Lecture 4 Skeleton

The structure of this lecture is similar to the previous one:

Today, we extend the approach from last lecture (both generating and proving formulas) to support data structures such as references and arrays.

We use weakest preconditions, although you could also use strongest postconditions or any other variants of the conversion from programs to formulas.

More on wp

wp of conjunction and disjunction

  • what holds, why, and when

wp of havoc

Can we derive wp(Q,havoc(x))?

  • By consider havoc(x) as (… [] x=-1 [] x = 0 [] x = 1 [] …) ?
  • From relational definition?

By wp semantics of havoc and assume

wp(Q, havoc(x); assume(x=t)) = 

Desugaring loops with loop invariants

while [inv I] (F) c1

havoc(x1,...,xn);    (those that can be modified in the loop)

Provided that I is inductive. We can check that I is inductive by proving:


in isolation. Or proving


at any point. If we combine these two, we get:

 (assume(~F) []
  assert I;

Benefit: if there is x_{n+1} that is not changed, we do not need to write its properties in the loop invariant. This can make loop invariant shorter.

References about weakest precondition (under construction)

  • (some online references will come here)
  • Back, Wright: Refinement Calculus
  • Edsger W. Dijkstra: A Discipline of Programming
  • C. A. R. Hoare, He Jifeng: Unifying Theories of Programming

Modeling data structures

Semantics of global arrays

If then else expressions.

Desugaring if-then-else.

Avoiding exponential explosion using flattening.

Array bounds checking.

Semantics of references

Objects as references, null as an object.

Program with class declaration

class Node {
   Node left, right;

How can we represent fields?

Possible mathematical model: fields as functions from objects to objects.

left : Node => Node
right : Node => Node

What is the meaning of assignment?

x.f = y

f[x \mapsto y](z) = \left\{ \begin{array}{lr}
y, & z=x   \\
f(z), & z \neq x 

left, right - uninterpreted functions (can have any value, depending on the program, unlike arithmetic functions such as +,-,* that have fixed interpretation).

Null checks.

Modeling dynamic allocation of objects

All objects exist.

The allocated object set.

Dynamically allocated arrays

Why we need so many arguments.

Proving formulas with uninterpreted functions

Again, the second part! More technical. But, often you can use these things as a black box.

Congruence closure algorithm

Properties of relations

  • equivalence relation
  • congruence; example from modular arithmetic

Equality is a congruence with respect to all function symbols.

More information on congruence closure algorithm: