⊢κV ∪{w} ([w/v]∆2 ) ∗ ∆3
• Rule [ENT−LHS−EX]: There is one possible transition:∃v · ∆1 ⊢κV ∆2 ∗ ∆ → [w/v]∆1 ⊢κV ∆2 ∗ ∆• Rule [FOLDING]: There is one possible transition:′′foldκ (κ∧π, p::chv ∗ i) → κ∧π⊢κ{v∗ } [p/root]Φ ∗ {(∆i , κi , Vi , πi )}ni=1
Definition B.3 (Entailment Search Tree)is a tree formed as follows:

An entailment search tree for E ≡ ∆1 ⊢κV ∆2 ∗ ∆• The nodes of the tree are either entailment relations or fold operations (of the form
foldκ (∆, p::chv ∗ i)).
• The root of the tree is E.
• The edges from parent nodes to their children nodes are entailment transitions defined in
Definition B.2.
B.5. Theorem 5.4 – Soundness of Heap Entailment
Proof: We need to show that if E0 ≡ ∆1 ⊢κV ∆2 ∗ ∆3 succeeds and s, h |= ∆1 , then s, h |= ∆2 ∗∆3 .
Note that the entailment rule [ENT−MATCH] in Sec. 4 denotes a match of two nodes/shape predicates between the antecedent and the consequent. We apply induction on the number of such
matches for each path in the entailment search tree for E0 .
Base case. The entailment search succeeds requiring no matches. It can only be the casewhere rule [ENT−EMP] is applied. It is straightforward to conclude.
Inductive case. Suppose a sequence of transitions E0 → · · · → En where no match transitions(due to rule [ENT−MATCH]) are involved in this sequence but En will perform a match transition.
These transitions can only be generated by the following rules: [ENT−UNFOLD], [ENT−FOLD],[ENT−LHS−OR], [ENT−RHS−OR], [ENT−LHS−EX], and [ENT−RHS−EX]. A case analysis on theserules shows that the following properties hold:s, h |= LHS(Ei ) =⇒ s, h |= LHS(Ei+1 )s, h |= RHS(Ei+1 ) =⇒ s, h |= RHS(Ei )(†)

Suppose the match node for En ≡ ∆a ⊢κV ∆c ∗ ∆r is p::chv ∗ i, and En becomesκ∗p::chv ∗ i ′∆′a ⊢V∆c ∗ ∆r for some ∆′a , ∆′c . By induction, we have∀s, h · s, h |= ∆′a =⇒ s, h |= ∆′c ∗∆r(‡)

From the entailment process, we have ∆a = p::chv ∗ i∗∆′a , and ∆c = p::chv ∗ i∗∆′c . Supposes, h |= ∆a , then there exist h0 , h1 , such that h = h0 ∗h1 , s, h0 |= p::chv ∗ i, and s, h1 |= ∆′a . From(‡), we have s, h1 |= ∆′c ∗∆r , which immediately yields
s, h |= ∆c ∗∆r . We then conclude from (†).
2
Before we prove the termination theorem, we state and prove two lemmas.

Lemma B.2 For any ∆1 and ∆2 , the entailment search tree for E ≡ ∆1 ⊢κV ∆2 ∗ ∆ has only
finite number of fold nodes.
Proof sketch: Suppose the first rule applied in the search tree for E is [ENT−FOLD] (the only
rule that generates fold nodes). By Definition B.2, there are n+1 children E0 , · · · , En for theroot node E, for some n, where E0 is a fold node. Note that the length of the consequent in Ei isstrictly smaller than that in E. On the other hand, node E0 will perform a transition (due to rule
[FOLDING]), yielding a node E ′ :′(p1 ::c1 hv1∗ i∗κ1 )∧π1 ⊢κ{v2∗ } [p1 /root]Φ ∗ {· · · }This node E ′ performs some transitions which do not change the antecedent before it performs
a transition due to rule [ENT−MATCH], yielding a new node E0′ :κ′ ∗p ::c hv ∗ i

