1. Black-Box Testing Techniques III
Software Testing and Verification
Lecture 6
Prepared by
Stephen M. Thebaut, Ph.D.
University of Florida

2. Another Cause-Effect Example: Symbol Table Storage Specification
The conditions for storing an identifier in one of two symbol tables are:
(a) must be from 2 to 8 characters in length;
(b) first character must be a letter or “$”;
(c) other characters must be a letter or digit.
If the first character is a letter, the identifier will be stored in symbol table A. If the first character is “$”, it will be stored in symbol table B.
If the first character is neither a letter nor “$”, or if condition (c) is not satisfied, error message J11 is output.
If condition (a) is not satisfied, error message J12 is output.

3. Causes and Effects Causes: Effects:
(1) 2  no. chars  8 (31) store in table A
(2) 1st char is letter (32) store in table B
(3) 1st char is $ (33) output msg J11
(4) other chars only
letters/digits (34) output msg J12
only
(35) output msgs J11
and J12

4. Boolean Graphs [2,8] let $ (1) (2) (3) (4) (31) (32) −> A −> B E
others let/dig

5. Boolean Graphs (cont’d)
[2,8]
let $
(1)
(2)
(3)
(4)
Л 
(31)
(32)
−> A
−> B E
others let/dig

6. Boolean Graphs (cont’d)
[2,8]
let $
(1)
(2)
(3)
(4)
Л 
(31)
(32)
−> A
−> B E
Л 
others let/dig

7. Boolean Graphs (cont’d)
[2,8]
let $
(1)
(2)
(3)
(4)
(33)
(34)
(35)
J11 only
J12 only
J11 & J12 E
others let/dig

8. Boolean Graphs (cont’d)
[2,8]
let $
(1)
(2)
(3)
(4)
Л 
(33)
(34)
(35)
J11 only
J12 only
J11 & J12
Л  E
(A)
V 
(B)
others let/dig

9. Boolean Graphs (cont’d)
[2,8]
let $
(1)
(2)
(3)
(4)
Л 
(33)
(34)
(35)
J11 only
J12 only
J11 & J12
Л 
Л  E
(A)
V 
(B)
others let/dig

10. Boolean Graphs (cont’d)
[2,8]
let $
(1)
(2)
(3)
(4)
Л 
(33)
(34)
(35)
J11 only
J12 only
J11 & J12
Л 
Л  E
(A)
V 
(B)
Л 
others let/dig

11. A Variation on Test Case Selection Strategy #3
Test case selection “Strategy #3” (p. 32) considers ALL feasible combinations of connected Cause values that result in each Effect being True.
For complex specifications, this can be impractical.
We now consider a variation on this strategy which “culls” all but the combinations “of greatest interest”.

12. Test Case Selection Strategy #3 Plus “Culling Rules”
REPEAT
Select the next (initially, the first) Effect.
Tracing back through the graph (right to left), find all feasible combinations of connected Cause values that result in the Effect being True, subject to the following culling rules:
When encountering an nth-degree OR-node that must be True, consider only those n combinations for which exactly one incoming edge is True.

13. Test Case Selection Strategy #3 Plus “Culling Rules” (cont’d)
When encountering an nth-degree AND-node that must be False, consider only those n combinations for which exactly one incoming edge is False.
For each new such combination found:
Determine values of all other Effects, and
Enter values for each Cause and Effect in a new column of the test case coverage matrix.
UNTIL each Effect has been selected.

14. Rationale for these Culling Rules?
Number of combinations decreases by a factor of O(2 ) to O(n) at each true OR node and each false AND node.
Idea: cover only the minimally sufficient conditions for the desired result. n ( ( V Л T F

15. Applying Strategy #3 Plus Culling Rules
[2,8]
let $
(1)
(2)
(3)
(4)
Л 
(31)
(32)
−> A
−> B E
Л 
others let/dig

16. Coverage Matrix TEST CASES CAUSES 1 2 3 4 5 6 7 8 9 10
2  no. chars  (1) T
1st char is letter (2)
1st char is $ (3) F
others letters/digits (4)
EFFECTS
store in table A (31)
store in table B (32)
output J11 only (33)
output J12 only (34)
output J11 & J (35)

