IA159 Formal Verification Methods
Static analysis and abstract interpretation
Jan Obdržálek
Jan Strejˇcek
Department of Computer Science
Faculty of Informatics
Masaryk University
Focus and sources
Focus
lattices and fixpoints
static analysis
abstract interpretation
Source
P. Cousot and R. Cousot: Abstract Interpretation: A Unified
Lattice Model for Static Analysis of Programs by
Construction or Approximation of Fixpoints, POPL 1977.
Special thanks to Marek Trtík for providing me his slides.
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Motivation for static analysis
Floyd’s conjecture
To prove static properties of program it is often sufficient to
consider sets of states associated with each program point.
Examples
to check safety properties (reachability of an error state),
one only needs to know reachable states
for many optimizations during compilation, static
information is sufficient (e.g. detection of live variables,
available expressions, etc.)
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Motivation for static analysis
Operational semantics
defines how a state changes along program execution
it is concerned about computational sequences
computes a function relating input and output states
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Motivation for static analysis
Operational semantics
defines how a state changes along program execution
it is concerned about computational sequences
computes a function relating input and output states
Static semantic
observes which states pass which program location
it is concerned about observed sets of states at locations
computes a function assigning set of states to each
program location
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Motivation for abstract interpretation
It is usually impossible to compute the sets of reachable
states precisely
we can compute them on some level of abstraction
for example, instead with precise numbers we work only
with abstract values {+, 0, −}
abstraction brings some level of imprecission, for example,
15 − 17 is seen as (+) − (+), which can be +, 0, −
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Preliminaries
Lattices and fixpoints
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Introduction to lattices
Let (L, ≤) be a partially ordered set and M ⊆ L.
x ∈ L is an upper bound of M iff y ≤ x holds for all y ∈ M
x ∈ L is a lower bound of M iff x ≤ y holds for all y ∈ M
supremum of M is the least upper bound of M
infimum of M is the greatest lower bound of M
sup(M) and inf(M) denote supremum and infimum of M,
respectively
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Introduction to lattices
Let (L, ≤) be a partially ordered set and M ⊆ L.
x ∈ L is an upper bound of M iff y ≤ x holds for all y ∈ M
x ∈ L is a lower bound of M iff x ≤ y holds for all y ∈ M
supremum of M is the least upper bound of M
infimum of M is the greatest lower bound of M
sup(M) and inf(M) denote supremum and infimum of M,
respectively
Definition (Complete lattice)
An ordered set (L, ≤) is called complete lattice, if for each
M ⊆ L there exist both sup(M) and inf(M).
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Introduction to lattices
Which of the partially ordered sets are complete lattices?
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Introduction to lattices
Which of the partially ordered sets are complete lattices?
(All of the top row and the left of the bottom row.)
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Introduction to lattices
For every set S, the powerset P(S) with the partial order ⊆ is
a complete lattice.
For example, (P({0, 1, 2, 3}), ⊆) looks like:
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Introduction to lattices
Let (L, ≤) be a complete lattice.
the greatest element = sup(L) is called one of L
the least element ⊥ = inf(L) of L is called zero of L
the lattice is of finite height if there exists h ∈ N such that
the length of each strictly increasing chain of elements of L
is less than or equal to h
minimal such h is called lattice height
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Fixpoint and Knaster-Tarski fixpoint theorem
Let (L, ≤) be a complete lattice.
a function f : L → L is monotone if for all x, y ∈ L it holds
x ≤ y =⇒ f(x) ≤ f(y)
x ∈ L is called a fixpoint of f if f(x) = x
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Fixpoint and Knaster-Tarski fixpoint theorem
Let (L, ≤) be a complete lattice.
a function f : L → L is monotone if for all x, y ∈ L it holds
x ≤ y =⇒ f(x) ≤ f(y)
x ∈ L is called a fixpoint of f if f(x) = x
Theorem (Knaster-Tarski)
Let (L, ≤) be a complete lattice and f : L → L be a monotone
function. Then the set of fixpoints of f with partial order ≤ is
also a complete lattice.
