Black-Box Testing Techniques II
Software Testing and Verification
Lecture 5
Prepared by
Stephen M. Thebaut, Ph.D.
University of Florida
Black-Box Test Case Design Techniques
Considered
• Partition testing
• Combinatorial Approaches
• Boundary Value Analysis
• Intuition & Experience
Cause-Effect Analysis
• Cause-Effect Analysis is a combinatorial
approach that can be viewed as a logical
extension of partition testing.
• It extends the idea of partitioning a multidimensional input space by providing a
systematic means for generating test case
templates to cover different combinations
of input “Causes” resulting in output
“Effects.”
Cause-Effect Analysis
• Cause-Effect Analysis is a combinatorial
approach that can be viewed as a logical
extension of partition testing.
• It extends the idea of partitioning a multidimensional input space by providing a
systematic means for generating test case
templates to cover different combinations
of input “Causes” resulting in output
“Effects.”
Causes and Effects
• A CAUSE may be thought of as a distinct
input condition, or an “equivalence
class” of input conditions.
• An EFFECT may be thought of as a
distinct output condition or change in
program state.
Causes and Effects
• A CAUSE may be thought of as a distinct
input condition, or an “equivalence
class” of input conditions.
• An EFFECT may be thought of as a
distinct output condition or change in
program state.
Causes and Effects
• Causes and Effects are represented as
Boolean variables.
• The logical relationships among them
CAN (but need not) be represented as
one or more Boolean graphs.
Causes and Effects
• Causes and Effects are represented as
Boolean variables.
• The logical relationships among them
CAN (but need not) be represented as
one or more Boolean graphs.
Л 
Causes
Effects
V 
C-E Analysis Process Steps
1. Identify Causes and Effects
– The most critical and usually the
most difficult step
– Choose an appropriate level of
abstraction.
– Divide and conquer as necessary.
– Effects may or may not be mutually
exclusive.
C-E Analysis Process Steps
1. Identify Causes and Effects
– The most critical and usually the
most difficult step
– Choose an appropriate level of
abstraction.
– Divide and conquer as necessary.
– Effects may or may not be mutually
exclusive.
C-E Analysis Process Steps
1. Identify Causes and Effects
– The most critical and usually the
most difficult step
– Choose an appropriate level of
abstraction.
– Divide and conquer as necessary.
– Effects may or may not be mutually
exclusive.
C-E Analysis Process Steps
1. Identify Causes and Effects
– The most critical and usually the
most difficult step
– Choose an appropriate level of
abstraction.
– Divide and conquer as necessary.
– Effects may or may not be mutually
exclusive.
C-E Analysis Process Steps
1. Identify Causes and Effects
– The most critical and usually the
most difficult step
– Choose an appropriate level of
abstraction.
– Divide and conquer as necessary.
– Effects may or may not be mutually
exclusive.
C-E Analysis Process Steps (cont’d)
2. Deduce Logical Relationships and
Constraints
– Relationships take the form of
conditionals and utilize the logical
operators AND, OR, and NOT.
– Constraints describe relationships
among Causes that allow for the
identification of infeasible (i.e.,
impracticable) combinations.
C-E Analysis Process Steps (cont’d)
2. Deduce Logical Relationships and
Constraints
– Relationships take the form of
conditionals and utilize the logical
operators AND, OR, and NOT.
– Constraints describe relationships
among Causes that allow for the
identification of infeasible (i.e.,
impracticable) combinations.
C-E Analysis Process Steps (cont’d)
2. Deduce Logical Relationships and
Constraints
– Relationships take the form of
conditionals and utilize the logical
operators AND, OR, and NOT.
– Constraints describe relationships
among Causes that allow for the
identification of infeasible (i.e.,
impracticable) combinations.
(cont’d)
C-E Analysis Process Steps (cont’d)
2. Deduce Logical Relationships and
Constraints (cont’d)
– Boolean graphs provide a convenient
and economical way to visualize
relationships and constraints.
C-E Analysis Process Steps (cont’d)
3. Identify an appropriate Test Case
Selection Strategy
– Determines the number and nature
of Cause-combinations to be
considered.
– Strategies can be designed to meet a
variety of coverage requirements/
cost constraints.
