Discussion Session 7: Program Verification

formula ::=  true | false
formula ::=  not formula
formula ::=  formula and formula
formula ::=  formula or formula

Python code:

def formula_true():
    return "True"

def formula_false():
    return "False"

def formula_not(formula):
    return {"Not", [formula]}

def formula_and(formula1, formula2):
    return {"And", [formula1, formula2]}

def formula_or(formula1, formula2):
    return {"Or", [formula1, formula2]}

def evaluateFormula(formula):
    if is_true(formula):
        return True
    if is_false(formula):
        return False
    if is_not(formula):
        return not evaluateFormula(formula["Not"][0])
    if is_and(formula):
        formula1 = formula["And"][0]
        formula2 = formula["And"][1]
        return evaluateFormula(formula1) and evaluateFormula(formula2)
    if is_or(formula):
        formula1 = formula["Or"][0]
        formula2 = formula["Or"][1]
        return evaluateFormula(formula1) or evaluateFormula(formula2)
    return None


  • What’s the type for formula?
  • How to prove that evaluateFormula is implemented correctly?
  • What is correct? (Hint: Evaluation Rule)

Bounded Exhaustive Testing

Set of all possible inputs is defined inductively. We can enumerate them exhaustively. See notes. The introduction of metric guarantees that we do enumerate all the possibilities.

  • If formula is of format “true” or “false”, then its height is 1.
  • If formula is of format “not formula0”, then its height is 1 + height of “formula0”.
  • If formula is of format “formula1 and formula2”, then its height is 1 + max(height of “formula1”, height of “formula2”.
  • If formula is of format “formula1 and formula2”, then its height is 1 + max(height of “formula1”, height of “formula2”.


def metric(f):
    if is_true(f) or is_false(f):
        return 1
    if is_not(f):
        return 1 + metric(f["Not"][0])
    if is_and(f):
        f1 = f["And"][0]
        f2 = f["And"][1]
        return 1 + max(metric(f1), metric(f2))
    if is_or(f):
        f1 = f["Or"][0]
        f2 = f["Or"][1]
        return 1 + max(metric(f1), metric(f2))

def formulas(n):
    if n <= 0:
    elif n == 1:
        return [formula_true(), formula_false()]
        fs = formulas(n-1)
        fsN = []
        fsN += [formula_not(f) for f in fs]
        fsN += [formula_and(f1, f2) for f1 in fs for f2 in fs]
        fsN += [formula_or(f1, f2)  for f1 in fs for f2 in fs]
        return fs + fsN

Proof by Induction

  • Base Case: evaluateFormula is correct for formula whose height is 1.
  • Inductive Step: The input formula has height n+1.
  • Induction Hypothesis: evaluateFormula is correct for formula whose height is <= n.

Example of fibonacci function

Definition of fibonacci function

fib(n) =

0 if n = 0

1 if n = 1

fib(n-1) + f(n-2) if n > 1

Implementation of fibonacci function

def Fib(n):
  def Fib0(n, x, y):
    if n = 0:
      return y
    if n > 0:
      return Fib0(n - 1, x + y, x)

  return Fib0(n, 1, 0)

Verification Task

For any n >= 0, fib(n) == Fib(n).

Proof By Induction

We prove the following instead.

For any n >= 0, for any a >= 0, Fib0(n, fib(a+1), fib(a)) == fib(a+n).

Base Case:
When n = 0, we have
for any a >= 0, Fib0(0, fib(a+1), fib(a)) = fib(a) <== (By def of Fib0)
Inductive Step:

n = m > 0

Inductive Hypothesis: For any m0 < m, for any a >= 0, Fib0(m0, fib(a+1), fib(a)) == fib(a+m0).

For any a >= 0, we have the following

Fib0(m, fib(a+1), fib(a))

= Fib0(m-1, fib(a+1) + fib(a), fib(a+1)) <== (By def of Fib0)

= Fib0(m-1, fib(a+2), fib(a+1)) <== (By def of fib)

= fib(a+1 + m-1) <== (By Induction Hypothesis)

= fib(a+m) <== (Done)