2.1 Notations

We use a special notation for ordered pairs. The two components of an ordered pair are separated by $\mathbin{\star}$ . A definition of a pair $s=a\mathbin{\star} b$ silently defines the pair of selectors $s.a$ and $s.b$ .

A total (partial) function f from A to B is denoted by $A \to B$ ( $A \nrightarrow B$ , respectively). Given $f: A \nrightarrow B$ and $x \in A$ , we write $f(x)=\bot$ when f is undefined on x. The composition of functions f and g is denoted by $f \circ g$ . The domain and range of f are, respectively, dom(f) and rng(f). We denote by $[v_1 \mapsto t_1,\ldots,v_n \mapsto t_n]$ the function f such that $dom(f) = \{v_1,\ldots,v_n\}$ and $f(v_i)=t_i$ for $i=1,\ldots,n$ . We denote by $f[w_1\mapsto d_1,\ldots,w_m\mapsto d_m]$ an update of f, with a possibly enlarged domain. By $f|_s$ ( $f|_{-s}$ ), we denote the restriction of f to $s\subseteq dom(f)$ (to $dom(f)\setminus s$ , respectively).

Given a set X, we denote by $\mathcal{P}(X)$ the powerset of X and with $\mathcal{P}_2(X)$ those subsets of X of cardinality 1 or 2. Given $f: A \nrightarrow B$ , $Y \in \mathcal{P}(B)$ and $Z \subseteq \mathcal{P}_2(B)$ , we denote by $f^{-1}(Y)=\{a \in A \mid f(a) \in Y\}$ and $f^{-1}(Z)=\{\{a,a'\} \in \mathcal{P}_2(A) \mid \{f(a),f(a')\} \in Z\}$ .

For an ordered set $\mathit{S}\mathbin{\star}\mathord\le$ , if $s\in S$ , then $\downarrow s=\{s'\in S\mid s'\le s\}$ is the downward closure of S. For a preordered set $\mathit{S}\mathbin{\star}\mathord\le$ , we say that $s_1, s_2 \in S$ are equivalent, and we write $s_1 \sim s_2$ , when $s_1 \leq s_2$ and $s_2 \leq s_1$ . The set $\mathit{S}/{\sim}$ of equivalence classes modulo $\sim$ is ordered by $[s_1] \le [s_2]$ iff $s_1 \le s_2$ . We will freely use preordered sets in the place where ordered sets are expected, implicitly referring to the induced ordered set.

We recall now the basics of abstract interpretation from Cousot and Cousot (Reference Cousot and Cousot1977, Reference Cousot and Cousot1992). Given two posets $\mathit{C}\mathbin{\star}\mathord\le$ and $\mathit{A}\mathbin{\star}\mathord\preceq$ (the concrete and the abstract domain), a Galois connection is a pair of monotonic maps $\alpha:\mathit{C}\mapsto\mathit{A}$ and $\gamma:\mathit{A}\mapsto\mathit{C}$ such that $\gamma \circ \alpha$ is extensive and $\alpha \circ \gamma$ is reductive. It is a Galois insertion when $\alpha \circ \gamma$ is the identity map, that is, when the abstract domain does not contain useless elements. This is equivalent to $\alpha$ being onto, or $\gamma$ one-to-one.

We say that $a \in A$ is a correct approximation of $c \in C$ when $\alpha(c) \preceq a$ or, equivalently, $c \leq \gamma(a)$ . An abstract operator $f^A:\mathit{A}^n\mapsto\mathit{A}$ is correct w.r.t. $f:\mathit{C}^n\rightarrow\mathit{C}$ if, given $a_1, \ldots, a_n$ correct abstractions of $c_1, \ldots, c_n$ , we have that $f^A(a_1, \ldots, a_n)$ is a correct abstraction of $f(c_1, \ldots, c_n)$ . This is equivalent to $f(\gamma(a_1), \ldots, \gamma(a_n)) \leq \gamma(f^A(a_1, \ldots, a_n))$ for every tuple $a_1,\ldots, a_n$ of abstract objects.

2.2 The language

We use the Java-like object-oriented language defined by Secci and Spoto (Reference Secci and Spoto2005a), which is a normalized version of Java with downward casts, and which we extend with upper casts. The details of the concrete semantics first appeared in a long version of the above paper (Secci and Spoto Reference Secci and Spoto2005b) which is unpublished. Here we present the semantics for the sake of completeness.

2.2.1 Syntax

Each program has a set of identifiers $\mathord{\mathit{Ide}}$ and a finite set of classes (or types) $\mathcal{K}$ ordered by a subclass relation $\le$ such that $\mathcal{K}\mathbin{\star}\mathord\le$ is a poset. The set $\mathord{\mathit{Ide}}$ includes the special identifiers ${\mathord{\mathtt{this}}}$ , ${\mathord{\mathtt{res}}}$ , ${\mathord{\mathtt{out}}}$ . Since we do not allow multiple inheritance, for any class $\kappa\in \mathcal{K}$ , the set $\{\kappa'\mid \kappa' \geq\kappa\}$ is a chain. In the following, we assume that $\mathord{\mathit{Ide}}$ and $\mathcal{K}$ have been fixed beforehand.

We use type environments to describe the identifiers in scope in a given program point. A type environment is a map from a finite set of identifiers to the associated class. The set of type environments is

\begin{equation*} \mathord{\mathit{TypEnv}}=\{\tau: \mathord{\mathit{Ide}}\nrightarrow\mathcal{K}\mid dom(\tau)\text{ is finite}\} .\end{equation*}

We call variables the identifiers in $dom(\tau)$ . Any class $\kappa\in\mathcal{K}$ defines a type environment, also denoted by $\kappa$ , which maps the fields of $\kappa$ (including both the fields defined in $\kappa$ and those inherited by the superclasses) to their types.

We require that fields cannot be redefined in subclasses. It means that if ${\mathord{\mathtt{f}}} \in dom (\kappa)$ and $\kappa' \leq \kappa$ , then ${\mathord{\mathtt{f}}} \in dom (\kappa')$ and $\kappa'({\mathord{\mathtt{f}}})=\kappa({\mathord{\mathtt{f}}})$ . For consistency of notation, we write $\kappa.{\mathord{\mathtt{f}}}$ in place of $\kappa({\mathord{\mathtt{f}}})$ for the type of the field ${\mathord{\mathtt{f}}}$ in the class $\kappa$ .

Finally, we require the existence of a class $\top$ with no fields which is the common ancestor of all other classes.

In the examples, we will describe the set of classes and the corresponding type environment using a notation inspired by class definitions in Java.

Example 1. Classes in the example program in Section 1.4 may be described by the following Java-like syntax:

class Tree { Tree l; Tree r; }class Integer { }

Formally, we have $\mathcal{K}=\{\top,\mathtt{Tree},\mathtt{Integer}\}$ with a flat ordering. Moreover, $\mathtt{Tree}=[{\mathord{\mathtt{l}}}\mapsto\mathtt{Tree}, {\mathord{\mathtt{r}}}\mapsto\mathtt{Tree}]$ and $\mathtt{Integer}=[]$ .

Expressions and commands are normalized versions of those in Java. Their syntax is the following:

\begin{align*}\mathit{exp} ::= &\ {\mathord{\mathtt{null}}}\ \kappa\mid\mathtt{new}\ \kappa\mid v\mid\mathit{v}.{\mathord{\mathtt{f}}}\mid\mathtt{(}\kappa\mathtt{)}v\mid\mathit{v}\mathtt{.m(}v_1,\ldots,v_n\mathtt{)}\\\mathit{com} ::= &\ v\mathbin{\mathtt{\unicode{x02254}}}\mathit{exp} \mid v.{\mathord{\mathtt{f}}}\mathbin{\mathtt{\unicode{x02254}}}\mathit{exp}\mid\mathtt{\{}\mathit{com}\mathtt{;}\cdots\mathtt{;}\mathit{com}\mathtt{\}}\\&\ \mid\mathtt{if\ }v\ \mathtt{=}\ w\mathtt{\ then\ }\mathit{com}\mathtt{\ else\ }\mathit{com}\mid\mathtt{if\ }v\ \mathtt{=null\ then\ }\mathit{com}\mathtt{\ else\ }\mathit{com}\end{align*}

where $\kappa\in\mathcal{K}$ , ${\mathord{\mathtt{f}}}\in\mathord{\mathit{Ide}}$ and $v,w,v_1,\ldots,v_n\in\mathord{\mathit{Ide}}\setminus\{{\mathord{\mathtt{res}}}\}$ are distinct when they appear in the same clause. Each method $\kappa.{\mathord{\mathtt{m}}}$ of a class $\kappa$ is defined with a statement like

\begin{equation*}\mathtt{\kappa_0\ m(}w_1\!:\!\kappa_1,\ldots,w_n\!:\!\kappa_n\mathtt{)}\mathtt{\ with\ }w_{n+1}\!:\!\kappa_{n+1},\ldots,w_{n+m}\!:\!\kappa_{n+m}\mathtt{\ is\ }\mathit{com}\end{equation*}

where $w_1,\ldots,w_n,w_{n+1},\ldots,w_{n+m}\in\mathord{\mathit{Ide}}$ are distinct and are not ${\mathord{\mathtt{res}}}$ nor ${\mathord{\mathtt{this}}}$ nor ${\mathord{\mathtt{out}}}$ . Their declared types are $\kappa_1,\ldots,\kappa_n,\kappa_{n+1},\ldots,\kappa_{n+m}\in\mathcal{K}$ , respectively. Variables $w_1,\ldots,w_n$ are the formal parameters of the method, and $w_{n+1},\ldots,w_{n+m}$ are its local variables. The method can also use a variable ${\mathord{\mathtt{out}}}$ of type $\kappa_0$ which holds its return value. We define $\mathit{body}(\kappa.{\mathord{\mathtt{m}}})=\mathit{com}$ and $\mathit{returnType}(\kappa.{\mathord{\mathtt{m}}})=\kappa_0$ . Overriding methods cannot change the formal parameters but may specialize the return type.

Given a type environment $\tau$ , with an abuse of notation we denote with $\tau(\mathit{exp})$ the static type of an expression $\mathit{exp}$ , defined as follows:

\begin{gather*} \tau(v.{\mathord{\mathtt{f}}})=\tau(v).{\mathord{\mathtt{f}}} \qquad \tau(\mathtt{new}\ \kappa) =\tau({\mathord{\mathtt{null}}}\ \kappa) =\tau(\mathtt{(}\kappa\mathtt{)}v)=\kappa\\ \tau(v.\mathtt{m(}v_1,\ldots,v_n\mathtt{)})= \mathit{returnType}(\tau(v).{\mathord{\mathtt{m}}}). \end{gather*}

Note that the static type of a field of class $\kappa$ is recovered from the definition of $\kappa$ , while the static type of the return value of a method call is the return type of the method.

We require expressions, commands, and methods to be well-typed, according to the standard definition in Java. We also require that all casts are explicit, so that in any assignment $v\mathbin{\mathtt{\unicode{x02254}}}\mathit{exp}$ (resp. $v.{\mathord{\mathtt{f}}}\mathbin{\mathtt{\unicode{x02254}}}\mathit{exp}$ ) the types of v (resp. $v.{\mathord{\mathtt{f}}}$ ) and $\mathit{exp}$ coincide. The same is required for formal and actual parameters. This is not a limitation since we allow upward and downward casts.