17. Applying Strategy #3 Plus Culling Rules (cont’d)
[2,8]
let $
(1)
(2)
(3)
(4)
Л 
(31)
(32)
−> A
−> B E
Л 
others let/dig

18. Coverage Matrix (cont’d)
TEST CASES
CAUSES 1 2 3 4 5 6 7 8 9 10
2  no. chars  (1) T
1st char is letter (2) F
1st char is $ (3)
others letters/digits (4)
EFFECTS
store in table A (31)
store in table B (32)
output J11 only (33)
output J12 only (34)
output J11 & J (35)

19. Applying Strategy #3 Plus Culling Rules (cont’d)
[2,8]
let $
(1)
(2)
(3)
(4)
Л 
(33)
(34)
(35)
J11 only
J12 only
J11 & J12
Л 
Л  E
(A)
V 
(B)
Л 
others let/dig

20. Applying Strategy #3 Plus Culling Rules (cont’d)
(33)  1, B
B  (A V 4)
 (4, A) V (4, A) V (4, A)
(T,T) (T,F) (F,T)
culled (rule 1)

21. Applying Strategy #3 Plus Culling Rules (cont’d)

22. Applying Strategy #3 Plus Culling Rules (cont’d)

23. Applying Strategy #3 Plus Culling Rules (cont’d)
1, 4, 2, 3

24. Applying Strategy #3 Plus Culling Rules (cont’d)
1, 4, 2, 3

25. Applying Strategy #3 Plus Culling Rules (cont’d)
 (2, 3) V (2, 3) V (2, 3)
(F,F) (F,T) (T,F)
culled (rule 2)
and infeasible
1, 4, 2, 3

26. Applying Strategy #3 Plus Culling Rules (cont’d)
(33)  1, 4, 2, 3
1, 4, 2, 3
1, 4, 2, 3

27. Coverage Matrix (cont’d)
TEST CASES
CAUSES 1 2 3 4 5 6 7 8 9 10
2  no. chars  (1) T
1st char is letter (2) F
1st char is $ (3)
others letters/digits (4)
EFFECTS
store in table A (31)
store in table B (32)
output J11 only (33)
output J12 only (34)
output J11 & J (35)

28. Applying Strategy #3 Plus Culling Rules (cont’d)
[2,8]
let $
(1)
(2)
(3)
(4)
Л 
(33)
(34)
(35)
J11 only
J12 only
J11 & J12
Л 
Л  E
(A)
V 
(B)
Л 
others let/dig

29. Applying Strategy #3 Plus Culling Rules (cont’d)
(34)  1, B
B  (4 V A)
 4, A

30. Applying Strategy #3 Plus Culling Rules (cont’d)

31. Applying Strategy #3 Plus Culling Rules (cont’d)

32. Applying Strategy #3 Plus Culling Rules (cont’d)
 (2, 3) V (2, 3) V (2, 3)
(F,F) (F,T) (T,F)
culled (rule 2)
and infeasible

33. Applying Strategy #3 Plus Culling Rules (cont’d)
(34)  1, 4, 2, 3
 1, 4, 2, 3

34. Coverage Matrix (cont’d)
TEST CASES
CAUSES 1 2 3 4 5 6 7 8 9 10
2  no. chars  (1) T F
1st char is letter (2)
1st char is $ (3)
others letters/digits (4)
EFFECTS
store in table A (31)
store in table B (32)
output J11 only (33)
output J12 only (34)
output J11 & J (35)

35. Applying Strategy #3 Plus Culling Rules (cont’d)
[2,8]
let $
(1)
(2)
(3)
(4)
Л 
(33)
(34)
(35)
J11 only
J12 only
J11 & J12
Л 
Л  E
(A)
V 
(B)
Л 
others let/dig

36. Applying Strategy #3 Plus Culling Rules (cont’d)
(35)  1, B
Which are just the conditions associated with
error messages J11 (B) and J12 (1).
Combining these conditions from (33) and
(34) yields:
(35)  1, 4, 2, 3
1, 4, 2, 3
1, 4, 2, 3

37. Coverage Matrix (cont’d)
TEST CASES
CAUSES 1 2 3 4 5 6 7 8 9 10
2  no. chars  (1) T F
1st char is letter (2)
1st char is $ (3)
others letters/digits (4)
EFFECTS
store in table A (31)
store in table B (32)
output J11 only (33)
output J12 only (34)
output J11 & J (35)