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Kleene fixpoint theorem
Theorem (Kleene)
Let (L, ≤) be a complete lattice of finite height and f : L → L a
monotone function. Then there exists n ∈ N such that for all
k ∈ N it is fn(⊥) = fn+k (⊥) and fn(⊥) is the least fixpoint of f.
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Kleene fixpoint theorem
Theorem (Kleene)
Let (L, ≤) be a complete lattice of finite height and f : L → L a
monotone function. Then there exists n ∈ N such that for all
k ∈ N it is fn(⊥) = fn+k (⊥) and fn(⊥) is the least fixpoint of f.
Proof: Since ⊥ is the least element of L, we have ⊥ ≤ f(⊥).
Since f is monotone, them f(⊥) ≤ f(f(⊥)) and by induction
fi(⊥) ≤ fi+1(⊥). Thus, we have a nondecreasing chain
⊥ ≤ f(⊥) ≤ f2(⊥) ≤ . . .. Since L is assumed to be of a finite
height, there must exist n ∈ N such that fn(⊥) = fn+1(⊥). To
show that fn(⊥) is a least fixpoint of f, let us assume x is
another fixpoint of f. Since ⊥ ≤ x and f(⊥) ≤ f(x) = x from
monotonicity of f, we get by induction fn(⊥) ≤ x.
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Fixpoint computation
Algorithm for the least fixpoint computation
x := ⊥;
do { t := x; x := f(x); } while (x = t);
If we start with x := ;, we get the greatest fixpoint.
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Product lattice
Lemma (Product lattice)
Let (L1, ≤1), . . . , (Ln, ≤n) be complete lattices and order ≤ on
L1 × . . . × Ln is defined as (x1, . . . , xn) ≤ (y1, . . . , yn) iff
x1 ≤1 y1 ∧ . . . ∧ xn ≤n yn.
Then (L1 × . . . × Ln, ≤) is a complete lattice.
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Fixpoints on product lattices
Let (L, ≤) be a complete lattice and (Ln, ) be the
corresponding product lattice. Further, let F1, . . . , Fn : Ln → L
be monotone functions, i.e. (x1, . . . , xn) (y1, . . . , yn) implies
Fi(x1, . . . , xn) ≤ Fi(y1, . . . , yn) for each 1 ≤ i ≤ n. Then the
function F : Ln → Ln defined as
F(x1, . . . , xn) = (F1(x1, . . . , xn), . . . , Fn(x1, . . . , xn))
is a monotone function in (Ln, ). Further, the least fixpoint of F
is the least solution of the system
x1 = F1(x1, . . . , xn)
...
xn = Fn(x1, . . . , xn)
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Fixpoint comutation of product lattices
Naive algorithm for fixpoint computation
x := ⊥;
do { t := x; x := F(x); } while (x = t);
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Fixpoint comutation of product lattices
Naive algorithm for fixpoint computation
x := ⊥;
do { t := x; x := F(x); } while (x = t);
Better algorithm for fixpoint computation (faster convergence)
x1 = ⊥; ... xn = ⊥;
do {
t1 = x1; ...tn = xn;
x1 = F1(x1, . . . , xn);
...
xn = Fn(x1, . . . , xn);
} while (x1 = t1 ∨ . . . ∨ xn = tn);
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Moving to abstraction
Abstract interpretation
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Abstract interpretation
an abstract interpretation of a program is kind of a static
semantic, where original data domains are replaced with
abstract ones
abstract data domain must constitute a complete lattice
semantic of program instructions have to be changed as
well: we define unique monotone function for each
program instruction
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Abstract interpretation: Definition
Definition (Abstract interpretation)
An abstract interpretation I of a program P with n program
locations is a tuple
I = L, ◦, ≤, , ⊥, F
where (L, ≤) is complete lattice, and ⊥ are one and zero of
(L, ≤), ◦ is equal either to join or meet operation, and F is a
monotone function on product lattice (Ln, ≤) defining the
interpretation of basic instructions.
The join operator is defined as a ◦ b = inf({a, b}), while
the meet operator is defined as a ◦ b = sup({a, b}).