2.2.2 Semantics

The semantics of the language is defined by means of frames, objects, and memories defined as follows:

\begin{align*}\mathord{\mathit{Frame}}_\tau & =\{\phi\mid\phi\in dom(\tau)\to\mathord{\mathit{Loc}}\cup\{{\mathord{\mathtt{null}}}\}\}\\\mathord{\mathit{Obj}} & =\{\kappa\mathbin{\star}\phi\mid\kappa\in\mathcal{K}, \ \phi\in\mathord{\mathit{Frame}}_{\kappa}\}\\\mathord{\mathit{Memory}} & =\{\mu\in\mathord{\mathit{Loc}}\nrightarrow\mathord{\mathit{Obj}}\mid dom(\mu)\text{ is finite}\}\end{align*}

where $\mathord{\mathit{Loc}}$ is an infinite set of locations. A frame binds identifiers to locations or ${\mathord{\mathtt{null}}}$ . A memory binds such locations to objects, which contain a class tag and the frame for their fields. A new object of class $\kappa$ is $\mathtt{new}(\kappa)=\kappa\mathbin{\star}\phi$ , with $\phi(v)={\mathord{\mathtt{null}}}$ for each $v\in dom(\kappa)$ .

Example 2. Let $\tau=[v_7\mapsto \mathtt{Tree}, v_8\mapsto \mathtt{Tree}]$ and consider the state depicted in Figure 10, whose abstraction is in Figure 11. We have that $\phi = [ v_7\mapsto l_0, v_8\mapsto l_1]$ and

\begin{align*} \mu = [ & l_0 \mapsto \mathtt{Tree}\mathbin{\star}[{\mathord{\mathtt{l}}}\mapsto l_2, {\mathord{\mathtt{r}}}\mapsto l_3], l_1 \mapsto \mathtt{Tree}\mathbin{\star}[{\mathord{\mathtt{l}}}\mapsto l_4, {\mathord{\mathtt{r}}}\mapsto {\mathord{\mathtt{null}}}],\\ & l_2 \mapsto \mathtt{Tree}\mathbin{\star}[{\mathord{\mathtt{l}}}\mapsto {\mathord{\mathtt{null}}}, {\mathord{\mathtt{r}}}\mapsto {\mathord{\mathtt{null}}}], l_3 \mapsto \mathtt{Tree}\mathbin{\star}[{\mathord{\mathtt{l}}}\mapsto {\mathord{\mathtt{null}}}, {\mathord{\mathtt{r}}}\mapsto l_5],\\ & l_4 \mapsto \mathtt{Tree}\mathbin{\star}[{\mathord{\mathtt{l}}}\mapsto l_6, {\mathord{\mathtt{r}}}\mapsto {\mathord{\mathtt{null}}}], l_5 \mapsto \mathtt{Tree}\mathbin{\star}[{\mathord{\mathtt{l}}}\mapsto {\mathord{\mathtt{null}}}, {\mathord{\mathtt{r}}}\mapsto l_7], \\ & l_6 \mapsto \mathtt{Tree}\mathbin{\star}[{\mathord{\mathtt{l}}}\mapsto l_7, {\mathord{\mathtt{r}}}\mapsto {\mathord{\mathtt{null}}}], l_7 \mapsto \mathtt{Tree}\mathbin{\star}[{\mathord{\mathtt{l}}}\mapsto {\mathord{\mathtt{null}}}, {\mathord{\mathtt{r}}}\mapsto {\mathord{\mathtt{null}}}] ] \enspace .\end{align*}

Figure 10.

A concrete state with variables $v_7,v_8$ .

Figure 11.

Abstraction of the concrete state in Figure 10.

Our language is strongly typed, which means that the static type of an expression should be consistent with its runtime type. We formalize here the notion of type correctness for a frame and a memory.

Definition 3 (Types of locations) Let $\phi\in\mathord{\mathit{Frame}}_\tau$ , $\mu\in\mathord{\mathit{Memory}},$ and $l \in dom(\mu)$ , we write $\tau(l)=\mu(l).\kappa$ for the runtime type of location l.

If v is a variable, the object associated with v should be of a subtype of the static type of v. This leads to the definition of weak correctness:

Definition 4 (Weak $\tau$ -correctness) Let $\phi\in\mathord{\mathit{Frame}}_\tau$ and $\mu\in\mathord{\mathit{Memory}}$ . We say that $\phi \mathbin{\star} \mu$ is weakly $\tau$ -correct if for every $v\in dom(\phi)$ such that $\phi(v)\not={\mathord{\mathtt{null}}}$ we have $\phi(v)\in dom(\mu)$ and $\tau(\phi(v))\le\tau(v)$ .

We strengthen the correctness notion of Definition 4 by requiring that it also holds for the fields of the objects in memory.

Definition 5 ( $\tau$ -correctness) Let $\phi\in\mathord{\mathit{Frame}}_\tau$ and $\mu\in\mathord{\mathit{Memory}}$ . We say that $\phi \mathbin{\star} \mu$ is $\tau$ -correct and write $\phi\mathbin{\star}\mu:\tau$ , if

1. (1). $\phi \mathbin{\star} \mu$ is weakly $\tau$ -correct and,

2. (2). for every $o\in rng(\mu)$ , $o.\phi \mathbin{\star} \mu$ is weakly $o.\kappa$ -correct.

We call state a pair $\phi \mathbin{\star} \mu$ which is $\tau$ -correct for some type environment $\tau$ . The set of $\tau$ -correct states is

\begin{equation*} \Sigma_\tau=\{\phi\mathbin{\star}\mu\mid \phi\in\mathord{\mathit{Frame}}_\tau,\ \mu\in\mathord{\mathit{Memory}},\ \phi\mathbin{\star}\mu:\tau\} .\end{equation*}

Example 6 (Noncorrect state) Let $\tau \in \mathord{\mathit{TypEnv}}=[ u \mapsto \mathtt{Tree}, v \mapsto \mathtt{Tree}, w \mapsto \mathtt{Integer} ]$ , $\phi \in \mathord{\mathit{Frame}}_\tau = [ u \mapsto l_0, v \mapsto {\mathord{\mathtt{null}}}, w \mapsto l_1 ]$ and $\mu \in \mathord{\mathit{Memory}}$ such that $\mu(l_1)=\mathtt{Integer} \mathbin{\star} []$ and $\mu(l_0) = \mathtt{Tree} \mathbin{\star} [ {\mathord{\mathtt{l}}} \mapsto l_0, {\mathord{\mathtt{r}}} \mapsto l_1 ]$ . This may be depicted graphically as follows:

It turns out that $\phi \mathbin{\star} \mu$ is weakly $\tau$ -correct, but it is not $\tau$ -correct, since the right child of u points to an integer node instead of a tree node.

The semantics of an expression is a partial map $\mathcal{E}_{\tau}^{I}\unicode{x0301A}{\mathit{exp}}\unicode{x0301B}: \Sigma_\tau\nrightarrow\Sigma_{\tau+\mathit{exp}}$ from an initial to a final state, containing a distinguished variable ${\mathord{\mathtt{res}}}$ holding the value of the expression, where $\tau+\mathit{exp}=\tau[{\mathord{\mathtt{res}}}\mapsto \tau(\mathit{exp})]$ .

Definition 7 (Semantics of expressions) Let $\tau$ describe the variables in scope and I be an interpretation. The semantics for expressions $\mathcal{E}_{\tau}^{I}\unicode{x0301A}{\mathit{exp}}\unicode{x0301B}: \Sigma_\tau \nrightarrow\Sigma_{\tau+\mathit{exp}}$ is defined as

\begin{align*} \mathcal{E}_{\tau}^{I}\unicode{x0301A}{{\mathord{\mathtt{null}}}\ \kappa}\unicode{x0301B}(\phi\mathbin{\star}\mu) &=\phi[{\mathord{\mathtt{res}}}\mapsto{\mathord{\mathtt{null}}}]\mathbin{\star}\mu \\ \mathcal{E}_{\tau}^{I}\unicode{x0301A}{\mathtt{new}\ \kappa}\unicode{x0301B}(\phi\mathbin{\star}\mu) &=\phi[{\mathord{\mathtt{res}}}\mapsto l]\mathbin{\star}\mu[l\mapsto\mathtt{new}(\kappa)]\quad \text{with $l\in\mathord{\mathit{Loc}}\setminus dom(\mu)$} \\ \mathcal{E}_{\tau}^{I}\unicode{x0301A}{{v}}\unicode{x0301B}(\phi\mathbin{\star}\mu)&=\phi[{\mathord{\mathtt{res}}}\mapsto\phi(v)]\mathbin{\star}\mu \\ \mathcal{E}_{{\tau}}^{I}\unicode{x0301A}{v.{\mathord{\mathtt{f}}}}\unicode{x0301B}(\phi\mathbin{\star}\mu)&=\begin{cases} \phi[{\mathord{\mathtt{res}}}\mapsto \phi(v).{\mathord{\mathtt{f}}}]\mathbin{\star}\mu & \text{if $\phi(v)\not={\mathord{\mathtt{null}}}$} \\ \bot & \text{otherwise} \end{cases}\\ \mathcal{E}_{\tau}^{I}\unicode{x0301A}{\mathtt{(}\kappa\mathtt{)}v}\unicode{x0301B}(\phi\mathbin{\star}\mu)&= \begin{cases} \phi[{\mathord{\mathtt{res}}}\mapsto \phi(v)]\mathbin{\star}\mu & \text{if $\phi(v)={\mathord{\mathtt{null}}}$ or}\\ & \text{$\phi(v)\not={\mathord{\mathtt{null}}}$ and $\{\tau(\phi(v)),\kappa\}$ is a chain}\\ \bot & \text{otherwise} \end{cases} \\ \mathcal{E}_{\tau}^{{I}}\unicode{x0301A}{v.\mathtt{m(}v_1,\ldots,v_n\mathtt{)}}\unicode{x0301B}(\phi\mathbin{\star}\mu) &=\begin{cases} \phi[{\mathord{\mathtt{res}}}\mapsto\phi'({\mathord{\mathtt{out}}})]\mathbin{\star}\mu' & \text{if $\phi(v)\not={\mathord{\mathtt{null}}}$} \\ \bot & \text{otherwise} \end{cases} \end{align*}

with $\phi'\mathbin{\star}\mu'=I(\tau(v).{\mathord{\mathtt{m}}})(\sigma^\unicode{x2020})$ and $\sigma^\unicode{x2020}= [{\mathord{\mathtt{this}}}\mapsto\phi(v),w_1\mapsto\phi(v_1),\ldots, w_n\mapsto\phi(v_n)]\mathbin{\star}\mu$ .

The semantics of a command is a partial map from an initial to a final state: $\mathcal{C}_{{\tau}}^{{I}}\unicode{x0301A}{{\mathit{com}}}\unicode{x0301B}:\Sigma_\tau\nrightarrow\Sigma_\tau$ . We assume that $\tau$ in both $\mathcal{E}_{\tau}^{I}\unicode{x0301A}{\_}\unicode{x0301B}$ and $\mathcal{C}_{{\tau}}^{{I}}\unicode{x0301A}{{\_}}\unicode{x0301B}$ does not contain the variable ${\mathord{\mathtt{res}}}$ .

