1. Formal Program Specification
Software Testing and Verification
Lecture 16
Prepared by
Stephen M. Thebaut, Ph.D.
University of Florida

2. Overview Review of Basics
Propositions, propositional logic, predicates, predicate calculus
Sets, Relations, and Functions
Specification via pre- and post-conditions
Specifications via functions

3. Propositions, Propositional Logic, Predicates, and the Predicate Calculus

4. Propositions and Propositional Logic
A proposition, P, is a statement of some alleged fact which must be either true or false, and not both.
Which of the following are propositions?
elephants are mammals
France is in Asia
go away
5 > 4
X > 5

5. Propositions and Propositional Logic (cont’d)
Propositional Logic is a formal language that allows us to reason about propositions. The alphabet of this language is:
{P, Q, R, … Л, V, , , ¬}
where P, Q, R, … are propositions, and the other symbols, usually referred to as connectives, provide ways in which compound propositions can be built from simpler ones.

6. Truth Tables
Truth tables provide a concise way of giving the meaning of compound forms in a tabular form.
Example: construct a truth table to show all possible interpretation for the following sentences:
A V B, A  B, and A  B

7. Example A B
A V B
A  B
A  B T F

8. Equivalence
Two sentences are said to be equivalent if and only if their truth values are the same under every interpretation.
If A is equivalent to B, we write A  B.
Exercise: Use a truth table to show:
(P  Q)  (Q V ¬P)

9. How would you write A  B as “an expression of fact”?
Equivalence (cont’d)
Many users of logic slip into the habit of using  and  interchangeably.
However, AB is written down in the full knowledge that it may denote either true or false in some interpretation, whereas AB is an expression of fact (i.e., the writer thinks it is true).
How would you write A  B as “an expression of fact”?

10. Predicates
Predicates are expressions containing one or more free variables (place holders) that can be filled by suitable objects to create propositions.
For example, instantiating the value 2 for X in the predicate X>5 results in the (false) proposition 2>5.

11. Predicates (cont’d)
In general, a predicate itself has no truth value; it expresses a property or relation using variables.

12. Predicates (cont’d)
Two ways in which predicates can give rise to propositions:
As illustrated above, their free variables may be instantiated with the names of specific objects, and
They may be quantified. Quantification introduces two additional symbols:
 and 

13. Predicates (cont’d)
 and  are used to represent universal and existential quantification, respectively.
x • duck(x) represents the proposition “every object is a duck.”
x • duck(x) represents the proposition “there is at least one duck.”

14. Predicates (cont’d) x • Q(x,y) or x • Q(x,y)
For a predicate with two free variables, quantifying over one of them yields another predicate with one free variable, as in
x • Q(x,y) or x • Q(x,y)

15. Predicates (cont’d) i∈{1,2,…,N} • A[i]>0
Where appropriate, a domain of interest may be specified which identifies the objects for which the quantifier applies. For example,
i∈{1,2,…,N} • A[i]>0
represents the predicate “the first N elements of array A are all greater than 0.”

16. Predicate Calculus
The addition of a deductive apparatus gives us a formal system permitting proofs and derivations which we will refer to as the predicate calculus.
The system is based on providing rules of inference for introducing and removing each of the five connective symbols plus the two quantifiers.

17. Predicate Calculus (cont’d)
A rule of inference is expressed in the form:
A1, A2, …, An C
and is interpreted to mean
(A1 Л A2 Л … Л An)  C

18. Predicate Calculus (cont’d)
Examples of deductive rules:
A , B A A
A V B
¬¬A A
A, A  B B
(cont’d)

19. Predicate Calculus (cont’d)
Examples of deductive rules: (cont’d)
A  B
A  B
A  B, B  A
A  B
x • P(x)
P(n1) Л P(n2) Л … Л P(nk)

20. Sets, Relations, and Functions

21. Sets and Relations
A set is any well-defined collection of objects, called members or elements.
The relation of membership between a member, m, and a set, S, is written:
m  S
If m is not a member of S, we write:
m ∉ S

22. Sets and Relations (cont’d)
A relation, r, is a set whose members (if any) are all ordered pairs.
The set comprised of the first member of each pair is called the domain of r and is denoted D(r). Members of D(r) are called arguments of r.
The set comprised of the second member of each pair is called the range of r and is denoted R(r). Members of R(r) are called values of r.

23. Functions
A function, f, is a relation such that for each x ∈ D(f) there exists a unique element (x,y) ∈ f.
We often express this as y=f(x), where y is the unique value corresponding to x in the function f.
It is the uniqueness of y that distinguishes a function from other relations.

24. f(x) = x2 + 3x + 2 where D(f)={0,1}
Functions (cont’d)
It is often convenient to define a function by giving its domain and a rule for calculating the corresponding value for each argument in the domain.
For example:
f = {(x,y)|x ∈ {0,1}, y = x2 + 3x + 2}
This could also be written:
f(x) = x2 + 3x + 2 where D(f)={0,1}

25. Conditional Rules
Conditional rules are a sequence of (predicate  rule) pairs separated by vertical bars and enclosed in parentheses:
(p1  r1 | p2  r2 | … | pk  rk)

26. Conditional Rules (cont’d)
The meaning is: evaluate predicates p1, p2,…pk in order; for the first predicate, pi, which evaluates to true, if any, use the rule ri; if no predicate evaluates to true, the rule is undefined. (Note that “” ≠ “”.)
(p1  r1 | p2  r2 | … | pk  rk)

27. Conditional Rules (cont’d)
For example:
f = ((x,y)|(x divisible by 2  y = x/2 |
x divisible by 3  y = x/3 |
true  y = x))
Note that “true  r” has the effect of “if all else fails (i.e., if all the previous predicates evaluate to false), use r.”