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Abstract interpretation: Definition
Definition (Abstract interpretation)
An abstract interpretation I of a program P with n program
locations is a tuple
I = L, ◦, ≤, , ⊥, F
where (L, ≤) is complete lattice, and ⊥ are one and zero of
(L, ≤), ◦ is equal either to join or meet operation, and F is a
monotone function on product lattice (Ln, ≤) defining the
interpretation of basic instructions.
The join operator is defined as a ◦ b = inf({a, b}), while
the meet operator is defined as a ◦ b = sup({a, b}).
Typically, F(x) = (F1(x), . . . , Fn(x)), where each Fi : Ln → L
defines effect of i-th program instruction.
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Example: Available expressions
A nontrivial expression in a program is available at a program
location if its current value has already been computed earlier
in the execution.
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Example: Available expressions
A nontrivial expression in a program is available at a program
location if its current value has already been computed earlier
in the execution.
var x,y,z,a,b;
z := a+b;
y := a*b;
while (y > a+b) {
a := a+1;
x := a+b;
}
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Example: Available expressions
A nontrivial expression in a program is available at a program
location if its current value has already been computed earlier
in the execution.
Available expressions: AExprs = {a+b, a*b, y>a+b, a+1}
var x,y,z,a,b;
z := a+b;
y := a*b;
while (y > a+b) {
a := a+1;
x := a+b;
}
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Example: Available expressions
A nontrivial expression in a program is available at a program
location if its current value has already been computed earlier
in the execution.
Available expressions: AExprs = {a+b, a*b, y>a+b, a+1}
A.I.: I = P(AExprs), ∩, ⊆, AExprs, ∅, λx.(F1(x), . . . , F6(x))
var x,y,z,a,b;
z := a+b;
y := a*b;
while (y > a+b) {
a := a+1;
x := a+b;
}
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Example: Available expressions
A nontrivial expression in a program is available at a program
location if its current value has already been computed earlier
in the execution.
Available expressions: AExprs = {a+b, a*b, y>a+b, a+1}
A.I.: I = P(AExprs), ∩, ⊆, AExprs, ∅, λx.(F1(x), . . . , F6(x))
Product lattice: (P6(AExprs), ≤).
var x,y,z,a,b; x1
z := a+b; x2
y := a*b; x3
while (y > a+b) { x4
a := a+1; x5
x := a+b; x6
}
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Example: Available expressions
A nontrivial expression in a program is available at a program
location if its current value has already been computed earlier
in the execution.
Available expressions: AExprs = {a+b, a*b, y>a+b, a+1}
A.I.: I = P(AExprs), ∩, ⊆, AExprs, ∅, λx.(F1(x), . . . , F6(x))
Product lattice: (P6(AExprs), ≤).
var x,y,z,a,b; x1 = F1(x) = ∅
z := a+b; x2 = F2(x) = (x1 ∪ {a+b}) ∅
y := a*b; x3 = F3(x) = (x2 ∪ {a*b}) {y>a+b}
while (y > a+b) { x4 =F4(x) = (x3 ∩ x6) ∪ {a+b, y>a+b}
a := a+1; x5 = F5(x) = (x4 ∪ {a+1}) AExprs
x := a+b; x6 = F6(x) = (x5 ∪ {a+b}) ∅
}
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Example: Available expressions
A nontrivial expression in a program is available at a program
location if its current value has already been computed earlier
in the execution.
Available expressions: AExprs = {a+b, a*b, y>a+b, a+1}
A.I.: I = P(AExprs), ∩, ⊆, AExprs, ∅, λx.(F1(x), . . . , F6(x))
Product lattice: (P6(AExprs), ≤).
var x,y,z,a,b; x1 = F1(x) = ∅
z := a+b; x2 = F2(x) = (x1 ∪ {a+b}) ∅
y := a*b; x3 = F3(x) = (x2 ∪ {a*b}) {y>a+b}
while (y > a+b) { x4 =F4(x) = (x3 ∩ x6) ∪ {a+b, y>a+b}
a := a+1; x5 = F5(x) = (x4 ∪ {a+1}) AExprs
x := a+b; x6 = F6(x) = (x5 ∪ {a+b}) ∅
}
Direction: Forward
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Example: Available expressions
A nontrivial expression in a program is available at a program
location if its current value has already been computed earlier
in the execution.