1 1 1
κ1 ∧π1 ⊢V −dom(ρ)∆4 ∗ ∆′

Note that the length of the antecedent in this node is one smaller than that in the root node E.
Moreover, it is not possible for E0′ to perform an unfold operation as the only data node in Φ hasbeen consumed by the match transition. This guarantees the strict decreasing of the length of
the antecedent.
In a nutshell, any paths that involve a chain of fold operations will keep the length of theantecedent decreasing, while other paths keep the length of the consequent decreasing. By
induction, we can conclude that the number of fold operations is finite.
2
Lemma B.3 For any entailment relation E ≡ ∆1 ⊢κV ∆2 ∗ ∆, its entailment search tree is finite
branching and has finite depth.
Proof: Let li = length(∆i ) for i = 1, 2. Obviously we have l1 ≥0, l2 ≥0. Let f denote thenumber of fold operations that have appeared in the entailment search tree.
Due to the well-foundedness of separation constraints, there are finite possible entailment
transitions starting from any entailment relation (thus finite possible children for it). This ensures finite branching for each node. What we need to prove is the finite depth property.
To prove finite depth property, we can apply induction on the well-founded measure (f, l2 , l1 )using the following lexicographic order:(f ′ , l2′ , l1′ ) < (f, l2 , l1 ) =df f ′ <f ∨ f ′ =f ∧l2′ <l2 ∨ f ′ =f ∧(l2′ =l2 ∧l1′ <l1 )(i). For the base case where the measure at root node is(f =0, l2 =0, l1 =0). The only possible transition for the root node is from one of the following
rules [ENT−EMP], [ENT−RHS−EX], and [ENT−LHS−EX], as all other rules require l1 >0 or l2 >0.
If the transition is due to rule [ENT−EMP], the finite depth is obvious due to Definition B.2.
If the transition is due to rule [ENT−RHS−EX] or [ENT−LHS−EX], the finite depth is guaranteed
as all paths of the tree are formed by finite number of transitions due to rule [ENT−RHS−EX] or[ENT−LHS−EX] and then a transition due to rule [ENT−EMP]. This is because we only havefinite number of existential variables.
(ii). For the inductive case (f =m, l2 =n, l1 =k), where m+n+k>0. Let us do a case analysis
on the rule that we apply to the root node E to generate transitions:(iia) Rule [ENT−EMP]. The finite depth property is trivial as discussed in (i).
(iib) Rule [ENT−MATCH]. There is only one possible transition E → E1 (Definition B.2).
Let (f ′ , l2′ , l1′ ) denote the measure in the child node E1 , immediately we have f ′ =f , l2′ =l2 −1,
l1′ =l1 −1. Thus (f ′ , l2′ , l1′ ) < (f, l2 , l1 ). By induction hypothesis, the finite depth property holds
for the subtree rooted at E1 . So does the whole tree.
(iic) Rule [ENT−UNFOLD]. There is only one possible transition E → E1 (Definition B.2).
Let (fa , l2a , l1a ) denote the measure in the child node E1 .We have fa = f , l2a = l2 , and l1a ≥l1 .
The measure does not decrease. However, as a new match is generated after unfolding, theonly possible transition from E1 is the one generated by rule [ENT−MATCH] which we denoteas E1 → E2 . Let (fb , l2b , l1b ) denote the measure in the node E2 . From (iib), we know fb =f ,l2b =l2a −1, which yields l2b =l2 −1<l2 , thus (fb , l2b , l1b ) < (f, l2 , l1 ). By induction hypothesis,
the finite depth property holds.
(iid) Rule [ENT−FOLD]. There are 2 possible transitions. For the first transition E → E1
where E1 = foldκ (...), all nodes in the subtrees of node E1 have a decreased measure (numberof fold operations is decreased!), by induction hypothesis all subtrees of E1 have finite depth,
so is the subtree rooted at E1 . For the other transition E → Ei (for some i∈2, ..., n+1), we also
see the decrease of the measure (the length of the consequence l2 ) in nodes Ei . By induction
hypothesis again, subtrees rooted at nodes Ei have finite depth. This concludes the whole tree
has finite depth.
(iie) Rule [ENT−LHS−OR] or [ENT−RHS−OR]. The corresponding measure in the only childnode is smaller than that in E. It concludes immediately by induction hypothesis.
(iif) Rule [ENT−RHS−EX] or [ENT−LHS−EX]. Starting from E, after finite number of similartransitions (due to [ENT−RHS−EX] or [ENT−LHS−EX]), a different transition (due to rules otherthan [ENT−RHS−EX] or [ENT−LHS−EX]) will be taken. This then reduces the case to what wehave discussed above.
Thus it concludes that the entailment search tree has finite depth.
2
B.6. Theorem 5.5 – Termination of Heap Entailment
Proof: By Koenig’s lemma [29] and Lemma B.3, all paths are finite. This concludes thatthe entailment checking terminates.
2
B.7. Lemma 5.6 – Sound Abstraction
Before we prove Lemma 5.6, we state and prove the following support lemma.
Lemma B.4 Given a separation constraint Φ where the invariants of the predicates appearingin Φ are sound, we have ∀s, h · ( s, h |= Φ =⇒ s |= XPure0 (Φ)).
Proof: By structural induction on Φ.
W• Φ = Φ1 Φ2 .
s, h |= ΦW⇐⇒ s, h |= Φ1 W Φ2⇐⇒ s, h |= Φ1 s, h |=WΦ2 (model for separation constraint - Def 5.1)=⇒ s |= XPure0 (Φ1 )W s |= XPure0 (Φ2 ) (induction hypothesis)⇐⇒ s |= XPure0 (Φ1 ) XPure0 (Φ2 ) (XPuren definition - Fig 6)⇐⇒ s |= XPure0 (Φ)