Definition 8 (Semantics of commands) Let $\tau$ describe the variables in scope, I be an interpretation and

\begin{align*} \mathit{setVar}_\tau^v&=\lambda(\phi\mathbin{\star}\mu)\in\Sigma_\tau. \phi|_{-{\mathord{\mathtt{res}}}}[v\mapsto\phi({\mathord{\mathtt{res}}})]\mathbin{\star}\mu\\ \mathit{setField}_\tau^{v.{\mathord{\mathtt{f}}}}&= \lambda(\phi\mathbin{\star}\mu)\in\Sigma_\tau.\begin{cases} \phi|_{-{\mathord{\mathtt{res}}}}\mathbin{\star}\mu[l\mapsto\mu(l)[{\mathord{\mathtt{f}}}\mapsto\phi({\mathord{\mathtt{res}}})]] & \text{if $\phi(v)=l\neq{\mathord{\mathtt{null}}}$} \\ \bot & \text{otherwise.} \end{cases} \end{align*}

The semantics for commands $\mathcal{C}_{{\tau}}^{{I}}\unicode{x0301A}{{\mathit{com}}}\unicode{x0301B}:\Sigma_\tau\nrightarrow\Sigma_\tau$ is defined as

\begin{align*} \mathcal{C}_{{\tau}}^{{I}}\unicode{x0301A}{{v\mathbin{\mathtt{\unicode{x02254}}}\mathit{exp}}}\unicode{x0301B}&= \mathit{setVar}_{\tau+\mathit{exp}}^v \circ \mathcal{E}_{\tau}^{I}\unicode{x0301A}{\mathit{exp}}\unicode{x0301B} \\ \mathcal{C}_{{\tau}}^{I}\unicode{x0301A}{v.{\mathord{\mathtt{f}}} \mathbin{\mathtt{\unicode{x02254}}}\mathit{exp}}\unicode{x0301B}&= \mathit{setField}_{\tau+\mathit{exp}}^{v.{\mathord{\mathtt{f}}}} \circ \mathcal{E}_{\tau}^{I}\unicode{x0301A}{\mathit{exp}}\unicode{x0301B}\\ \mathcal{C}_{{\tau}}^{{I}}\unicode{x0301A}{{\begin{aligned} \mathtt{if\ v=w\ then\ }\mathit{com}_1\\ \mathtt{else\ }\mathit{com}_2 \end{aligned}}}\unicode{x0301B}(\phi\mathbin{\star}\mu)&=\begin{cases} \mathcal{C}_{\tau}^{I}\unicode{x0301A}{{\mathit{com}_1}}\unicode{x0301B}(\phi\mathbin{\star}\mu) & \text{if $\phi(v)=\phi(w)$} \\ \mathcal{C}_{{\tau}}^{I}\unicode{x0301A}{{\mathit{com}_2}}\unicode{x0301B}(\phi\mathbin{\star}\mu) & \text{if $\phi(v)\not=\phi(w)$} \end{cases} \\ \mathcal{C}_{{\tau}}^{{I}}\unicode{x0301A}{{\begin{aligned} \mathtt{if\ v=null\ then\ }\mathit{com}_1\\ \mathtt{else\ }\mathit{com}_2 \end{aligned}}}\unicode{x0301B}(\phi\mathbin{\star}\mu)&=\begin{cases} \mathcal{C}_{\tau}^{I}\unicode{x0301A}{\mathit{com}_1}\unicode{x0301B}(\phi\mathbin{\star}\mu) & \text{if $\phi(v)={\mathord{\mathtt{null}}}$} \\ \mathcal{C}_{\tau}^{i}\unicode{x0301A}{{\mathit{com}_2}}\unicode{x0301B}(\phi\mathbin{\star}\mu) & \text{if $\phi(v)\not={\mathord{\mathtt{null}}}$} \end{cases}\\ \mathcal{C}_{\tau}^{I}\unicode{x0301A}{\{\mathit{com}_1;\ldots;\mathit{com}_p\}}\unicode{x0301B}&= (\lambda\sigma\in\Sigma_\tau.\sigma) \circ\mathcal{C}_{{\tau}}^{I}\unicode{x0301A}{{\mathit{com}_p}}\unicode{x0301B}\circ\cdots\circ \mathcal{C}_{\tau}^{{I}}\unicode{x0301A}{\mathit{com}_1}\unicode{x0301B}. \end{align*}

The identity function $\lambda\sigma\in\Sigma_\tau.\sigma$ in the semantics of the sequence of commands is needed when $p=0$ .

Each method $\kappa.{\mathord{\mathtt{m}}}$ is denoted by a partial function from input to output states and an interpretation I maps methods to partial functions on states, such that $I({\kappa.{\mathord{\mathtt{m}}}}):\Sigma_{\mathit{input}(\kappa.{\mathord{\mathtt{m}}})}\nrightarrow\Sigma_{\mathit{output}(\kappa.{\mathord{\mathtt{m}}})}$ , with the type environments:

\begin{align*}\mathit{input}(\kappa.{\mathord{\mathtt{m}}}) & = [{\mathord{\mathtt{this}}}\mapsto\kappa,w_1\mapsto\kappa_1,\ldots,w_n\mapsto\kappa_n] \\\mathit{output}(\kappa.{\mathord{\mathtt{m}}})&=[{\mathord{\mathtt{out}}}\mapsto\kappa_0,w'_1\mapsto\kappa_1,\ldots,w'_n\mapsto\kappa_n] \mbox{ and}\\\mathit{scope}(\kappa.{\mathord{\mathtt{m}}})&=\begin{aligned}[t]\mathit{input}(\kappa.{\mathord{\mathtt{m}}})& \cup \mathit{output}(\kappa.{\mathord{\mathtt{m}}}) \\& \cup [w_{n+1}\mapsto\kappa_{n+1},\ldots,w_{n+m}\mapsto \kappa_{n+m}]\end{aligned}\end{align*}

where $w'_1,\ldots,w'_n$ are fresh variables used to keep track of the actual parameters. Each $w'_i$ is automatically assigned to the same value of the corresponding $w_i$ at the beginning of the method execution, and it is never changed later.

Example 9. Consider the method ${\mathord{\mathtt{makeTree}}}$ in Section 1.4. We have that

\begin{align*} \mathit{input}(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}}) & =[{\mathord{\mathtt{this}}} \mapsto \mathtt{Tree}, n \mapsto \mathtt{Integer}] \\ \mathit{output}(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}}) & = [{\mathord{\mathtt{out}}} \mapsto \mathtt{Tree},n' \mapsto \mathtt{Integer}]\\ \mathit{scope}(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}}) & = \begin{aligned}[t] \mathit{input}(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}}) & \cup \mathit{output}(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}}) \cup [m \mapsto \mathtt{Integer}] . \end{aligned} \end{align*}

The denotational semantics of a program is the least fixpoint of a transformer on interpretations which maps an interpretation I into a new interpretation I’ evaluating the bodies of the methods in I from an input state where local variables are bound to ${\mathord{\mathtt{null}}}$ . If $\kappa.{\mathord{\mathtt{m}}}$ is defined as

\begin{equation*}\mathtt{\kappa_0\ m(}w_1\!:\!\kappa_1,\ldots,w_n\!:\!\kappa_n\mathtt{)}\mathtt{\ with\ }w_{n+1}\!:\!\kappa_{n+1},\ldots,w_{n+m}\!:\!\kappa_{n+m}\mathtt{\ is\ }\mathit{com}\end{equation*}

we have

\begin{equation*}\begin{array}{lll}I'(\kappa.{\mathord{\mathtt{m}}}) &= &(\lambda\phi\mathbin{\star}\mu\in\Sigma_{\mathit{scope}(\kappa.{\mathord{\mathtt{m}}})}.\,\phi|_{dom(\mathit{output}(\kappa.{\mathord{\mathtt{m}}}))}\mathbin{\star}\mu) \, \circ \, \mathcal{C}_{\mathit{scope}(\kappa.{\mathord{\mathtt{m}}})}^{I}\unicode{x0301A}{\mathit{body}(\kappa.{\mathord{\mathtt{m}}})}\unicode{x0301B} \circ \\&&(\lambda\phi\mathbin{\star}\mu\in\Sigma_{\mathit{input}(\kappa.{\mathord{\mathtt{m}}})}.\,\phi[{\mathord{\mathtt{out}}}\mapsto{\mathord{\mathtt{null}}}, w'_1 \mapsto \phi(w_1),\ldots, w'_n \mapsto \phi(w_n),\\&& \quad \quad w_{n+1}\mapsto{\mathord{\mathtt{null}}},\ldots,w_{n+m}\mapsto{\mathord{\mathtt{null}}}]\mathbin{\star}\mu) \enspace .\end{array}\end{equation*}

3.1 Reachability among identifiers

As we said before, we want to record sharing and linearity information not only for variables in the type environment but also for their fields. Therefore, we introduce some notation to treat variables and their fields as uniformly as possible.

It is worth noting that we only consider fields that are in the declared type of the variables, and we do not consider further fields that are in the actual type. This choice, although it may decrease the precision of the analysis, simplifies a lot the correspondence between abstract and concrete semantics and may increase the speed of the analysis. Note that since $dom(\tau)$ is finite, we have that $I_\tau$ is also finite.

The following proposition states that the runtime type of a qualified identifiers is a subtype of its declared type.

We now lift the definitions and properties of sharing and linearity from locations to identifiers.

• • $i_1 $ and $i_2$ share in $\sigma$ when $\phi(i_1) \neq {\mathord{\mathtt{null}}} \neq \phi(i_2)$ and $\phi(i_1)$ , $\phi(i_2)$ share in $\sigma$ ;

• • $i_1$ is nonlinear in $\sigma$ when $\phi(i_1) \neq{\mathord{\mathtt{null}}}$ and $\phi(i_1)$ is nonlinear in $\sigma$ ; otherwise, $i_1$ is said to be linear.

• • $i_1 $ and $i_2$ are (weakly) aliased in $\sigma$ when $\phi(i_1)=\phi(i_2)$ .

Note that two identifiers that are both ${\mathord{\mathtt{null}}}$ are considered to be weakly aliased.

Note that a qualified identifier $i \in I_\tau$ shares with itself if and only if it is not ${\mathord{\mathtt{null}}}$ . Moreover, each $i \in I_\tau$ such that $\phi(i)= {\mathord{\mathtt{null}}}$ is linear and does not share with any other identifier.

Note that the reachability set for a variable is related to the reachability set of its qualified fields. This is formalized by the following result.

Proposition 25. Let $\sigma = \phi \mathbin{\star} \mu \in \Sigma_\tau$ and $v \in dom(\tau)$ . If $\phi(v)\neq{\mathord{\mathtt{null}}}$ , then

\begin{equation*} \mathord{\mathit{RLoc_\sigma}}(v) \supseteq \{ \phi(v) \} \cup \bigcup_{v.{\mathord{\mathtt{f}}} \in Q_\tau} \mathord{\mathit{RLoc_\sigma}}(v.{\mathord{\mathtt{f}}}) \enspace . \end{equation*}

Equality does not hold since there is some sharing information in $\mathord{\mathit{RLoc_\sigma}}(v)$ which is not derivable from the sharing information of its fields. This is due to the fact that we consider only fields in the declared type of a variables. Thus, further sharing relationships may exist in other fields which do not belong to the declared type.