Available expressions: AExprs = {a+b, a*b, y>a+b, a+1}
A.I.: I = P(AExprs), ∩, ⊆, AExprs, ∅, λx.(F1(x), . . . , F6(x))
Product lattice: (P6(AExprs), ≤).
var x,y,z,a,b; x1 = F1(x) = ∅
z := a+b; x2 = F2(x) = (x1 ∪ {a+b}) ∅
y := a*b; x3 = F3(x) = (x2 ∪ {a*b}) {y>a+b}
while (y > a+b) { x4 =F4(x) = (x3 ∩ x6) ∪ {a+b, y>a+b}
a := a+1; x5 = F5(x) = (x4 ∪ {a+1}) AExprs
x := a+b; x6 = F6(x) = (x5 ∪ {a+b}) ∅
}
Analysis: Must
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Example: Available expressions
A nontrivial expression in a program is available at a program
location if its current value has already been computed earlier
in the execution.
Available expressions: AExprs = {a+b, a*b, y>a+b, a+1}
A.I.: I = P(AExprs), ∩, ⊆, AExprs, ∅, λx.(F1(x), . . . , F6(x))
Product lattice: (P6(AExprs), ≤).
var x,y,z,a,b; x1 = F1(x) = ∅
z := a+b; x2 = F2(x) = (x1 ∪ {a+b}) ∅
y := a*b; x3 = F3(x) = (x2 ∪ {a*b}) {y>a+b}
while (y > a+b) { x4 =F4(x) = (x3 ∩ x6) ∪ {a+b, y>a+b}
a := a+1; x5 = F5(x) = (x4 ∪ {a+1}) AExprs
x := a+b; x6 = F6(x) = (x5 ∪ {a+b}) ∅
}
Are all functions Fi monotone?
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Example: Available expressions
A nontrivial expression in a program is available at a program
location if its current value has already been computed earlier
in the execution.
Available expressions: AExprs = {a+b, a*b, y>a+b, a+1}
A.I.: I = P(AExprs), ∩, ⊆, AExprs, ∅, λx.(F1(x), . . . , F6(x))
Product lattice: (P6(AExprs), ≤).
var x,y,z,a,b; x1 = F1(x) = ∅
z := a+b; x2 = F2(x) = (x1 ∪ {a+b}) ∅
y := a*b; x3 = F3(x) = (x2 ∪ {a*b}) {y>a+b}
while (y > a+b) { x4 =F4(x) = (x3 ∩ x6) ∪ {a+b, y>a+b}
a := a+1; x5 = F5(x) = (x4 ∪ {a+1}) AExprs
x := a+b; x6 = F6(x) = (x5 ∪ {a+b}) ∅
}
Proof F4: Let x, y ∈ P6(AExprs) such that x ≤ y.. . .
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Example: Available expressions
A nontrivial expression in a program is available at a program
location if its current value has already been computed earlier
in the execution.
Available expressions: AExprs = {a+b, a*b, y>a+b, a+1}
A.I.: I = P(AExprs), ∩, ⊆, AExprs, ∅, λx.(F1(x), . . . , F6(x))
Product lattice: (P6(AExprs), ≤).
var x,y,z,a,b; x1 = F1(x) = ∅
z := a+b; x2 = F2(x) = (x1 ∪ {a+b}) ∅
y := a*b; x3 = F3(x) = (x2 ∪ {a*b}) {y>a+b}
while (y > a+b) { x4 =F4(x) = (x3 ∩ x6) ∪ {a+b, y>a+b}
a := a+1; x5 = F5(x) = (x4 ∪ {a+1}) AExprs
x := a+b; x6 = F6(x) = (x5 ∪ {a+b}) ∅
}
Then x3 ⊆ y3 and x6 ⊆ y6, which implies (x3 ∩ x6) ⊆ (y3 ∩ y6). . .