• Φ = ∃v1..n · κ∧π.
s, h |= Φ⇐⇒ s, h |= ∃v1···n · κ ∧ π⇐⇒ ∃ν1..n · s[(vi 7→νi )ni=1 ], h |= κ and s[(vi 7→νi )ni=1 ] |= π (model for sep. constraint - Def 5.1)=⇒ ∃ν1..n · s[(vi 7→νi )ni=1 ] |= XPure0 (κ) and s[(vi 7→νi )ni=1 ] |= [0/null]π(induction hypothesis and XPure0 definition - Fig 6)⇐⇒ ∃ν1..n · s[(vi 7→νi )ni=1 ] |= XPure0 (κ) ∧ [0/null]π (model for sep. constraint - Def 5.1)⇐⇒ ∃ν1..n · s[(vi 7→νi )ni=1 ] |= XPure0 (κ ∧ π) (XPuren definition - Fig 6)⇐⇒ ∃ν1..n · s |= ∃ v1..n · XPure0 (κ ∧ π) (model for separation constraint - Def 5.1)⇐⇒ ∃ν1..n · s |= XPure0 (∃ v1..n · κ ∧ π) (XPuren definition - Fig 6)⇐⇒ s |= XPure0 (Φ)• Φ = κ1 ∗κ2 .
s, h |= Φ⇐⇒ s, h |= κ1 ∗κ2⇐⇒ s, h1 |= κ1 ∧ s, h2 |= κ2 ∧ h = h1 ∗h2
Let XPure0 (κ1 ) = ∃I · φ1 , XPure0 (κ2 ) = ∃J · φ2 where
I and J are composed of fresh symbolic addresses and I ∩ J = ∅=⇒ s |= ∃I · φ1 ∧ s |= ∃J · φ2 and I ∩ J = ∅ (induction hypothesis)⇐⇒ s |= (∃I · φ1 ) ∧ (∃J · φ2 )⇐⇒ s |= XPure0 (κ1 ∗κ2 ) (XPuren definition - Fig.6)⇐⇒ s |= XPure0 (Φ)• Φ = emp. Straightforward.
• Φ = p::chv ∗ i, and IsData(c).
s, h |= Φ⇐⇒ s, h |= p::chv ∗ i=⇒ ∃ν · s(p) = ν ∧ ν 6= null (model for separation constraint - Def 5.1)⇐⇒ s |= ∃i · p = i ∧ i 6= 0⇐⇒ s |= XPure0 (Φ) (XPuren definition - Fig.6)• Φ = p::chv ∗ i, and IsView(c).
s, h |= Φ⇐⇒ s, h |= p::chv ∗ i⇐⇒ s, h |= [p/root]Φc (assuming chv ∗ i ≡ Φc inv π ∈ P )=⇒ s |= XPure0 ([p/root]Φc ) (induction hypothesis)=⇒ s |= [p/root, 0/null]π ( all invariants of predicates in Φ are sound)⇐⇒ s |= XPure0 (Φ) (XPuren definition - Fig.6)
2
Now we present the proof for Lemma 5.6 in what follows.

