# Hoare Logic Basics

Hoare logic is a way of inserting annotations into code to make proofs about program behavior simpler. We first explain them using sets and relations.

## Example Proof

//{0 <= y} i = y; //{0 <= y & i = y} r = 0; //{0 <= y & i = y & r = 0} while //{r = (y-i)*x & 0 <= i} (i > 0) ( //{r = (y-i)*x & 0 < i} r = r + x; //{r = (y-i+1)*x & 0 < i} i = i - 1 //{r = (y-i)*x & 0 <= i} ) //{r = x * y}

## Hoare Triple for Sets and Relations

When (sets of states) and (relation on states, command semantics) then Hoare triple

means

We call precondition and postcondition.

Note: weakest conditions (predicates) correspond to largest sets; strongest conditions (predicates) correspond to smallest sets that satisfy a given property (Graphically, a stronger condition denotes one quadrant in plane, whereas a weaker condition denotes the entire half-plane.)

## Strongest Postcondition - sp

### Lemma: Characterization of sp

is the the smallest set such that , that is:

## Backward Propagation of Errors

If we have a relation and a set of errors , we can check if program meets specification by checking:

In other words, we obtain an upper bound on the set of states from which we do not reach error. We next introduce the notion of weakest precondition, which allows us to express from given as complement of error states .

## Weakest Precondition - wp

Definition: for , ,

Note that this is in general not the same as when then relation is non-deterministic or partial.

### Lemma: Characterization of wp

is the largest set such that , that is:

## Some More Laws on Preconditions and Postconditions

We next list several more lemmas on properties of wp, sp, and Hoare triples.

### Postcondition of inverse versus wp

If instead of good states we look at the completement set of “error states”, then corresponds to doing backwards. In other words, we have the following:

### Disjunctivity of sp

### Conjunctivity of wp

### Pointwise wp

### Pointwise sp

### Three Forms of Hoare Triple

The following three conditions are equivalent:

## Expanding Paths

The condition

is equivalent to

## Transitivity

If and then also .

We write this as the following inference rule:

## Hoare Logic for Loops

The following inference rule holds:

Proof is by transitivity.

By Expanding Paths condition above, we then have:

In fact, , so we have

This is the rule for non-deterministic loops.