28. Recursive Functions
A recursive function is a function that is defined by using the function itself in the rule that defines it. For example:
oddeven(x) = (x ∈ {0,1}  x |
x > 1  oddeven(x-2) |
x < 0  oddeven(x+2))
Exercise: define the factorial function recursively.

29. Specification via Pre- and Post-Conditions

30. Specification via Pre- and Post-Conditions
The (functional) requirements of a program may be specified by providing:
an explicit predicate on its state before execution (a pre-condition), and
an explicit predicate on its state after execution (a post-condition).

31. Specification via Pre- and Post-Conditions (cont’d)
Describing the state transition in two parts highlights the distinction between:
the assumptions that an implementer is allowed to make, and
the obligation that must be met.

32. Specification via Pre- and Post-Conditions (cont’d)
The language of pre- and post-conditions is that of the predicate calculus.
Predicates denote properties of program variables or relations between them.

33. Assumptions
Reference to a variable in a predicate implies that it exists and is defined.
Variables are assumed to be of type “integer”, unless the context of their use implies otherwise.
“A[1:N]” denotes an array with lower index bound of 1 and upper index bound of N (an integer constant).

34. Example 1
Consider the pre- and post-conditions for a program that sets variable MAX to the maximum value of two integers, A and B.
pre-condition: ?
post-condition: ?

35. What does “unsorted” mean here?
Example 2
Consider the pre- and post-conditions for a program that sets variable MIN to the minimum value in the unsorted, non-empty array A[1:N].
pre-condition: ?
post-condition: ?
What does “unsorted” mean here?

36. Example 2 (cont’d) Possible interpretations of “unsorted”:
(i∈{1,2,…,N-1} • A[i]A[i+1] V
i∈{1,2,…,N-1} • A[i]A[i+1])
“the sort operation has not been applied to A”
What was the specifier’s intent?

37. Specification via Functions

38. Specification via Functions
Programs may also be specified in terms of intended program functions.
These define explicit mappings from initial to final data states for individual variables and can be expanded into program control structures.
The correctness of an expansion can be determined by considering correctness conditions associated with the control structures relative to the intended function.

39. Specification via Functions (cont’d)
Data mappings may be specified via the use of a concurrent assignment function.
The domain of the function corresponds to the initial data states that would be trans-formed into final data states by a suitable program.
For example...

40. Specification via Functions (cont’d)
The conditional function:
f = (x  0 Л y  0  x, y := x+y, 0)
specifies a program, say F, for which:
the final value of x is required to be the sum of the initial values of x and y, and
the final value of y is required to be 0...
…if x and y are both initially  0. Otherwise, F may yield some other result (sufficient correct-ness) or not terminate (complete correctness) in keeping with f being undefined in this case.

41. Specification via Functions (cont’d)
Similarly, in a program with data space x, y, z, the sequence of assignment statements:
x := x+1; y := 2*x
computes a function that can be specified by the concurrent assignment function:
f = (x,y,z := x+1,2(x+1),z)
This function could also be specified using the short- hand notation:
f = (x,y := x+1,2(x+1))
implying an assignment into that portion of the data space containing x and y, while that containing z is assumed to be unmodified.

42. Specification via Functions (cont’d)
In addition, when an intended function is followed by a list of variables surrounded by “#” characters, the intent is to specify a program’s effect on these variables only. Other variables are assumed to receive arbitrary, unspecified values.
For example, consider a program with variables x, y, and temp. The intended function description:
f = (x,y := y,x) #x,y#
is equivalent to (x,y,temp := y,x,?) where “?” represents an arbitrary, unspecified value.

43. Comparing specification approaches
Pre- and post-conditions for a program with data space x, y, z, temp that is required to swap the values of x and y and leave z un-changed (but has no requirement concerning the disposition of temp):
pre-condition: {true}
post-condition: {x=y’ Л y=x’ Л z=z’}
Comparable intended function (f1):
f1 = (x,y := y,x) #x,y,z#
(z is unmodified and temp gets an unspecified value)

44. Comparing specification approaches (cont’d)
Pre- and post conditions given that the initial values of z and temp can be assumed to be greater that 0:
pre-condition: {z>0 Л temp>0}
post-condition: {x=y’ Л y=x’ Л z=z’}
Comparable† intended function (f2):
f2 = (z>0 Л temp>0  x,y := y,x) #x,y,z#
† “Comparable” in the context of sufficient correctness. f2
is “undefined” when (z>0 Л temp>0) evaluates to false.

45. Formal Program Specification
Software Testing and Verification
Lecture 16
Prepared by
Stephen M. Thebaut, Ph.D.
University of Florida