Proof of Lemma 5.6: Given a separation constraint Φ where the invariants of the predicatesappearing in Φ are sound, we show that ∀s, h · ( s, h |= Φ =⇒ s |= XPuren (Φ)) by induction onn.
Base case: n = 0. It follows from Lemma B.4.
Inductive case: We show that for all s, h, s |= XPuren+1 (Φ) if s, h |= Φ and s |= XPuren (Φ). To
prove this, we conduct a structural induction on Φ.
• Φ = Φ1W

Φ2 .

s, h |= ΦW⇐⇒ s, h |= Φ1 W Φ2⇐⇒ s, h |= Φ1 s, h |= ΦW2 (model for sep. constraint - Def 5.1)=⇒ s |= XPuren+1 (Φ1 )W s |= XPuren+1 (Φ2 ) (hypothesis of structural induction)⇐⇒ s |= XPuren+1 (Φ1 ) XPuren+1 (Φ2 ) (XPuren definition - Fig 6)⇐⇒ s |= XPuren+1 (Φ)• Φ = ∃v1..m · κ∧π.
s, h |= Φ⇐⇒ s, h |= ∃v1···m · κ ∧ πm⇐⇒ ∃ν1..m · s[(vi 7→νi )m(model for sep. constraint - Def 5.1)i=1 ], h |= κ and s[(vi 7→νi )i=1 ] |= πmm=⇒ ∃ν1..m · s[(vi 7→νi )i=1 ] |= XPuren+1 (κ) and s[(vi 7→νi )i=1 ] |= [0/null]π (hypothesis of
structural induction and XPuren definition - Fig 6)⇐⇒ ∃ν1..m · s[(vi 7→νi )m(model for sep. constraint - Def 5.1)i=1 ] |= XPuren+1 (κ) ∧ [0/null]πm⇐⇒ ∃ν1..m · s[(vi 7→νi )i=1 ] |= XPuren+1 (κ ∧ π) (XPuren definition - Fig 6)⇐⇒ ∃ν1..m · s |= ∃ v1..m · XPuren+1 (κ ∧ π) (model for sep. constraint - Def 5.1)⇐⇒ ∃ν1..m · s |= XPuren+1 (∃ v1..m · κ ∧ π) (XPuren definition - Fig 6)⇐⇒ s |= XPuren+1 (Φ)• Φ = κ1 ∗κ2 .
s, h |= Φ⇐⇒ s, h |= κ1 ∗κ2⇐⇒ s, h1 |= κ1 ∧ s, h2 |= κ2 ∧ h = h1 ∗h2
Let XPuren+1 (κ1 ) = ∃I · φ1 , XPuren+1 (κ2 ) = ∃J · φ2 where
I and J are composed of fresh symbolic addresses and I ∩ J = ∅=⇒ s |= ∃I · φ1 ∧ s |= ∃J · φ2 and I ∩ J = ∅ (hypothesis of structural induction)⇐⇒ s |= (∃I · φ1 ) ∧ (∃J · φ2 )⇐⇒ s |= XPuren+1 (κ1 ∗κ2 ) (XPuren definition - Fig.6)⇐⇒ s |= XPuren+1 (Φ)• Φ = emp. Straightforward.

• Φ = p::chv ∗ i, and IsData(c).
s, h |= Φ⇐⇒ s, h |= p::chv ∗ i=⇒ ∃ν · s(p) = ν ∧ ν 6= null (model for separation constraint - Def 5.1)⇐⇒ s |= ∃i · p = i ∧ i 6= 0⇐⇒ s |= XPuren+1 (Φ) (XPuren definition - Fig.6)• Φ = p::chv ∗ i, and IsView(c).
s, h |= Φ⇐⇒ s, h |= p::chv ∗ i⇐⇒ s, h |= [p/root](Φc ) (assuming chv ∗ i ≡ Φc inv π ∈ P )=⇒ s |= XPuren ([p/root]Φc ) (hypothesis of induction over n)⇐⇒ s |= XPuren+1 (p::chv ∗ i) (XPuren definition - Fig.6)
2