3.2 Class-induced reachability

It must be observed that two qualified identifiers might never be able to share if their static types do not let them be bound to overlapping data structures. Analogously, certain qualified identifiers are forced to be linear.

Then, every object of class $\mathtt{A}$ is linear.

Identifying pair of classes which cannot share, or that are forced to be linear, may improve the result of the analysis. This is the topic of the rest of this section.

In Definition 28, if a class $\kappa'$ (different from $\kappa$ ) is reachable from $\kappa$ , then all its subclasses $\downarrow\kappa'$ are considered reachable. This reflects the fact that we consider a language with (checked) casts. The following proposition relates reachability with class reachability: if location $l_2$ is reachable from $l_1$ , the type of $l_2$ should be reachable from the type of $l_1$ .

This notion of class reachability corresponds to the one in Secci and Spoto (Reference Secci and Spoto2005a) if we denote by $C(\kappa)$ the set of classes reachable by $\downarrow \kappa$ , that is,

\begin{equation*}C(\kappa) = \{ \kappa' \mid \kappa'' \in \downarrow \kappa\text{ and } \kappa'' \xrightarrow{*} \kappa' \} \enspace .\end{equation*}

We introduce this alternative notation since it will be convenient in the next definitions.

We will denote by NL the set of classes whose instances may be nonlinear and by ${SH} \,$ the set of pair of classes which may share. Both NL and SH may be computed using class reachability, that is, by typing information only.

First of all, note that identifiers $i_1$ and $i_2$ may share only if there is a common location l which is reachable both from $\phi(i_1)$ and $\phi(i_2)$ . Therefore, the class $\tau(l)$ should be class reachable from both $\tau(\phi(i_1))$ and $\tau(\phi(i_2))$ . This is formalized by the following:

Definition 31 (The SH set) Given the type environment $\tau$ , we define the set of pairs of classes which may share:

\begin{equation*} {SH}=\{ (\kappa, \kappa') \mid C(\kappa) \cap C(\kappa') \neq \emptyset \} \enspace . \end{equation*}

We now consider the problem of linearity induced by type information. In general, an object of class $\kappa$ may be nonlinear either if $\kappa$ has two fields which may share, it has a field which may share with itself or if a nonlinear class $\kappa'$ is reachable from $\kappa$ . This is formalized by the following:

Definition 33 (The NL set) The set NL of nonlinear classes is the upward closure of the least solution of the equation:

\begin{multline*}\mathcal S = \{ \kappa \mid C(\kappa.{\mathord{\mathtt{f}}}_1) \cap C(\kappa.{\mathord{\mathtt{f}}}_2) \neq \emptyset,{\mathord{\mathtt{f}}}_1 \neq {\mathord{\mathtt{f}}}_2 \}\cup \{ \kappa \mid \kappa \in C(\kappa.{\mathord{\mathtt{f}}}) \} \cup \{ \kappa \mid \kappa\xrightarrow{} \kappa', \kappa' \in \mathcal S \}\enspace .\end{multline*}

Note that $\mathit{NL}$ is upward closed by definition. If $\kappa$ is possibly nonlinear, the same holds for any $\kappa' \geq \kappa$ since a variable of type $\kappa'$ may actually point to an object of class $\kappa$ .

In the following sections, we use the concepts of sharing, linearity, and aliasing introduced before to define a new abstract domain, called $\mathsf{ALPS}$ (Aliasing Linearity Pair Sharing), for the analysis of Java-like programs.

4.1 Morphisms of aliasing graphs

We give in this section a different characterization of the preordering over aliasing graphs.

This notion of morphism respects the intended meaning of aliasing graphs. If $h: G_1 \rightarrow G_2$ and $\ell_1(v) = \bot$ , then $\ell_2(v)=\bot$ . Moreover, if $\ell_1(v)=\ell_1(w)$ , then $\ell_2(v)=\ell_2(w)$ . Aliasing morphisms enjoys many interesting properties. In particular,

It is often easier to think in terms of morphism than to check whether Definition 38 holds: most of the proofs of the properties in this section use graph morphisms and the characterization in Theorem 42. Moreover, morphisms will be pivotal in Section 5 when comparing sharing and linearity information attached to different aliasing graphs.

and the type environment $\tau = [a \mapsto \mathtt{A}, b \mapsto \mathtt{B}]$ . Consider the aliasing graphs $G_1$ and $G_2$ , respectively, in Figure 13A and B. There is a morphism $h: G_2 \to G_1$ given by $\{n_3 \mapsto n_1, n_6 \mapsto n_1, n_4 \mapsto n_2 \}$ , since

\begin{gather*} h(\ell_2(a))= h(n_3) = n_1 = \ell_1(a) \qquad h(\ell_2(b))= h(n_6) = n_1 = \ell_1(b) \\ h(\ell_2(a.{\mathord{\mathtt{l}}}))=h(n_4) = n_2 = \ell_1(a.{\mathord{\mathtt{l}}}) \qquad h(\ell_2(a.{\mathord{\mathtt{r}}}))=h(n_5)=\bot = \ell(a.{\mathord{\mathtt{r}}}) \end{gather*}

By Theorem 42, we have that $G_1 \preceq G_2$ .

Figure 13.

Comparison of aliasing graphs. We have explicitly annotated each node with its identity.

4.2 The lattice of aliasing graphs

We now show that $\preceq$ for aliasing graphs has least upper bounds and greatest lower bounds, and we show how to build them.

We begin by defining a new aliasing graph $G_1 \curlyvee G_2$ , which we will later prove to be the least upper bound. In the definition, we use the inverse function $\ell^{-1}$ . Note that $\ell^{-1}$ may be different if we consider $\ell$ as the map $\ell: dom(\tau) \nrightarrow N$ in Definition 35 or as the map $\ell: I_\tau \nrightarrow N$ in Definition 37. When it is not specified, the latter is assumed.

• • $N = X/\mathrm{\sim}$ is the set of equivalence classes of X;

• • for any $v \in dom(\tau)$ , $\ell(v)=[v]_\sim$ if $v \in X$ , $\ell(v)=\bot$ otherwise;

• • $S_1 \xrightarrow{{\mathord{\mathtt{f}}}} S_2 \in E$ iff there exists $v \in S_1$ s.t. $v.{\mathord{\mathtt{f}}} \in S_2$ .

Example 45. Let us consider the same classes and type environment of Example 43. Figure 14 shows an example of lub of aliasing graphs. Note that, even without knowing the class definitions, the graphs contain enough information to justify the result of the operation. For example, in Figure 14A, the fact that the left child of the node ab does not contain $b.{\mathord{\mathtt{l}}}$ means that that ${\mathord{\mathtt{l}}}$ is not a field of $\tau(b)$ , hence $\tau(a)$ is a subclass of $\tau(b)$ .

Figure 14.

Least upper bound of aliasing graphs.

An analogous, although more complex, definition may be given for $G_1 \curlywedge G_2$ , which we will later prove to be the greatest lower bound of aliasing graphs.

• • $N \subseteq \{[i]_\sim \mid i \in I_\tau, \tau([i]_\sim) \text{ is a chain and } [i]_{\sim} \subseteq \ell_1^{-1}(N_1) \cap \ell_2^{-1}(N_2)\}$ ;

• • if $[v]_\sim \notin N$ , then $[v.{\mathord{\mathtt{f}}}]_\sim \notin N$ .

We define the aliasing graph $G_1 \curlywedge G_2 = N \mathbin{\star} E \mathbin{\star} \ell$ where

• • $\ell: dom(\tau) \nrightarrow N$ such that $\ell(v)=[v]_\sim$ if $[v]_\sim \in N$ , $\ell(v)=\bot$ otherwise;

• • $S_1 \xrightarrow{{\mathord{\mathtt{f}}}} S_2 \in E$ iff there exists $v \in dom(\tau), v \in S_1$ s.t. $v.{\mathord{\mathtt{f}}} \in S_2$ .

The definition above is similar to the one for $\curlyvee$ : we start from defining an equivalence relation $\sim$ which propagates weak aliasing and we define $G_1 \curlywedge G_2$ whose nodes are equivalence classes of identifiers modulo $\sim$ . However, propagation of nullness is more complex. There are several situations which may force an identifier to be null.

• • It may be that $i_1 \sim i_2$ , but $i_2$ is null in either $G_1$ or $G_2$ . In this case, both $i_1$ and $i_2$ are forced to be null. This is the reason of the condition “ $[i]_{\sim} \subseteq \ell_1^{-1}(N_1) \cap \ell_2^{-1}(N_2)$ ” in the first clause of the definition for N.

• • Assume $\tau(i) = \kappa$ , $\tau(i_1)=\kappa_1$ , and $\tau(i_2)=\kappa_2$ , such that $\kappa_1 \leq \kappa$ , $\kappa_2 \leq \kappa$ with $\kappa_1$ and $\kappa_2$ incomparable. It may happen that $\ell_1(i)=\ell_1(i_1)$ and $\ell_2(i)=\ell_2(i_2)$ , which implies $i \sim i_1 \sim i_2$ . However, this forces i, $i_1$ , and $i_2$ to be null, since there is no object which is both an element of $\kappa_1$ and $\kappa_2$ . This is cared by the condition “ $\tau([i]_\sim)$ is a chain” in the first clause of the definition for N.

• • If variable v is forced to be null for one of the reasons above, then fields of v cannot exist. This is cared of by the second clause of the definition for N.

and the type environment $\tau = [a \mapsto \mathtt{A}, b \mapsto \mathtt{B}]$ . Figure 15 shows an example of glb of aliasing graphs.

Figure 15.

Greatest lower bound of aliasing graphs.

The following theorem proves that $\curlyvee$ and $\curlywedge$ are actually the least upper bound and greatest lower bound of aliasing graphs.

• • a least element $\bot_\tau = \emptyset \mathbin{\star} \emptyset \mathbin{\star} \bot$ where $\bot$ is the always undefined map;

• • a greatest element $\top_\tau = I_\tau \mathbin{\star} E \mathbin{\star} {id}$ where $n_1 \xrightarrow{{\mathord{\mathtt{f}}}} n_2 \in E \iff n_1 = v \in dom(\tau) \wedge n_2= v.{\mathord{\mathtt{f}}} \in Q_\tau$ ;

• • a least upper bound $G_1 \curlyvee G_2$ for each $G_1, G_2 \in \mathcal{G}_\tau$ ;

• • a greatest lower bound $G_1 \curlywedge G_2$ for each $G_1, G_2 \in \mathcal{G}_\tau$ .

4.3 Projection

Several operations may be defined on aliasing graphs. Most of them will be introduced later, since they depend on the concrete semantics of our language. We introduce here only the operation of restriction of a graph to a subset of variables.