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Example: Available expressions
After fixpoint computation ...
var x,y,z,a,b; x1 = ∅
z := a+b; x2 = {a+b}
y := a*b; x3 = {a+b, a*b}
while (y > a+b) { x4 = {a+b, y>a+b}
a := a+1; x5 = ∅
x := a+b; x6 = {a+b}
}
Solution: Minimal
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Example: Available expressions
After fixpoint computation ...
var x,y,z,a,b; x1 = ∅
z := a+b; x2 = {a+b}
y := a*b; x3 = {a+b, a*b}
while (y > a+b) { x4 = {a+b, y>a+b}
a := a+1; x5 = ∅
x := a+b; x6 = {a+b}
}
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Example: Live variables
A variable is live at a program point if its current value may be
read during the remaining execution of the program.
var x,y,z;
x := input;
while (x>1) {
y := x/2;
if (y>3)
x := x-y;
z := x-4;
if (z>0)
x := x/2;
z := z-1; }
output x;
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Example: Live variables
A variable is live at a program point if its current value may be
read during the remaining execution of the program.
Vars = {x,y,z} and
I = P(Vars), ∪, ⊆, Vars, ∅, λx.(F1(x), . . . , F11(x))
var x,y,z;
x := input;
while (x>1) {
y := x/2;
if (y>3)
x := x-y;
z := x-4;
if (z>0)
x := x/2;
z := z-1; }
output x;
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Example: Live variables
A variable is live at a program point if its current value may be
read during the remaining execution of the program.
Product lattice is (P11(Vars), ≤).
var x,y,z; x1 = x2 {x,y,z}
x := input; x2 = x3 {x}
while (x>1) { x3 = (x4 ∪ x11) ∪ {x}
y := x/2; x4 = (x5 {y}) ∪ {x}
if (y>3) x5 = (x6 ∪ x7) ∪ {y}
x := x-y; x6 = (x7 {x}) ∪ {x,y}
z := x-4; x7 = (x8 {z}) ∪ {x}
if (z>0) x8 = (x9 ∪ x10) ∪ {z}
x := x/2; x9 = (x10 {x}) ∪ {x}
z := z-1; } x10 = (x3 {z}) ∪ {z}
output x; x11 = {x}
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Example: Live variables
A variable is live at a program point if its current value may be
read during the remaining execution of the program.
Direction: Backward
var x,y,z; x1 = x2 {x,y,z}
x := input; x2 = x3 {x}
while (x>1) { x3 = (x4 ∪ x11) ∪ {x}
y := x/2; x4 = (x5 {y}) ∪ {x}
if (y>3) x5 = (x6 ∪ x7) ∪ {y}
x := x-y; x6 = (x7 {x}) ∪ {x,y}
z := x-4; x7 = (x8 {z}) ∪ {x}
if (z>0) x8 = (x9 ∪ x10) ∪ {z}
x := x/2; x9 = (x10 {x}) ∪ {x}
z := z-1; } x10 = (x3 {z}) ∪ {z}
output x; x11 = {x}
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Example: Live variables
A variable is live at a program point if its current value may be
read during the remaining execution of the program.
Analysis: May
var x,y,z; x1 = x2 {x,y,z}
x := input; x2 = x3 {x}
while (x>1) { x3 = (x4 ∪ x11) ∪ {x}
y := x/2; x4 = (x5 {y}) ∪ {x}
if (y>3) x5 = (x6 ∪ x7) ∪ {y}
x := x-y; x6 = (x7 {x}) ∪ {x,y}
z := x-4; x7 = (x8 {z}) ∪ {x}
if (z>0) x8 = (x9 ∪ x10) ∪ {z}
x := x/2; x9 = (x10 {x}) ∪ {x}
z := z-1; } x10 = (x3 {z}) ∪ {z}
output x; x11 = {x}
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Example: Live variables
A variable is live at a program point if its current value may be
read during the remaining execution of the program.