C-E Analysis Process Steps (cont’d)
3. Identify an appropriate Test Case
Selection Strategy
– Determines the number and nature
of Cause-combinations to be
considered.
– Strategies can be designed to meet a
variety of coverage requirements/
cost constraints.
C-E Analysis Process Steps (cont’d)
3. Identify an appropriate Test Case
Selection Strategy
– Determines the number and nature
of Cause-combinations to be
considered.
– Strategies can be designed to meet a
variety of coverage requirements/
cost constraints.
C-E Analysis Process Steps (cont’d)
4. Construct a Test Case Coverage Matrix
– Typically involves tracing through
the Cause-Effect relationships to
identify combinations of Causes
resulting in each Effect according to
the selection strategy chosen.
– This can be extremely tedious if
done manually...
C-E Analysis Process Steps (cont’d)
4. Construct a Test Case Coverage Matrix
– Typically involves tracing through
the Cause-Effect relationships to
identify combinations of Causes
resulting in each Effect according to
the selection strategy chosen.
– This can be extremely tedious if
done manually...
C-E Analysis Process Steps (cont’d)
4. Construct a Test Case Coverage Matrix
– Typically involves tracing through
the Cause-Effect relationships to
identify combinations of Causes
resulting in each Effect according to
the selection strategy chosen.
– This can be extremely tedious if
done manually...
Question…
To what extent do you think CASE
support might be applicable to each step
in the process? For which steps do you
think it might be most important?
Question…
To what extent do you think CASE
support might be applicable to each step
in the process? For which steps do you
think it might be most important?
(We’ll come back to this after illustrating
the process steps with some examples.)
Illustration of Step 1 (Identify Causes
and Effects)
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%.
Guidelines for identifying Causes
and Effects
• Underline words or phrases in the
specification that correspond to
input/output conditions or changes in
state.
Guidelines for identifying Causes
and Effects (cont’d)
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%.
Guidelines for identifying Causes
and Effects (cont’d)
• List each Cause and Effect.
• Assign a unique number to each (use
different number ranges to differentiate
Causes from Effects).
Guidelines for identifying Causes
and Effects (cont’d)
• List each Cause and Effect.
• Assign a unique number to each (use
different number ranges to differentiate
Causes from Effects).
Illustration of Step 1 (cont’d)
Ignoring, again, the unspecified responses
to “invalid” inputs, we have:
Causes:
Effects:
(1) Non-Resident
(11) 1% tax
(2) Resident
(12) 5% tax
(3) $0  Gross Pay  $30K
(13) 15% tax
(4) $30K  Gross Pay  $50K
(5) Gross Pay  $50K
Illustration of Step 2 (Deduce Logical
Relationships and Constraints)
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%.
What are the constraints?
Causes:
Effects:
(1) Non-Resident
(11) 1% tax
(2) Resident
(12) 5% tax
(3) $0  Gross Pay  $30K
(13) 15% tax
(4) $30K  Gross Pay  $50K
(5) Gross Pay  $50K
Constraints deducible from spec,
problem domain knowledge, etc.
A. [(1) Л ¬(2)] V [¬(1) Л (2)] (i.e., one, and
only one of (1) and (2) must be true.)
B. [(3) Л ¬(4) Л ¬(5)] V [¬(3) Л (4) Л ¬(5)] V
[¬(3) Л ¬(4) Л (5)]
C. [(11) Л ¬(12) Л ¬(13)] V [¬(11) Л (12) Л
¬(13)] V [¬(11) Л ¬(12) Л (13)]
Constraints deducible from spec,
problem domain knowledge, etc.
A. [(1) Л ¬(2)] V [¬(1) Л (2)] (i.e., one, and
only one of (1) and (2) must be true.)
B. [(3) Л ¬(4) Л ¬(5)] V [¬(3) Л (4) Л ¬(5)] V
[¬(3) Л ¬(4) Л (5)]
C. [(11) Л ¬(12) Л ¬(13)] V [¬(11) Л (12) Л
¬(13)] V [¬(11) Л ¬(12) Л (13)]
Constraints deducible from spec,
problem domain knowledge, etc.
A. [(1) Л ¬(2)] V [¬(1) Л (2)] (i.e., one, and
only one of (1) and (2) must be true.)