Definition 49 (Projection) Given a pre-aliasing graph G and a set of nodes X, we denote by $G|_X$ the tuple $X \mathbin{\star} E' \mathbin{\star} \ell'$ where

\begin{equation*} E' = E \cap (X \times \mathord{\mathit{Ide}} \times X) \qquad \ell'(v) = \begin{cases} \ell(v) & \text{if $\ell(v) \in X$},\\ \bot & \text{otherwise} \end{cases} \end{equation*}

It is immediate to check that $G|_X$ is a pre-aliasing graph. Given a pre-aliasing graph $G = N \mathbin{\star} E \mathbin{\star} \ell$ , a set of nodes $X \subseteq N$ is said to be backward closed when, for each $n \in X$ , if there exists $n' \xrightarrow{{\mathord{\mathtt{f}}}} n \in E$ , then $n' \in X$ . Given a set of nodes X, we denote by $\overleftarrow X$ the smallest backward closed set of nodes containing X. Symmetrically, we define forward closed sets and the forward closure operator $\overrightarrow X$ .

It turns out that if X is backward closed, then $G|_X \preceq G$ . Moreover, if G is an aliasing graph, $G|_X$ is too. More precisely, the following hold:

We will come back to this point when we introduce the abstract semantics.

4.4 The domain of aliasing graphs

Given a concrete state $\sigma \in \Sigma_\tau$ , we may abstract it into an aliasing graph which conveys the relevant information.

• • $N =\{ l \in \mathord{\mathit{Loc}} \mid \exists i \in I_\tau. \phi(i)=l \}$ ;

• • for each $v \in dom(\tau)$ , $\ell(v) = \phi(v)$ if $\phi(v) \neq {\mathord{\mathtt{null}}}$ , $\ell(v)=\bot$ otherwise;

• • $l \xrightarrow{{\mathord{\mathtt{f}}}} l' \in E$ iff there exists $v \in dom(\tau)$ such that $\ell(v)=l$ , $v.{\mathord{\mathtt{f}}} \in Q_\tau$ and $l.{\mathord{\mathtt{f}}}=l'$ .

The abstraction of a state $\sigma$ is essentially the representation of the environment and stores as an aliasing graph, limited to the locations reachable from a qualified identifier.

The following proposition shows that the abstraction of a concrete state is an aliasing graph.

We say that $G \in \mathcal{G}_\tau$ is a correct abstraction of $\sigma=\phi \mathbin{\star} \mu \in \Sigma_\tau$ iff $\alpha_a(\sigma) \preceq G$ . Note that if $\alpha_a(\sigma) \preceq G$ and $G.\ell(i)=\bot$ , then $\phi(i) = {\mathord{\mathtt{null}}}$ , hence $\ell$ may actually be used to represent definite nullness. Moreover, if $G.\ell(i_1)=G.\ell(i_2)$ , then either $\phi(i_1)=\phi(i_2) \in \mathord{\mathit{Loc}}$ or $\phi(i_1)=\phi(i_2)={\mathord{\mathtt{null}}}$ . Hence, $\ell$ actually encodes definite weak aliasing between variables.

The following propositions show that each aliasing graph can be viewed as the abstraction of a concrete state.

The map $\alpha_a$ of Definition 52 may be lifted to the abstraction map of a Galois insertion from $\mathcal{P}(\Sigma_\tau)$ to $\mathcal{G}_\tau$ given by $\alpha_a(S)= \unicode{x0059}_{\sigma \in S} \alpha_a(\sigma)$ . The abstraction map induces the concretization map $\gamma_a: \mathcal{G}_\tau \rightarrow \mathcal{P}(\Sigma_\tau)$ , which maps aliasing graphs to the set of concrete states they represent. Its explicit definition, below, is straightforward:

\begin{equation*}\gamma_a(G) = \{ \sigma \in \Sigma_\tau \mid \alpha_a(\sigma) \preceq G \} .\end{equation*}

The above theorem may be considered the analogous of injectivity of $\gamma_a$ when the abstract domain is preordered instead of partially ordered. It allows to prove that $\gamma_a(G_1) \subseteq\gamma_a(G_2)$ by just checking $G_1 \preceq G_2$ .

5.1 Projection

Analogously to aliasing graphs, we define a projection operator for $\mathsf{ALPS}$ graphs.

Definition 63 (Projection of pre- $\mathsf{ALPS}$ graphs) Given a pre- $\mathsf{ALPS}$ graph $\mathbb{G}$ and $X \subseteq N$ backward closed, we denote by $\mathbb{G}|_X$ the pre- $\mathsf{ALPS}$ graph $G|_X \mathbin{\star} \mathit{sh}' \mathbin{\star} \mathit{nl}'$ where

\begin{equation*} \mathit{sh}' = \mathit{sh} \cap \mathcal{P}_2(X) \qquad \mathit{nl}' = \mathit{nl} \cap X \enspace . \end{equation*}

In the definition above, the hypothesis that X is backward closed is needed in order to ensure that $G|_X$ is an aliasing graph and not just a pre-aliasing graph. It is immediate to check that projection maps $\mathsf{ALPS}$ graphs to $\mathsf{ALPS}$ graphs. More specifically:

Moreover, the following holds:

5.2 Up- and down-closures of pre- $\mathsf{ALPS}$ graphs

Given a pre- $\mathsf{ALPS}$ graph $\mathbb{G}$ , the up-closure of $\mathbb{G}$ is a new pre- $\mathsf{ALPS}$ graph, obtained by adding derived sharing and nonlinearity information to the $\mathit{sh}$ and $\mathit{nl}$ components. Moreover, if $\mathbb{G}$ is reduced (i.e., it does not contain spurious sharing and nonlinearity elements which are not G-SH-compatible and G-NL-compatible, respectively), then the up-closure of $\mathbb{G}$ is an $\mathsf{ALPS}$ graph.

It is immediate to see that the up-closure of a pre- $\mathsf{ALPS}$ graph $\mathbb{G}$ always exists and can be simply computed starting from $\mathit{sh} \mathbin{\star} \mathit{nl}$ and adding new elements according to the five properties in Definition 60. Note that the graph G does not change when computing the up-closure.

Symmetrically, we can define the down-closure as follows.

Note that, differently from the up-closure, when computing the down-closure the graph G can possibly change, since some nodes could be removed. The next proposition shows how to compute the down-closure.

1. (1). $\mathit{nl}^*= \mathit{nl} \setminus \{n \mid m \not\in \mathit{nl}^* \wedge m \xrightarrow{{\mathord{\mathtt{f}}}} n \in E\}$ ;

2. (2). $\mathit{sh}^* = \mathit{sh} \setminus \{ \{m_1, m_2 \} \mid n \not\in \mathit{nl}^*, n \xrightarrow{{\mathord{\mathtt{f}}}_1} m_1 \in E, n \xrightarrow{{\mathord{\mathtt{f}}}_2} m_2 \in E, {\mathord{\mathtt{f}}}_1 \neq {\mathord{\mathtt{f}}}_2\} \setminus \{\{ n, m \} \mid \{ n',m \} \not\in \mathit{sh}^* \wedge n' \xrightarrow{{\mathord{\mathtt{f}}}} n \in E \}$ .

Then, we have that

\begin{equation*}cl^{\downarrow}(\mathbb{G}) = (G \mathbin{\star} \mathit{sh}^* \mathbin{\star} \mathit{nl}^*)|_{N\setminus \overrightarrow X}\end{equation*}

where $X=\{n \mid n\not\in \mathit{nl}^*$ , there is a loop in G such that $n \xrightarrow{{\mathord{\mathtt{f}}}_1} \cdots \xrightarrow{{\mathord{\mathtt{f}}}_k} n \in E\} \cup \{n \mid \{n\} \not\in \mathit{sh}^*\}$ . Moreover, if $\mathbb{G}$ is closed w.r.t. red, then $cl^{\downarrow}(\mathbb{G})$ is an $\mathsf{ALPS}$ graph.

The down-closure shows the interaction between the three components of aliasing (which is encoded in the graph structure), sharing, and nonlinearity. It precisely describes how linearity and sharing information propagate to the other components.

The first point states that whenever a node m is linear, then all its children are linear too. The second point explains the interaction between sharing and linearity: when a node n is linear, then its children cannot share. Moreover, when a node n’ does not share with a node m, the same holds for the children of n’. Note that, whenever in a loop of the graph a node is linear, then all the nodes in the loop and all their children must be null. This is reflected in the projection on $N \setminus \overrightarrow X$ .

Example 69. Figure 17 shows the aliasing graph G and the $\mathsf{ALPS}$ graph $\mathbb{G} = G \mathbin{\star} \mathit{sh} \mathbin{\star} \mathit{nl}$ where

\begin{equation*} \mathit{sh}=\{ \{a b\}, \{c\}, \{a.{\mathord{\mathtt{r}}}\,c.{\mathord{\mathtt{l}}}\}, \{c.{\mathord{\mathtt{r}}}\}, \{a b, a.{\mathord{\mathtt{r}}}\,c.{\mathord{\mathtt{l}}} \}, \{c, a.{\mathord{\mathtt{r}}}\,c.{\mathord{\mathtt{l}}} \}, \{c, c.{\mathord{\mathtt{r}}} \}, \{ab, c\} \} \end{equation*}

\begin{equation*} \mathit{nl}=\{ c, c.{\mathord{\mathtt{r}}} \} \end{equation*}

In Figure 17B, nonlinearity is represented with a double circle, while sharing information is represented as follows:

Figure 17.

Example of an $\mathsf{ALPS}$ graph.

• • the sharing information of the singletons $\{a b\}, \{c\}, \{a.{\mathord{\mathtt{r}}}\,c.{\mathord{\mathtt{l}}}\}, \{c.{\mathord{\mathtt{r}}}\}$ can be deduced from the existence of the nodes in the aliasing graph;

• • the sharing information of a node with its field, for example, $\{c, c.{\mathord{\mathtt{r}}} \}$ , can be deduced from the corresponding edge in the aliasing graph $c \xrightarrow{{\mathord{\mathtt{r}}}} c.r$ ;

• • additional sharing information is represented with a dotted line, for example, between the nodes a b and c.

5.3 The lattice of $\mathsf{ALPS}$ graphs

In order to define an abstract domain of $\mathsf{ALPS}$ graphs, we start by defining the least upper bound $\mathbb{G}_1 \curlyvee \mathbb{G}_2$ and greatest lower bound $\mathbb{G}_1 \curlywedge \mathbb{G}_2$ of $\mathsf{ALPS}$ graphs.

We use morphisms when we need to combine sharing and nonlinearity information coming from different graphs with different sets of nodes.

• • $G=G_1 \curlyvee G_2$ , with morphisms $h_1: G \rightarrow G_1$ and $h_2: G \rightarrow G_2$ ;

• • $\mathit{sh}= h_1^{-1}(\mathit{sh}_1) \cup h_2^{-1}(\mathit{sh}_2)$ and

• • $\mathit{nl} =h_1^{-1}(\mathit{nl}_1) \cup h_2^{-1}(\mathit{nl}_2)$ .