Solution: Minimal
var x,y,z; x1 = x2 {x,y,z} x1 = ∅
x := input; x2 = x3 {x} x2 = ∅
while (x>1) { x3 = (x4 ∪ x11) ∪ {x} x3 = {x}
y := x/2; x4 = (x5 {y}) ∪ {x} x4 = {x}
if (y>3) x5 = (x6 ∪ x7) ∪ {y} x5 = {x,y}
x := x-y; x6 = (x7 {x}) ∪ {x,y} x6 = {x,y}
z := x-4; x7 = (x8 {z}) ∪ {x} x7 = {x}
if (z>0) x8 = (x9 ∪ x10) ∪ {z} x8 = {x,z}
x := x/2; x9 = (x10 {x}) ∪ {x} x9 = {x,z}
z := z-1; } x10 = (x3 {z}) ∪ {z} x10 = {x,z}
output x; x11 = {x} x11 = {x}
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Example: Live variables
A variable is live at a program point if its current value may be
read during the remaining execution of the program.
Variables y,z are never live together.
var x,y,z; x1 = x2 {x,y,z} x1 = ∅
x := input; x2 = x3 {x} x2 = ∅
while (x>1) { x3 = (x4 ∪ x11) ∪ {x} x3 = {x}
y := x/2; x4 = (x5 {y}) ∪ {x} x4 = {x}
if (y>3) x5 = (x6 ∪ x7) ∪ {y} x5 = {x,y}
x := x-y; x6 = (x7 {x}) ∪ {x,y} x6 = {x,y}
z := x-4; x7 = (x8 {z}) ∪ {x} x7 = {x}
if (z>0) x8 = (x9 ∪ x10) ∪ {z} x8 = {x,z}
x := x/2; x9 = (x10 {x}) ∪ {x} x9 = {x,z}
z := z-1; } x10 = (x3 {z}) ∪ {z} x10 = {x,z}
output x; x11 = {x} x11 = {x}
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Example: Reaching definitions
The reaching definitions for a given program point are those
assignments that may have defined the current values of
variables.
var x,y,z;
x := input;
while (x>1) {
y := x/2;
if (y>3)
x := x-y;
z := x-4;
if (z>0)
x := x/2;
z := z-1; }
output x;
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Example: Reaching definitions
The reaching definitions for a given program point are those
assignments that may have defined the current values of
variables.
var x,y,z;
x := input;
while (x>1) {
y := x/2;
if (y>3)
x := x-y;
z := x-4;
if (z>0)
x := x/2;
z := z-1; }
output x;
Assignments:
Asgns = {x=input, y=x/2, x=x-y,
z=x-4, x=x/2, z=z-1}
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Example: Reaching definitions
The reaching definitions for a given program point are those
assignments that may have defined the current values of
variables.
var x,y,z;
x := input;
while (x>1) {
y := x/2;
if (y>3)
x := x-y;
z := x-4;
if (z>0)
x := x/2;
z := z-1; }
output x;
Assignments:
Asgns = {x=input, y=x/2, x=x-y,
z=x-4, x=x/2, z=z-1}
I = P(Asgns), ∪, ⊆, Asgns, ∅,
λx.(F1(x), . . . , F11(x))
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Example: Reaching definitions
The reaching definitions for a given program point are those
assignments that may have defined the current values of
variables.
var x,y,z;
x := input;
while (x>1) {
y := x/2;
if (y>3)
x := x-y;
z := x-4;
if (z>0)
x := x/2;
z := z-1; }
output x;
Assignments:
Asgns = {x=input, y=x/2, x=x-y,
z=x-4, x=x/2, z=z-1}
I = P(Asgns), ∪, ⊆, Asgns, ∅,
λx.(F1(x), . . . , F11(x))
Product lattice: (P11(Asgns), ⊆)
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Example: Reaching definitions
The reaching definitions for a given program point are those
assignments that may have defined the current values of
variables.
var x,y,z;
x := input;
while (x>1) {
y := x/2;
if (y>3)
x := x-y;
z := x-4;
if (z>0)
x := x/2;
z := z-1; }
output x;
Assignments:
Asgns = {x=input, y=x/2, x=x-y,
z=x-4, x=x/2, z=z-1}
I = P(Asgns), ∪, ⊆, Asgns, ∅,
λx.(F1(x), . . . , F11(x))
Product lattice: (P11(Asgns), ⊆)
Direction: Forward
Analysis: May
Solution: Minimal
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Example: Busy expressions
An expression is busy if it will definitely be evaluated again
before its value changes.