B. [(3) Л ¬(4) Л ¬(5)] V [¬(3) Л (4) Л ¬(5)] V
[¬(3) Л ¬(4) Л (5)]
C. [(11) Л ¬(12) Л ¬(13)] V [¬(11) Л (12) Л
¬(13)] V [¬(11) Л ¬(12) Л (13)]
Constraints deducible from spec,
problem domain knowledge, etc.
A. [(1) Л ¬(2)] V [¬(1) Л (2)] (i.e., one, and
only one of (1) and (2) must be true.)
B. [(3) Л ¬(4) Л ¬(5)] V [¬(3) Л (4) Л ¬(5)] V
[¬(3) Л ¬(4) Л (5)]
C. [(11) Л ¬(12) Л ¬(13)] V [¬(11) Л (12) Л
¬(13)] V [¬(11) Л ¬(12) Л (13)]
What are the logical relationships?
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%.
Conditionals deducible from
specification and constraints
• From the specification we have:
(1) => (11)
[(2) Л (3)] => (11)
[(2) Л (4)] => (12)
[(2) Л (5)] => (13)
Conditionals deducible from
specification and constraints (cont’d)
• Which, in light of the identified constraints,
simplify to:
[(1) V (3) => (11)
[(2) Л (4)] => (12)
[(2) Л (5)] => (13)
Boolean Graph Representation
(1)
V 
(11)
(3)
(4)
Л  (12)
(2)
Л  (13)
(5)
Boolean Graph Representation
Non-Res (1)
V 
(11) 1% tax
[0,30K] (3)
(30K,50K] (4)
Л  (12)
5% tax
Л  (13)
15% tax
Res (2)
>50K (5)
Boolean Graph Representation
Non-Res (1)
V 
(11) 1% tax
[0,30K] (3)
O
O
(30K,50K] (4)
O
Л  (12)
5% tax
Л  (13)
15% tax
Res (2)
>50K (5)
Cause/Effect Constraints
Exclusive
E
“at most one”
One & Only One
Inclusive
I
“at least one”
Requires
A
O
A => B
“one and only one”
B
Illustration of Step 3 (Identify Test
Case Selection Strategy)
• Simple (but extreme) strategies:
– “All Feasible Combinations of Cause
Values” (AFCCV)
– “All Effects” (AE)
• For the relationships depicted in our graph,
how many test cases would be required to
achieve AFCCV coverage? AE coverage?
Illustration of Step 3 (Identify Test
Case Selection Strategy)
• Simple (but extreme) strategies:
– “All Feasible Combinations of Cause
Values” (AFCCV)
– “All Effects” (AE)
• For the relationships depicted in our graph,
how many test cases would be required to
achieve AFCCV coverage? AE coverage?
Illustration of Step 3 (Identify Test
Case Selection Strategy)
• Simple (but extreme) strategies:
– “All Feasible Combinations of Cause
Values” (AFCCV)
– “All Effects” (AE)
• For the relationships depicted in our graph,
how many test cases would be required to
achieve AFCCV coverage? AE coverage?
Illustration of Step 3 (Identify Test
Case Selection Strategy)
• Simple (but extreme) strategies:
– “All Feasible Combinations of Cause
Values” (AFCCV)
– “All Effects” (AE)
• For the relationships depicted in our graph,
how many test cases would be required to
achieve AFCCV coverage? AE coverage?
AFCCV and AE Coverage
Non-Res (1)
V 
(11) 1% tax
[0,30K] (3)
O
O
(30K,50K] (4)
O
Л  (12)
5% tax
Л  (13)
15% tax
Res (2)
>50K (5)
AFCCV and AE Coverage (cont’d)
• AFCCV:
– There are 25 = 32 possible value
combinations for all 5 Causes.
– For Causes (1) and (2), there are 2
feasible value combination pairs (due
to the “one and only one” constraint):
TF and FT.
AFCCV and AE Coverage (cont’d)
• AFCCV:
– There are 25 = 32 possible value
combinations for all 5 Causes.
– For Causes (1) and (2), there are 2
feasible value combination pairs (due
to the “one and only one” constraint):
TF and FT.