Example 71. Consider the $\mathsf{ALPS}$ graphs in Figure 18A and B. For ease of notation, we assume a node to be denoted by its label. The morphisms $h_1: G_1 \curlyvee G_2 \rightarrow G_1$ and $h_2: G_1 \curlyvee G_2 \rightarrow G_2$ are defined as follows:

\begin{equation*}h_1=[a \mapsto a b, b \mapsto a b, a.{\mathord{\mathtt{l}}} \mapsto a.{\mathord{\mathtt{l}}} ]\end{equation*}

\begin{equation*}h_2=[a \mapsto a, b \mapsto b, a.{\mathord{\mathtt{r}}} \mapsto a.{\mathord{\mathtt{r}}} ]\end{equation*}

We have that:

Figure 18.

Least upper bound of $\mathsf{ALPS}$ graphs.

• • $G' = G_1 \curlywedge G_2$ with morphisms $h_1:G_1 \rightarrow G$ and $h_2: G_2 \rightarrow G$ ;

• • $\mathit{sh}' = \{ \{n,m\} \in \mathcal{P}_2(N) \mid \forall k \in \{1,2\}\ h_k^{-1}(\{\{n,m\}\}) \subseteq \mathit{sh}_k \}$ ;

• • $\mathit{nl}' = \{ n \in N \mid \forall k \in \{1,2\}\ h_k^{-1}(n) \subseteq\mathit{nl}_k \}$ ;

Example 73. Consider the $\mathsf{ALPS}$ graphs in Figure 19A and B. For ease of notation, we assume a node to be denoted by its label. The morphisms $h_1: G_1 \rightarrow G_1 \curlywedge G_2 $ and $h_2: G_2 \rightarrow G_1 \curlywedge G_2 $ are defined as follows:

\begin{align*}h_1 & =[ ab \mapsto a b, b.{\mathord{\mathtt{r}}} \mapsto b.{\mathord{\mathtt{r}}}]\\ h_2 & =[a \mapsto a b, b \mapsto a b, b.{\mathord{\mathtt{r}}} \mapsto b.{\mathord{\mathtt{r}}} ]\end{align*}

We have that:

Figure 19.

Greatest lower bound of $\mathsf{ALPS}$ graphs.

• • a least element $\bot_\tau \mathbin{\star} \emptyset \mathbin{\star} \emptyset$ ;

• • a greatest element $\top_\tau \,\mathbin{\star} \,\mathit{sh} \,\mathbin{\star} \, \mathit{nl}$ , where

• • $\mathit{sh} = \{\{ n, m \} \in \mathcal{P}_2(I_\tau) \mid (\tau(n),\tau(m)) \in {SH}\}$ and

• • $\mathit{nl} = \{ n \in I_\tau \mid \tau(n) \in {NL}\}$ ;

• • a least upper bound $\mathbb{G}_1 \curlyvee \mathbb{G}_2$ for each pair $\mathbb{G}_1$ and $\mathbb{G}_2$ of $\mathsf{ALPS}$ graphs;

• • a greatest lower bound $\mathbb{G}_1 \curlywedge \mathbb{G}_2$ for each pair $\mathbb{G}_1$ and $\mathbb{G}_2$ of $\mathsf{ALPS}$ graphs.

With an abuse of language, we denote the top and bottom of $\mathsf{ALPS}$ graphs for the domain environment $\tau$ with $\top_{\tau}$ and $\bot_\tau$ , which are the same symbols used for aliasing graphs. We omit the index $\tau$ when it is clear from or not relevant in the context.

5.4 The domain of $\mathsf{ALPS}$ graphs

Given a concrete state $\sigma \in \Sigma_\tau$ , we may abstract it into an aliasing graph which conveys the relevant information.

Definition 75 (Abstraction map on $\mathsf{ALPS}$ graph) Given $\sigma \in \Sigma_\tau$ , we define the abstraction $\alpha: \Sigma_\tau \rightarrow \mathsf{ALPS}$ as $\alpha(\sigma)= \alpha_a(\sigma) \mathbin{\star} \mathit{sh} \mathbin{\star}\mathit{nl}$ where

\begin{align*}\mathit{sh} &= \{ \{l_1, l_2\} \subseteq N \mid l_1 \text{ and } l_2 \text{ share in } \sigma \} \enspace ,\\\mathit{nl} &= \{ l \in N \mid l \text { is not linear in } \sigma \} \enspace .\end{align*}

We say $\mathbb{G}$ is a correct abstraction of $\sigma$ when $\alpha(\sigma) \preceq \mathbb{G}$ .

The abstraction of a state $\sigma$ is essentially the representation of the environment and stores as an $\mathsf{ALPS}$ graph, limited to the locations reachable from a qualified identifier.

Given $\sigma \in \Sigma_\tau$ and $\alpha(\sigma) \preceq \mathbb{G}$ , if $i_1, i_2 \in I_\tau$ share in $\sigma$ , then $\{\mathbb{G}.\ell(i_1), \mathbb{G}.\ell(i_2)\} \in \mathbb{G}.\mathit{sh}$ . Moreover, if $i \in I_\tau$ is nonlinear in $\sigma$ , then $\mathbb{G}.\ell(i) \in \mathbb{G}.\mathit{nl}$ . Hence, $\mathbb{G}$ actually encodes possible sharing and nonlinearity among variables and fields.

We may define a concretization map $\gamma: \mathsf{ALPS}_\tau \rightarrow \mathcal{P}(\Sigma_\tau)$ which maps $\mathsf{ALPS}$ graphs to the set of concrete states they represent as $\gamma(\mathbb{G}) = \{ \sigma \in \Sigma_\tau \mid \alpha(\sigma) \preceq \mathbb{G} \}$ . If we lift the map $\alpha$ in Definition 75 to an additive map $\alpha: \mathcal{P}(\Sigma_\tau) \rightarrow \mathsf{ALPS}_\tau$ as $\alpha(S)= \unicode{x0059}_{\sigma \in S} \alpha(\sigma)$ , then $\alpha$ and $\gamma$ form a Galois connection.

Proposition 76. (Concretization of $\mathsf{ALPS}$ graphs) The concretization map induced by the abstraction map $\alpha$ satisfies the following property:

\begin{equation*}\begin{split}\gamma(\mathbb{G}) = \big\{ \sigma \in \Sigma_\tau \mid\& \sigma \in\gamma_a(G), \\& \forall i \in I_\tau.\ i \text{ nonlinear in } \sigma \Rightarrow \ell(i) \in \mathit{nl},\\& \forall i,i' \in I_\tau.\ i \text{ share with } i' \text{ in } \sigma\Rightarrow \{ \ell(i), \ell(i')\} \in \mathit{sh} \big\} \enspace .\end{split}\end{equation*}

Note that $\mathsf{ALPS}$ -graphs do not form a Galois insertion with concrete states, as shown in the following example.

Since all classes may share among them, we have $SH=\{ (\kappa, \kappa') \mid \kappa, \kappa' \in \{A, B, C\} \}$ . Let $\tau$ be the type environment $\tau=\{ x \mapsto A, y \mapsto B \}$ and consider the $\mathsf{ALPS}$ -graph $\mathbb{G}$ given by:

It turns out that there is no set of states S such that $\alpha(S)=\mathbb{G}$ . This is because, due to the set of classes we have available, x and y may only share through the class C, that is, through the fields ${\mathord{\mathtt{f}}}$ and ${\mathord{\mathtt{g}}}$ . But according to $\mathbb{G}$ , ${\mathord{\mathtt{f}}}$ and ${\mathord{\mathtt{g}}}$ should be ${\mathord{\mathtt{null}}}$ , making sharing impossible. Therefore, $\alpha$ is not surjective and $\langle \alpha, \gamma \rangle$ is a Galois connection but not a Galois insertion.

Obtaining a Galois insertion would require the addition of new closure conditions on $\mathsf{ALPS}$ graphs. This is possible, but would make the definitions more cumbersome, and we have decided not to follow this line. While Galois insertions are more theoretical appealing since they do not contain redundant abstract elements, precise and efficient analysis can be obtained even without them. In the literature of numerical abstract domains (Cousot and Cousot Reference Cousot and Cousot1976; Amato and Scozzari Reference Amato and Scozzari2012) there are many examples of analysis which do not form a Galois insertion and not even a Galois connection, such as the polyhedral analysis of imperative programs (Cousot and Halbwachs Reference Cousot and Halbwachs1978) and the parallelotope abstract domain (Amato et al. Reference Amato, Rubino and Scozzari2017), which nonetheless enjoy interesting completeness properties (Amato and Scozzari Reference Amato and Scozzari2011)

In the next section, we will define the necessary operations on the domain of $\mathsf{ALPS}$ graphs in order to define an abstract semantics for sharing analysis. Note that, since the abstract domain is finite, we do not need a widening operator to ensure the termination of the analysis.

6.1 Auxiliary operators

First of all, we introduce some auxiliary operators which will be used later in the abstract semantics for commands and expressions.

6.1.1 Pruning

Given a triple $\mathbb{G} = G \mathbin{\star} \mathit{sh} \mathbin{\star} \mathit{nl}$ (not necessarily an $\mathsf{ALPS}$ graph), the operation $\textsf{prune}$ removes extraneous nodes and adds inferred information:

\begin{equation*}\textsf{prune}(\mathbb{G})=cl^{\uparrow}(\mathbb{G}|_{N'})\end{equation*}

where $N' = \{\ell(w)\mid w\in dom(\tau)\} \cup \{n\mid w\in dom(\tau),\ell(w) \xrightarrow{{\mathord{\mathtt{f}}}} n\in E, {\mathord{\mathtt{f}}} \in dom(\tau(w)) \}$ .

6.1.2 Restriction

Consider the operation on concrete states which, given $S \subseteq \Sigma_\tau$ and a set of variables $V \subseteq dom(\tau)$ , returns the set of states in S restricted to the variables in V, that is,

\begin{equation*}S_{\Vert V} = \{\phi|_{V} \mathbin{\star} \mu \mid \phi \mathbin{\star} \mu \in S \} \subseteq\Sigma_{\tau|_{V}}\enspace .\end{equation*}

Starting from a correct abstraction $\mathbb{G} \in\mathsf{ALPS}_\tau$ of the set of states S, we would like to define a new abstraction $\mathbb{G}' \in \mathsf{ALPS}_{\tau|_{V}}$ which correctly approximates $S_{\Vert V}$ .

Definition 79 (Abstract restriction) Given $\mathbb{G} \in \mathsf{ALPS}_\tau$ and $V \subseteq dom(\tau)$ , we define $\mathbb{G}_{\Vert V} = \textsf{prune}( N \mathbin{\star} E \mathbin{\star} \ell |_{V} \mathbin{\star} \mathit{sh} \mathbin{\star} \mathit{nl})\enspace .$

Let $W=V \cup \{v.{\mathord{\mathtt{f}}} \in Q_\tau \mid v \in V\}$ and let $\mathbb{G}_{\Vert V}= N' \mathbin{\star} E' \mathbin{\star} \ell' \mathbin{\star} \mathit{sh}' \mathbin{\star} \mathit{nl}'$ . Since $\ell'$ is obtained by restricting $\ell$ to the variables in V, we have that for any $x \in dom(\tau) \setminus V$ , $\ell'(x)=\ell| _{V} (x)=\bot$ . Then for each $n' \in N'$ , ${\ell'}^{-1} (n) \subseteq W$ and all the nodes in $\mathbb{G}$ which are not the image of a qualified identifier in W are removed from the graph. By construction and since $\mathbb{G} \in \mathsf{ALPS}_\tau$ , we have that N’ is backward closed and $\mathbb{G}_{\Vert V} \in \mathsf{ALPS}_{\tau|_{V}}$ .