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Example: Busy expressions
An expression is busy if it will definitely be evaluated again
before its value changes.
Direction: Backward
Analysis: Must
Solution: Minimal
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Computing variable values: different abstraction levels
We may consider different abstraction levels of variable values:
sets of integer values: P(Z)
intervals: {[l, u] | l, u ∈ Z ∪ {−∞, ∞}, l ≤ u} ∪ {⊥}
only signs with zero: P({−, 0, +})
initialized or not: {⊥, }
IA159 Formal Verification Methods: Static analysis and abstract interpretation 54/73
Computing variable values: different abstraction levels
We may consider different abstraction levels of variable values:
sets of integer values: P(Z)
intervals: {[l, u] | l, u ∈ Z ∪ {−∞, ∞}, l ≤ u} ∪ {⊥}
only signs with zero: P({−, 0, +})
initialized or not: {⊥, }
Which abstraction is more precise then other?
IA159 Formal Verification Methods: Static analysis and abstract interpretation 55/73
Fixpoint approximation techniques
Widening and narrowing
IA159 Formal Verification Methods: Static analysis and abstract interpretation 56/73
Fixpoint approximation techniques
When the extreme fixpoints of the system of equations cannot
be computed in finitely many steps, they can be approximated.
Generally, we have these two approaches:
1 we can find more abstract interpretation
2 we can make approximations in current interpretation to
accelerate convergence of Kleene’s sequence
Here we are concerned about second approach – the
technique called widening.
IA159 Formal Verification Methods: Static analysis and abstract interpretation 57/73
Fixpoint approximation techniques
Widening makes Kleene’s sequence to converge
to a fixpoint greater then the least one or
to an element s, such that s > F(s).
In the second case, since s is greater then the least fixpoint, we
can use narrowing to make the solution more precise – i.e. to
find some fixpoint smaller than s but possibly greater then the
least fixpoint.
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Widening
If the Kleene’s sequence does not converge, then exists a
location xi on a program loop where the sequence does
not converge.
We need a widening function : L × L → L, which is
applied every time the location xi is updated: xi = xi Fi(x).
We must define such that
for each x, y ∈ L x ◦ y ≤ x y, i.e. overapproximates
operation ◦,
it ensures, that every infinite sequence of elements
occurring in xi is not strictly increasing.
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Widening
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
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Widening
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
{functions}
x1 = [1, 1]
x2 = (x1 ∪ x3) ∩ [−∞, 100]
x3 = x2 + [1, 1]
x4 = (x1 ∪ x3) ∩ [101, +∞]
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Widening
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
{functions}
x1 = [1, 1]
x2 = (x1 ∪ x3) ∩ [−∞, 100]
x3 = x2 + [1, 1]
x4 = (x1 ∪ x3) ∩ [101, +∞]
Widening operator :
[i, j] [k, l] = [ite(k < i, −∞, i), ite(l > j, +∞, j)]
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Widening
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
{functions}
x1 = [1, 1]
x2 = (x1 ∪ x3) ∩ [−∞, 100]
x3 = x2 + [1, 1]
x4 = (x1 ∪ x3) ∩ [101, +∞]
Widening operator :
[i, j] [k, l] = [ite(k < i, −∞, i), ite(l > j, +∞, j)]
{no widening}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, 101]
x4 = [101, 101]
100 iterations
IA159 Formal Verification Methods: Static analysis and abstract interpretation 63/73
Widening
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
{functions}
x1 = [1, 1]
x2 = (x1 ∪ x3) ∩ [−∞, 100]
x3 = x2 + [1, 1]
x4 = (x1 ∪ x3) ∩ [101, +∞]
Widening operator :
[i, j] [k, l] = [ite(k < i, −∞, i), ite(l > j, +∞, j)]
{no widening}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, 101]
x4 = [101, 101]
100 iterations
{x3 = x3 (x2 + [1, 1])}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, + ∞]
x4 = [101, + ∞]
1 iterations
IA159 Formal Verification Methods: Static analysis and abstract interpretation 64/73
Widening
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
{functions}
x1 = [1, 1]
x2 = (x1 ∪ x3) ∩ [−∞, 100]
x3 = x2 + [1, 1]
x4 = (x1 ∪ x3) ∩ [101, +∞]
Widening operator :
[i, j] [k, l] = [ite(k < i, −∞, i), ite(l > j, +∞, j)]
{no widening}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, 101]
x4 = [101, 101]
100 iterations
{x3 = x3 (x2 + [1, 1])}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, + ∞]
x4 = [101, + ∞]
1 iterations
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Narrowing
When widening ends with s > F(s), we improve solution s
as follows: s ≥ F(s) ≥ . . . ≥ Fn(s) ≥ . . . ≥ s0, where s0 is
the least fixpoint.