AFCCV and AE Coverage (cont’d)
• AFCCV:
– There are 25 = 32 possible value
combinations for all 5 Causes.
– For Causes (1) and (2), there are 2
feasible value combination pairs (due
to the “one and only one” constraint):
TF and FT.
(cont’d)
AFCCV and AE Coverage (cont’d)
• AFCCV: (cont’d)
– Similarly, for Causes (3), (4), and (5),
there are 3 feasible value combination
triples: TFF, FTF, and FFT.
– Thus, there are 2 X 3 = 6 feasible
combinations of values for all 5
Causes, requiring a total of 6 test
cases.
AFCCV and AE Coverage (cont’d)
• AFCCV: (cont’d)
– Similarly, for Causes (3), (4), and (5),
there are 3 feasible value combination
triples: TFF, FTF, and FFT.
– Thus, there are 2 X 3 = 6 feasible
combinations of values for all 5
Causes, requiring a total of 6 test
cases.
How about AE Coverage?
Non-Res (1)
V 
(11) 1% tax
[0,30K] (3)
O
O
(30K,50K] (4)
O
Л  (12)
5% tax
Л  (13)
15% tax
Res (2)
>50K (5)
AFCCV and AE Coverage (cont’d)
• AE:
– There are 3 mutually exclusive
Effects.
– Thus, a different combination of
Cause values is required for each
Effect to evaluate to True.
– Therefore, 3 test cases are required.
AFCCV and AE Coverage (cont’d)
• AE:
– There are 3 mutually exclusive
Effects.
– Thus, a different combination of
Cause values is required for each
Effect to evaluate to True.
– Therefore, 3 test cases are required.
AFCCV and AE Coverage (cont’d)
• AE:
– There are 3 mutually exclusive
Effects.
– Thus, a different combination of
Cause values is required for each
Effect to evaluate to True.
– Therefore, 3 test cases are required.
AFCCV and AE Coverage (cont’d)
• AE:
– There are 3 mutually exclusive
Effects.
– Thus, a different combination of
Cause values is required for each
Effect to evaluate to True.
– Therefore, 3 test cases are required.
(cont’d)
AFCCV and AE Coverage (cont’d)
• AE: (cont’d)
– In general, when there are N Effects,
N or fewer test cases are required
for AE Coverage.
– When the N Effects are mutually
exclusive, all N test cases are
required.
AFCCV and AE Coverage (cont’d)
• AE: (cont’d)
– In general, when there are N Effects,
N or fewer test cases are required
for AE Coverage.
– When the N Effects are mutually
exclusive, all N test cases are
required.
AFCCV and AE Coverage (cont’d)
• Note that AE is analogous to partitioning
an input space based solely on the
specified outputs...
• AFCCV is analogous to associating a
separate equivalence class with every
(feasible) combination of the individual
input classes (i.e., the “brute-force”
approach)...
• Question: do these strategies depend on
the Cause-Effect relationships?
Partitioning Based on Specified Output
Gross_Pay
 30K
Res?
yes
1%
(30K, 50K]
> 50K
5%
15%
no
Would you be comfortable with the degree of
coverage afforded by choosing ONE test case
from each of these 3 partitions? Why or why
not?
AFCCV and AE Coverage (cont’d)
• Note that AE is analogous to partitioning
an input space based solely on the
specified outputs...
• AFCCV is analogous to associating a
separate equivalence class with every
(feasible) combination of the individual
input classes (i.e., the “brute-force”
approach)...
• Question: do these strategies depend on
the Cause-Effect relationships?
The “Brute-Force” Approach
We could “hedge our bet” by associating a
separate partition with every (feasible)
combination of classes from the sets:
Gross_Pay
Res?
 30K
(30K, 50K]
> 50K
yes
1%
5%
15%
no
1%
1%
1%
What are the pros and cons of this approach?
AFCCV and AE Coverage (cont’d)
• Note that AE is analogous to partitioning
an input space based solely on the
specified outputs...
• AFCCV is analogous to associating a
separate equivalence class with every
(feasible) combination of the individual
input classes (i.e., the “brute-force”
approach)...
• Question: do these strategies depend on
the Cause-Effect relationships?
AFCCV and AE Coverage (cont’d)
• Note that AE is analogous to partitioning
an input space based solely on the
specified outputs...