38. Complete Coverage Matrix
TEST CASES
CAUSES 1 2 3 4 5 6 7 8 9 10
2  no. chars  (1) T F
1st char is letter (2)
1st char is $ (3)
others letters/digits (4)
EFFECTS
store in table A (31)
store in table B (32)
output J11 only (33)
output J12 only (34)
output J11 & J (35)

39. Cause-Effect Analysis: Discussion Questions & Exercises
Under what circumstances should Cause-Effect Analysis be used for test case design?
Are there any other obvious benefits?
What are the pros and cons of having a set of mutually exclusive Effects?

40. Cause-Effect Analysis: Discussion Questions & Exercises (cont’d)
Suppose that some program Effect is associated with integer input X being either  30 or even. What are the pros and cons of defining { X | X  30 V EVEN(X) } to be a Cause?
Devise a scenario that illustrates some creative ideas for how a well-engineered CASE tool could effectively support Cause-Effect Analysis.

41. Cause-Effect Analysis: Discussion Questions & Exercises (cont’d)
Cause-Effect Analysis seems well suited for “single-state-transition” program models in which Causes are mapped to Effects in one conceptual step. How could you apply the strategy to multi-state-transition program models?

42. Boundary Value Analysis
A technique based on identifying, and generating test cases to explore boundary conditions.
Boundary conditions are an extremely rich source of errors.
Natural language based specifications of boundaries are often ambiguous, as in “for input values of X between 0 and 40,...”

43. Boundary Value Analysis (cont’d)
May be applied to both input and output conditions.
Also applicable to white box testing (as will be illustrated later).

44. Guidelines for Identifying Boundary Values
“Range” guideline:
K will range in value from 0.0 to 4.0.
Identify values at the endpoints of the range and just beyond.
Boundary values: 0.0- (I)
0.0 (V)
4.0 (V)
4.0+ (I)

45. Guidelines for Identifying Boundary Values (cont’d)
“Number of values” guideline:
The file will contain 1-25 records.
Identify the minimum, the maximum, and values just below the minimum and above the maximum.
Boundary values: empty file (I), file with 1 (V), 25 (V), and 26 (I) records

46. Boundary Value Analysis Exercise
Identify appropriate boundary values for the following program specification fragment.

47. City Tax Specification 1:
The first input is a yes/no response to the question “Do you reside within the city?” The second input is gross pay for the year in question.
A non-resident will pay 1% of the gross pay in city tax.
Residents pay on the following scale:
- If gross pay is no more than $30,000, the tax is 1%.
- If gross pay is more than $30,000, but no more than $50,000, the tax is 5%.
- If gross pay is more than $50,000, the tax is 15%.

48. Test Case Design Based on Intuition and Experience
Also known as Error Guessing, Ad Hoc Testing, Artistic Testing, etc.
Testers utilize intuition and experience to identify potential errors and design test cases to reveal them.
Guidelines:
Design tests for reasonable but incorrect assumptions that may have been made by developers.
(cont’d)

49. Intuition and Experience (cont’d)
Guidelines: (cont’d)
Design tests to detect errors in handling special situations or cases.
Design tests to explore unexpected or unusual program use or environmental scenarios.

50. Intuition and Experience (cont’d)
Examples of data conditions to explore:
(1)
(2) Repeated instances or occurrences
(3) Repeated instances or occurrences
(4) Bl anks or null char acters in strings (eT c.)
(-5) Negative numbers
(#) Non-numeric values in numeric fields (or
vic3 versa)
(6789) Inputs that are too long or too short

51. Intuition and Experience (cont’d)
Testing based on intuition and experience can be extremely effective.
Test plans should reflect the explicit allocation of resources for this activity.
Consider the “Try to break our system –lunch is on us” example…

52. Intuition and Experience Exercise
Using intuition and experience, identify tests you would want to design for a subroutine that is to input and sort a list of strings based on a user-specified field.

53. Black-Box Testing Techniques III
Software Testing and Verification
Lecture 6
Prepared by
Stephen M. Thebaut, Ph.D.
University of Florida