When the sequence is finite, its limit is better
approximation of s0.
If the sequence is infinite, we apply narrowing function
: L × L → L at not stabilizing location xi such that
xi = xi Fi(x).
Operator must satisfy:
for each x, y ∈ L x > y → (x ≥ x y ≥ y), i.e. tries to
slow down the decreasing of the sequence,
it ensures, that every infinite sequence elements starting
from any s is not strictly decreasing.
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Narrowing
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
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Narrowing
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
{functions}
x1 = [1, 1]
x2 = (x1 ∪ x3) ∩ [−∞, 100]
x3 = x2 + [1, 1]
x4 = (x1 ∪ x3) ∩ [101, +∞]
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Narrowing
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
{functions}
x1 = [1, 1]
x2 = (x1 ∪ x3) ∩ [−∞, 100]
x3 = x2 + [1, 1]
x4 = (x1 ∪ x3) ∩ [101, +∞]
Narrowing operator : [i, j] [k, l] = [ite(i = −∞, k, min(i, k)),
ite(j = +∞, l, max(j, l))].
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Narrowing
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
{functions}
x1 = [1, 1]
x2 = (x1 ∪ x3) ∩ [−∞, 100]
x3 = x2 + [1, 1]
x4 = (x1 ∪ x3) ∩ [101, +∞]
Narrowing operator : [i, j] [k, l] = [ite(i = −∞, k, min(i, k)),
ite(j = +∞, l, max(j, l))].
{no widening}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, 101]
x4 = [101, 101]
100 iterations
{widening}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, +∞]
x4 = [101, +∞]
1 iterations
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Narrowing
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
{functions}
x1 = [1, 1]
x2 = (x1 ∪ x3) ∩ [−∞, 100]
x3 = x2 + [1, 1]
x4 = (x1 ∪ x3) ∩ [101, +∞]
Narrowing operator : [i, j] [k, l] = [ite(i = −∞, k, min(i, k)),
ite(j = +∞, l, max(j, l))].
{no widening}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, 101]
x4 = [101, 101]
100 iterations
{widening}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, +∞]
x4 = [101, +∞]
1 iterations
{x3 = x3 (x2 + [1, 1])}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, 101]
x4 = [101, 101]
1 iterations
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Narrowing
Example: Interval bounds of integer variable x
{locations are after}
1 x := 1;
2 while (x <= 100) {
3 x := x + 1;
4 }
{functions}
x1 = [1, 1]
x2 = (x1 ∪ x3) ∩ [−∞, 100]
x3 = x2 + [1, 1]
x4 = (x1 ∪ x3) ∩ [101, +∞]
Narrowing operator : [i, j] [k, l] = [ite(i = −∞, k, min(i, k)),
ite(j = +∞, l, max(j, l))].
{no widening}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, 101]
x4 = [101, 101]
100 iterations
{widening}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, +∞]
x4 = [101, +∞]
1 iterations
{x3 = x3 (x2 + [1, 1])}
x1 = [1, 1]
x2 = [1, 100]
x3 = [2, 101]
x4 = [101, 101]
1 iterations
IA159 Formal Verification Methods: Static analysis and abstract interpretation 72/73
The End
Thank you for your attention!
Oral exam (subscribe via IS!)
30 min
Topics
Everything we have covered in the course.
Including the material not on the slides!
IA159 Formal Verification Methods: Static analysis and abstract interpretation 73/73