• AFCCV is analogous to associating a
separate equivalence class with every
(feasible) combination of the individual
input classes (i.e., the “brute-force”
approach)...
• Question: do these strategies depend on
the Cause-Effect relationships?
AFCCV: NO
AE: YES
Another Test Case Selection
Strategy…
Non-Res (1)
V 
(11) 1% tax
[0,30K] (3)
O
O
(30K,50K] (4)
O
Л  (12)
5% tax
Л  (13)
15% tax
Res (2)
>50K (5)
Another Test Case Selection
Strategy…
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.
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.
What Should We Call this Strategy?
How about: All Feasible Combinations of
Connected Cause Values that Result in
Each Effect being True (AFCCCVREET)?
What Should We Call this Strategy?
How about: All Feasible Combinations of
Connected Cause Values that Result in
Each Effect being True (AFCCCVREET)?
Nah…let’s just call it “Strategy #3”.
Applying Strategy #3
Non-Res (1)
V 
(11) 1% tax
[0,30K] (3)
O
O
(30K,50K] (4)
O
Л  (12)
5% tax
Л  (13)
15% tax
Res (2)
>50K (5)
Applying Strategy #3
Non-Res (1)
V 
(11) 1% tax
[0,30K] (3)
O
O
(30K,50K] (4)
O
Л  (12)
5% tax
Л  (13)
15% tax
Res (2)
>50K (5)
Applying Strategy #3
• Cause Value Combinations for Effect 11:
(1) V (3)  1, 3 or
1, 3 or
1,
3
• Cause Value Combinations for Effect 12:
• Cause Value Combinations for Effect 13:
Coverage Matrix
TEST CASES
CAUSES
1
2
3
Non-Resident
(1)
T
T
F
Resident
(2)
F
F
T
$0  Gross Pay  $30K
(3)
T
F
T
$30K  Gross Pay  $50 (4)
F

F
Gross Pay  $50K
(5)
F

F
1% tax
(11)
T
T
T
5% tax
(12)
F
F
F
15% tax
(13)
F
F
F
4
5
EFFECTS
 don’t care, subject to Cause constraint B
Applying Strategy #3
Non-Res (1)
V 
(11) 1% tax
[0,30K] (3)
O
O
(30K,50K] (4)
O
Л  (12)
5% tax
Л  (13)
15% tax
Res (2)
>50K (5)
Applying Strategy #3
• Cause Value Combinations for Effect 11:
(1) V (3)  1, 3 or
1, 3 or
1, 3
• Cause Value Combinations for Effect 12:
(4) Л (2)  2, 4
• Cause Value Combinations for Effect 13:
Coverage Matrix (cont’d)
TEST CASES
CAUSES
1
2
3
4
Non-Resident
(1)
T
T
F
F
Resident
(2)
F
F
T
T
$0  Gross Pay  $30K
(3)
T
F
T
F
$30K  Gross Pay  $50K (4)
F

F
T
Gross Pay  $50K
(5)
F

F
F
1% tax
(11)
T
T
T
F
5% tax
(12)
F
F
F
T
15% tax
(13)
F
F
F
F
5
EFFECTS
 don’t care, subject to Cause constraint B
Applying Strategy #3
Non-Res (1)
V 
(11) 1% tax
[0,30K] (3)
O
O
(30K,50K] (4)
O
Л  (12)
5% tax
Л  (13)
15% tax
Res (2)
>50K (5)
Applying Strategy #3
• Cause Value Combinations for Effect 11:
(1) V (3)  1, 3 or
1, 3 or
1, 3
• Cause Value Combinations for Effect 12:
(4) Л (2)  2, 4
• Cause Value Combinations for Effect 13:
(5) Л (2)  2, 5
Coverage Matrix (cont’d)
TEST CASES
CAUSES
1
2
3
4
5
Non-Resident
(1)
T
T
F
F
F
Resident
(2)
F
F
T
T
T
$0  Gross Pay  $30K
(3)
T
F
T
F
F
$30K  Gross Pay  $50K (4)
F

F
T
F
Gross Pay  $50K
(5)
F

F
F
T
1% tax
(11)
T
T
T
F
F
5% tax
(12)
F
F
F
T
F
15% tax
(13)
F
F
F
F
T
EFFECTS
 don’t care, subject to Cause constraint B
Complete Coverage Matrix
TEST CASES
CAUSES
1
2
3
4
5
Non-Resident
(1)
T
T
F
F
F
Resident
(2)
F
F
T
T
T
$0  Gross Pay  $30K
(3)
T
F
T
F
F
$30K  Gross Pay  $50K (4)
F

F
T
F
Gross Pay  $50K
(5)
F

F
F
T
1% tax
(11)
T
T
T
F
F
5% tax
(12)
F
F
F
T
F
15% tax
(13)
F
F
F
F
T
EFFECTS
 don’t care, subject to Cause constraint B
Comparing Strategy #3 to AFCCV
and AE Coverage
• How does Strategy #3 differ from AFCCV?