Proposition 80. For each $\mathbb{G} \in \mathsf{ALPS}_\tau$ and $V\subseteq dom(\tau)$ , $\gamma(\mathbb{G})_{\Vert V} \subseteq \gamma(\mathbb{G}_{\Vert V})$ .

6.1.3 Nullness propagation

Consider the operation on concrete states which, given $S \subseteq \Sigma_\tau$ and an identifier $i \in I_\tau$ , returns the subset of those states in S where i is ${\mathord{\mathtt{null}}}$ , that is,

\begin{equation*}S_{|i={\mathord{\mathtt{null}}}} = \{ \sigma = \phi \mathbin{\star} \mu \in S \mid \phi(i) = {\mathord{\mathtt{null}}} \} \enspace .\end{equation*}

If $\mathbb{G}$ is a correct abstraction of S, it is also a correct abstraction of $S_{|i={\mathord{\mathtt{null}}}}$ , but we would like to refine $\mathbb{G}$ into a more precise abstract state which still correctly approximates $S_{|i={\mathord{\mathtt{null}}}}$ . This suggests the following definition.

Definition 81 (Abstract nullness propagation) Given $\mathbb{G} \in \mathsf{ALPS}_\tau$ and an identifier $i \in I_\tau$ , we define $\mathbb{G}_{|i={\mathord{\mathtt{null}}}} = \mathbb{G}|_{N_v}$ where $N_v = N \setminus \overrightarrow{\{\ell(i)\}}$ .

In $\mathbb{G}_{|i={\mathord{\mathtt{null}}}}$ , since the identifier i is forced to be ${\mathord{\mathtt{null}}}$ , all the nodes reachable from i need to be removed from the graph since they do not really exist. Given that $N \setminus \overrightarrow{\{\ell(i)\}}$ is backward closed, we know from Propositions 64 and 65 that $\mathbb{G}_{|i={\mathord{\mathtt{null}}}}$ is an $\mathsf{ALPS}$ graph and $\mathbb{G}_{|i={\mathord{\mathtt{null}}}} \preceq \mathbb{G}$ .

Nullness propagation is a special case of greatest lower bound between $\mathsf{ALPS}$ graphs, as proved in the following:

Proposition 82. For each $\mathbb{G} \in \mathsf{ALPS}_\tau$ and $i \in I_\tau$ , $\mathbb{G}_{|i={\mathord{\mathtt{null}}}} = \mathbb{G} \curlywedge \top_{|i={\mathord{\mathtt{null}}}}$ .

In turn, this allows us to prove that:

Proposition 83. For each $\mathbb{G} \in \mathsf{ALPS}_\tau$ and $i \in I_\tau$ , $\gamma(\mathbb{G})_{|i={\mathord{\mathtt{null}}}} \subseteq \gamma(\mathbb{G}_{|i={\mathord{\mathtt{null}}}})$ .

In the rest of the paper, given $i, j \in I_\tau$ , we use $\mathbb{G}_{|i={\mathord{\mathtt{null}}}, j={\mathord{\mathtt{null}}}}$ as a short form for $(\mathbb{G}_{|i={\mathord{\mathtt{null}}}})_{|j={\mathord{\mathtt{null}}}}$ .

6.1.4 Restriction to aliasing

Consider the operation on concrete states which, given $S \subseteq \Sigma_\tau$ and two variables v, w, returns the subset of those states in S where v is weakly aliased with w, that is,

\begin{equation*}S_{|v=w} = \{ \sigma = \phi \mathbin{\star} \mu \in S \mid \phi(v) = \phi(w) \} \enspace .\end{equation*}

Similarly to what we have done in the previous section, we aim to determine a correct approximation of $S_{|v=w}$ . Given $\mathbb{G} \in \mathsf{ALPS}_\tau$ , we define

\begin{equation*} \begin{array}{ll}\mathbb{G}_{|v=w} = &\mathbb{G} \curlywedge \top_{v=w}\end{array}\end{equation*}

where

\begin{equation*}\top_{v=w} = \begin{cases}\textsf{prune}(\top.N \mathbin{\star} \top.E \mathbin{\star} \top.\ell[w \mapsto v] \mathbin{\star} \top.\mathit{sh} \mathbin{\star} \top.\mathit{nl}) &\text{if $\tau(v)\leq \tau(w)$;}\\\textsf{prune}(\top.N \mathbin{\star} \top.E \mathbin{\star} \top.\ell[v \mapsto w] \mathbin{\star} \top.\mathit{sh} \mathbin{\star} \top.\mathit{nl})&\text{if $\tau(w) < \tau(v)$;}\\\top_{|v={\mathord{\mathtt{null}}}, w={\mathord{\mathtt{null}}}} & \text{otherwise.}\end{cases}\end{equation*}

Proposition 84. For each $\mathbb{G} \in \mathsf{ALPS}_\tau$ and $v, w \in dom(\tau)$ , $\gamma(\mathbb{G})_{|v=w} \subseteq \gamma(\mathbb{G}_{|v=w})$ .

In the rest of the paper, we will use $\mathbb{G}_{|v_1=w_1,\ldots,w_n=w_n}$ as a shorthand for $((\mathbb{G}_{|v_1=w_1})\ldots)_{|v_n=w_n}$ .

6.2 Abstract semantics for expressions

The abstract semantics specifies how each expression $\mathit{exp}$ transforms input abstract states $\mathbb{G} = N \mathbin{\star} E \mathbin{\star} \ell \mathbin{\star} \mathit{sh} \mathbin{\star} \mathit{nl}$ into final abstract states $\mathbb{G}' = N' \mathbin{\star} E' \mathbin{\star} \ell' \mathbin{\star} \mathit{sh}' \mathbin{\star}\mathit{nl}'$ where ${\mathord{\mathtt{res}}}$ holds the $\mathit{exp}$ ’s value. Abstract semantics for expressions (and later commands) is given compositionally on their syntax.

Definition 85. Let $\tau$ describe the variables in scope and I be an $\mathsf{ALPS}$ interpretation. Figure 20 defines the $\mathsf{ALPS}$ semantics for expression (except method calls) $\mathcal{S\!E}_{{\tau}}^{I}\unicode{x0301A}{\mathit{exp}}\unicode{x0301B}:\mathsf{ALPS}_\tau\to\mathsf{ALPS}_{\tau+\mathit{exp}}$ .

Figure 20.

The $\mathsf{ALPS}$ interpretation for expressions.

We briefly explain the behavior of the abstract semantic operators with respect to the corresponding concrete ones. The concrete semantics of ${\mathord{\mathtt{null}}}\ \kappa$ stores ${\mathord{\mathtt{null}}}$ in the variable ${\mathord{\mathtt{res}}}$ . Therefore, in the abstract semantics, we only need to add the new variable ${\mathord{\mathtt{res}}}$ into the type environment, without modifying the abstract state.

The concrete semantics of $\mathtt{new} \ \kappa$ stores in ${\mathord{\mathtt{res}}}$ a reference to a new object o, whose fields are ${\mathord{\mathtt{null}}}$ . The other variables do not change. Since o is only reachable from ${\mathord{\mathtt{res}}}$ , variable ${\mathord{\mathtt{res}}}$ shares with itself only and is clearly linear. Therefore, we only need to add a new node labeled with ${\mathord{\mathtt{res}}}$ and the corresponding sharing singleton, without affecting nonlinearity information.

The concrete semantics of v simply makes ${\mathord{\mathtt{res}}}$ an alias for v. Since the types of v and ${\mathord{\mathtt{res}}}$ coincide, we only need to add the variable v to the same node of ${\mathord{\mathtt{res}}}$ . The other variables are unchanged.

When $v={\mathord{\mathtt{null}}}$ , then $\mathtt{(}\kappa\mathtt{)}v$ behaves like ${\mathord{\mathtt{null}}}\ \kappa$ . If $\kappa$ and the type of v are not compatible, then $\mathtt{(}\kappa\mathtt{)}v$ only returns a nonfailed state when $v={\mathord{\mathtt{null}}}$ . Therefore, in the abstract semantics, $\mathtt{(}\kappa\mathtt{)}v$ restricts the input graph $\mathbb{G}$ to those states where $v={\mathord{\mathtt{null}}}$ . In the other cases, the cast $\mathtt{(}\kappa\mathtt{)}v$ stores in ${\mathord{\mathtt{res}}}$ the value of v. We use an auxiliary operator $add(\mathbb{G},n,\kappa)$ , explained in the following section, which adds the label ${\mathord{\mathtt{res}}}$ to the node n, and possibly adds new nodes for the fields of ${\mathord{\mathtt{res}}}$ which are not fields of $\psi_G(n)$ . In this case, we can exploit the notion of linearity. In fact, when v is linear, we know that fields of ${\mathord{\mathtt{res}}}$ cannot share with each other and are linear.

The concrete semantics of $v.{\mathord{\mathtt{f}}}$ stores in ${\mathord{\mathtt{res}}}$ the value of the field ${\mathord{\mathtt{f}}}$ of v, provided v is not ${\mathord{\mathtt{null}}}$ . When $v.{\mathord{\mathtt{f}}}$ is not ${\mathord{\mathtt{null}}}$ , this essentially amounts to the same procedure of the previous case.

6.2.1 Assignment to a node

The auxiliary operator $add(\mathbb{G}, n, \kappa)$ adds a new variable ${\mathord{\mathtt{res}}}$ of type $\kappa$ as an alias of the node $n \in N$ . The operator also adds children of ${\mathord{\mathtt{res}}}$ when needed to have an $\mathsf{ALPS}$ graph.

\begin{equation*}add(\mathbb{G},n,\kappa) = \begin{cases} \bot & \text{if$\{\tau_G(n), \kappa\}$ is not a chain}\\cl^{\uparrow}(\mathbb{G}')& \text{otherwise}\end{cases}\end{equation*}

Given $\mathbb{G} = G \mathbin{\star} \mathit{sh} \mathbin{\star} \mathit{nl}$ , in order to define $\mathbb{G}'$ , let $\kappa' = \psi_{G} (n)$ be the inferred type for node n in $\mathbb{G}$ (from variables only) and $F' = dom (\kappa)\setminus dom (\kappa') = \{{\mathord{\mathtt{f}}}_1,\ldots,{\mathord{\mathtt{f}}}_m \}$ be the set of the new fields in class $\kappa$ which are not currently considered in $\mathbb{G}$ ; let also $\{n_{{\mathord{\mathtt{f}}}_1},\ldots, n_{{\mathord{\mathtt{f}}}_m} \}$ be a set of fresh nodes, that is, such that $\{n_{{\mathord{\mathtt{f}}}_1},\ldots, n_{{\mathord{\mathtt{f}}}_m} \} \cap N=\emptyset$ ; then:

Definition of G’ is straightforward: we just add the new nodes as children of node n, each one pointed through the corresponding field in F’. If n is nonlinear, then all new nodes are nonlinear, all new nodes share among them and with other nodes n’ sharing with n. This procedure might add spurious sharing or nonlinearity information: we solve this problem by filtering with SH and NL.