– For each Effect, only the connected
Causes are considered.
– It is less conservative:
•
Does not ensure that every feasible
combination of Cause values will be
covered.
•
And thus does not ensure that every
feasible combination of Effect values will
be covered. (Relevant when Effects are
not mutually exclusive.)
Comparing Strategy #3 to AFCCV
and AE Coverage
• How does Strategy #3 differ from AFCCV?
– For each Effect, only the connected
Causes are considered.
– It is less conservative:
•
Does not ensure that every feasible
combination of Cause values will be
covered.
•
And thus does not ensure that every
feasible combination of Effect values will
be covered. (Relevant when Effects are
not mutually exclusive.)
Comparing Strategy #3 to AFCCV
and AE Coverage
• How does Strategy #3 differ from AFCCV?
– For each Effect, only the connected
Causes are considered.
– It is less conservative:
•
Does not ensure that every feasible
combination of Cause values will be
covered.
•
And thus does not ensure that every
feasible combination of Effect values will
be covered. (Relevant when Effects are
not mutually exclusive.)
Complete Coverage Matrix
TEST CASES
CAUSES
1
2
3
4
5
Non-Resident
(1)
T
T
F
F
F
Resident
(2)
F
F
T
T
T
$0  Gross Pay  $30K
(3)
T
F
T
F
F
$30K  Gross Pay  $50K (4)
F

F
T
F
Gross Pay  $50K
(5)
F

F
F
T
1% tax
(11)
T
T
T
F
F
5% tax
(12)
F
F
F
T
F
15% tax
(13)
F
F
F
F
T
EFFECTS
 don’t care, subject to Cause constraint B
Comparing Strategy #3 to AFCCV
and AE Coverage
• How does Strategy #3 differ from AFCCV?
– For each Effect, only the connected
Causes are considered.
– It is less conservative:
•
Does not ensure that every feasible
combination of Cause values will be
covered.
•
And thus does not ensure that every
feasible combination of Effect values will
be covered. (Relevant when Effects are
not mutually exclusive.)
Comparing Strategy #3 to AFCCV
and AE Coverage
• How does it differ from AE?
– It is more conservative (ALL feasible
combinations of connected Cause
values must be covered for each
Effect).
Comparing Strategy #3 to AFCCV
and AE Coverage
• How does it differ from AE?
– It is more conservative (ALL feasible
combinations of connected Cause
values must be covered for each
Effect).
Coming up in “Black-Box Testing
Techniques III”…
• We step-through another (somewhat
more complex) example of Cause-Effect
Analysis,
• Describe a test case design technique for
exploring boundary conditions, and
• Consider a test case design strategy
based on intuition and experience.
Coming up in “Black-Box Testing
Techniques III”…
• We step-through another (somewhat
more complex) example of Cause-Effect
Analysis,
• Describe a test case design technique for
exploring boundary conditions, and
• Consider a test case design strategy
based on intuition and experience.
Coming up in “Black-Box Testing
Techniques III”…
• We step-through another (somewhat
more complex) example of Cause-Effect
Analysis,
• Describe a test case design technique for
exploring boundary conditions, and
• Consider a test case design strategy
based on intuition and experience.
Black-Box Testing Techniques II
Software Testing and Verification
Lecture 5
Prepared by
Stephen M. Thebaut, Ph.D.
University of Florida