When n is linear, then all new nodes are linear, do not share among them, and do not share with any node n’ reachable from n through a nonempty sequence of fields $ {\mathord{\mathtt{f}}}'_1, \ldots, {\mathord{\mathtt{f}}}'_k$ in E, but share with all the other nodes sharing with n.

6.3 Abstract semantics for commands

In the concrete semantics, each command $\mathit{com}$ transforms an initial state into a final state. On the abstract domain, it transforms an initial $\mathsf{ALPS}$ graph $G \mathbin{\star} \mathit{sh} \mathbin{\star} \mathit{nl}$ into an $\mathsf{ALPS}$ graph $G' \mathbin{\star} \mathit{sh}'\mathbin{\star} \mathit{nl}'$ .

Definition 86. Let $\tau$ describe the variables in scope and I be an $\mathsf{ALPS}$ interpretation. Figures 21–23 show the $\mathsf{ALPS}$ semantics for commands $\mathcal{SC}_{{\tau}}^{{I}}\unicode{x0301A}{{\mathit{com}}}\unicode{x0301B}: \mathsf{ALPS}_\tau \to \mathsf{ALPS}_\tau$ .

Figure 21.

The $\mathsf{ALPS}$ interpretation for simple commands.

Figure 22.

The $\mathsf{ALPS}$ interpretation for assignment to field.

Figure 23.

The $\mathsf{ALPS}$ interpretation for method calls. The auxiliary function $match_{v.{\mathord{\mathtt{m}}}}$ is defined later in Figure 24.

The concrete semantics of $v\mathbin{\mathtt{\unicode{x02254}}}\mathit{exp}$ evaluates $\mathit{exp}$ and stores its result into v. Thus, the final abstract state is obtained by first computing $\mathcal{S\!E}_{\tau}^{I}\unicode{x0301A}{\mathit{exp}}\unicode{x0301B}$ and then renaming ${\mathord{\mathtt{res}}}$ into v. Some of the nodes may become unlabeled and must be removed. This is accomplished by the auxiliary operation $\textsf{prune}$ which removes unnecessary information, in particular unlabeled nodes and fields which are not in the declared type of the variables.

To determine a correct approximation of the conditional “ $\mathtt{if\ }v\\mathtt{=null}$ ” we check whether $\ell(v)=\bot$ . If this is the case, then we know that v is null and we evaluate $com_1$ . Otherwise, we evaluate both branches and compute the lub. When evaluating the first branch, we may improve precision by using the auxiliary operator $\mathbb{G}_{|v={\mathord{\mathtt{null}}}}$ (Section 6.1.3) which returns a correct approximation of the program states $\{ \phi \mathbin{\star} \mu \mid \alpha(\phi\mathbin{\star} \mu) \preceq \mathbb{G} \wedge \phi(v)={\mathord{\mathtt{null}}} \}$ , that is, the states correctly approximated by $\mathbb{G}$ where v is ${\mathord{\mathtt{null}}}$ . Note that, since our domain does not model definite non-nullness, there is no way to define a projection $\mathbb{G}_{|v\neq{\mathord{\mathtt{null}}}}$ to improve the input for $\mathcal{SC}_{{\tau}}^{{I}}\unicode{x0301A}{{\mathit{com}_2}}\unicode{x0301B}$ as in $\mathcal{SC}_{{\tau}}^{{I}}\unicode{x0301A}{{\mathit{com}_1}}\unicode{x0301B}(\mathbb{G}_{|v={\mathord{\mathtt{null}}}})$ .

Similarly for the conditional “ $\mathtt{if\ }v\ \mathtt{=}\ w$ ” where we use another auxiliary operator $\mathbb{G}_{|v=w}$ (Section 6.1.4) which returns a correct approximation of the set of program states $\{ \phi \mathbin{\star} \mu \mid \alpha(\phi \mathbin{\star} \mu) \preceq \mathbb{G} \wedge\phi(v)=\phi(w) \}$ . Again, since our domain does not model definite not aliasing, we cannot improve the input for the second branch.

The composition of commands is denoted by functional composition over $\mathsf{ALPS}$ , where the identity map $\lambda s\in \mathsf{ALPS}_\tau.s$ is needed when $p=0$ . The evaluation of $v.{\mathord{\mathtt{f}}} \mathbin{\mathtt{\unicode{x02254}}} \mathit{exp}$ in Figure 22 is the most complex operation of the abstract semantics. Although it may seem similar to the assignment $v \mathbin{\mathtt{\unicode{x02254}}} \mathit{exp}$ , we must take into account that v might be aliased to many different nodes. The candidates are those variables, denoted by $V_{comp}$ , which share with $\ell(v)$ and have compatible types. For each node labeled by a variable in $V_{comp}$ , we add a new fresh node in $N_{new}$ pointed by an edge (labeled by the field ${\mathord{\mathtt{f}}}$ ) in $E_{new}$ . Finally, all possible sharing and nonlinearity are added. A slightly different treatment is devoted to the special case when the result of the expression is definitely ${\mathord{\mathtt{null}}}$ .

6.4 Abstract semantics of method call

The concrete semantics of the method call $v.\mathtt{m(}v_1,\ldots,v_n\mathtt{)}$ builds an input state containing the local variables $w_1,\ldots,w_n$ and the special variable $\mathit{this}$ , and executes the method body. In order to improve the precision of methods call, we also use a copy of the local variables $w'_1,\ldots,w'_n$ which will be used when returning from the method call to match the original variables $v_1,\ldots,v_n$ . This allows a change to the object pointed by $w_i$ to be distinguished from a change to $w_i$ itself.

When a method $v.{\mathord{\mathtt{m}}}$ is called, the class of v is inspected and the correct overloaded method for ${\mathord{\mathtt{m}}}$ is selected. The abstract domain contains only a partial information on the runtime class of v, since we only know that the class of v must be a superclass of the class of any variable aliased with v, namely a superclass of $\tau_G(\ell(v))$ . We exploit this information in computing the abstract semantics of a method call in Figure 23. In practice, we conservatively assume that every method ${\mathord{\mathtt{m}}}$ in any subclass of $\tau_G(\ell(v))$ may be called. Note that methods defined only in superclasses of $\kappa$ are already considered in $\kappa$ .

When exiting from a method call, we need to rename ${\mathord{\mathtt{out}}}$ into ${\mathord{\mathtt{res}}}$ since, from the point of view of the caller, the returned value of the callee ( ${\mathord{\mathtt{out}}}$ ) is the value of the method call expression ( ${\mathord{\mathtt{res}}}$ ). We use an auxiliary operation $match_{v.{\mathord{\mathtt{m}}}}$ which, given an initial and final state, updates the initial state trying to guess a possible matching of variables in the abstract states. The definition of $match_{v.{\mathord{\mathtt{m}}}}$ is given in Figure 24.

Figure 24.

The $match_{v.{\mathord{\mathtt{m}}}}$ auxiliary operation.

Analougously to the concrete case, we may define an abstract transformer which, given a $\mathsf{ALPS}$ interpretation I, returns a new $\mathsf{ALPS}$ interpretation I’ such that

\begin{equation*}\begin{array}{lll}I'(\kappa.{\mathord{\mathtt{m}}}) &=&(\lambda \mathbb{G} \in \mathsf{ALPS}_{\mathit{scope}(\kappa.{\mathord{\mathtt{m}}})}.\mathbb{G} _{\Vert dom(\mathit{output}(\kappa.{\mathord{\mathtt{m}}}))}) \ \circ \\&& \mathcal{SC}_{{\mathit{scope}(\kappa.{\mathord{\mathtt{m}}})}}^{{I}}\unicode{x0301A}{{\mathit{body}(\kappa.{\mathord{\mathtt{m}}})}}\unicode{x0301B} \ \circ \\ && (\lambda \mathbb{G} \in \mathsf{ALPS}_{\mathit{input}(\kappa.{\mathord{\mathtt{m}}})}. N \mathbin{\star} E \mathbin{\star} \ell' \mathbin{\star} \mathit{sh} \mathbin{\star} \mathit{nl})\end{array}\end{equation*}

where $\ell' = \ell[w'_1 \mapsto \ell(w_1),\ldots, w'_n \mapsto \ell(w_n)]$ .

The new interpretation returned by the abstract transformer is computed by first adding primed variables which are used to hold a copy of the original actual parameters, then evaluating the body of the method and finally restricting the graph to the output variables. The abstract denotational semantics is the least fixpoint of this transformer.

Example 89. Consider the method $\mathtt{Tree}.{\mathord{\mathtt{makeTree}}}$ in Section 1.4, where

\begin{align*}\begin{split}\mathit{scope}(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}})=[ & {\mathord{\mathtt{this}}} \mapsto \mathtt{Tree}, n\mapsto \mathtt{Integer}, n' \mapsto \mathtt{Integer},m \mapsto \mathtt{Integer},{\mathord{\mathtt{out}}} \mapsto \mathtt{Tree}].\end{split}\end{align*}

We can compute a new $\mathsf{ALPS}$ interpretation from the least informative $\mathsf{ALPS}$ interpretation $I_\bot(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}}) = \lambda\mathbb{G}.\bot_{{\mathord{\mathtt{out}}}(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}})}$ :

\begin{equation*}\begin{split} I^1(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}})(\mathbb{G})= & N \cup \{n_{out}\} \mathbin{\star} E \mathbin{\star} {} \ell[n' \mapsto \ell(n), out \mapsto n_{out}] \mathbin{\star} \mathit{sh} \cup \{\{n_{out}\}\} \mathbin{\star} \mathit{nl} \end{split}\end{equation*}

Now, starting from $I^1(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}})$ , we can compute a new interpretation as follows:

\begin{equation*}\begin{split} &I^2(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}})(\mathbb{G})= N \cup \{n_{out}, n_{out.l},n_{out.r},\} \mathbin{\star} {}\\ & \qquad E \cup \{n_{out} \xrightarrow{{\mathord{\mathtt{l}}}} n_{out.l}, n_{out} \xrightarrow{{\mathord{\mathtt{r}}}} n_{out.r} \}\mathbin{\star} {} \\ & \qquad \ell[n' \mapsto \ell(n), out \mapsto n_{out} ] \mathbin{\star} {}\\ & \qquad \mathit{sh} \cup \{\,\{n_{out}\}, \{n_{out.l}\},\{n_{out.r}\}, \{n_{out},n_{out.l} \}, \{n_{out},n_{out.r} \}\, \}\mathbin{\star} \mathit{nl} \end{split}\end{equation*}

which is the least fixpoint. Relatively to the case $\ell(n)\neq {\mathord{\mathtt{null}}}$ , the abstract states $I^1(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}})(\mathbb{G})$ and $I^2(\mathtt{Tree}.{\mathord{\mathtt{makeTree}}})(\mathbb{G})$ are depicted in Figure 25. From this graph, it appears that the tree generated by this method is linear and does not share with ${\mathord{\mathtt{this}}}$ (the object on which we call the makeTree method). Moreover since n and n’ are aliased we know that the method does not modify the variable n.

Figure 25.

$\mathsf{ALPS}$ interpretations for the makeTree method.