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SOFTWARE QUALITY ASSURANCE BLACK BOX Seminar: Oana FEIDI Quality Manager – Continental Automotive

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SOFTWARE QUALITY ASSURANCEBLACK BOX

Seminar: Oana FEIDIQuality Manager – Continental Automotive

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Preview

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Preview

the problem …very large or infinite number of test scenarios

+finite amount of time

=

impossible to test everything

the solution … Software test techniques exist to reduce the

number of tests to be run whilst still providing sufficient coverage of the system under test

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Overview

Black-box testing: Test cases derived from specifications The focus is not the design, nor the

implementation

positive testing → testing the implementation against specified conditions

negative testing → testing the implementation against unspecified conditions (unspecified inputs)→ stability and robustness of specifications

Input Output

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Equivalence partition

create partitions of the input and output values of the component

each partition shall contain a set or range of values, chosen such that all values can reasonably expected to be treated by the component in the same way

both valid and invalid values are partitioned in this way

For each test case specify: Input to the component Partition exercised The expected outcome of the test case

Test completeness criteria: test at least one input/output pair for each equivalence partition

Indicates when to stop testing

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Equivalence partition

Example: f(int month) – want to test this function

…, -1, 0, 1, 2, 3, …., 9, 10, 11, 12, 13, 14, ….______||____________________||__________ invalid1 valid partition invalid2

f(int month, int nrdays) – want to test this function1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑31 28/29 31 30 31 30 31 31 30 31 30 31

Valid partition: [(1, 3, 5, 7, 8, 10, 12; 31), (4, 6, 9, 11; 30), …] Invalid partition: [(1, 2, 3, 5, 7, 8, 10, 12; 30), …]

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Boundary Analysis

refinement of equivalence partitioning for which each edge of an equivalence class is a representative element of the class

invalid-input elements are found just beyond the ends

For each test case specify: the input(s) to the component the partition boundaries exercised The expected outcome of the test case

Test completeness criteria: test at least one input/output pair for each equivalence partition and the “borders” between the equivalence partitions

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Boundary Analysis

Example: f(int month) – want to test this function

if (month > 0 && month < 13) or

if (month >= 0 && month < 13)

…, -1, 0, | 1, 2, 3, …., 9, 10, 11, 12 |, 13, 14, ….|_________________________|

valid partition

Test: 0, 1, 12, 13

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State Transition Testing

use a model of the states the component may occupy, transitions between those states, the events which cause those transitions, and the actions which may result from those transitions

the model shall comprise states, transitions, events, actions and their relationships

For each test case specify: the starting state of the component the input(s) to the component the expected outputs from the component the expected final state

Test completeness criteria: 100% of the state transition diagram

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State Transition Testing

start

single

being born

married

getting married

widow

husband dies

remarried

divorced

remarried

getting divorced

dying

death

death

death

death

reborn

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Black Box - summary

Equivalence partition create partitions of the input and output values of the component each partition shall contain a set or range of values, chosen such that all

values can reasonably expected to be treated by the component in the same way

both valid and invalid values are partitioned in this way

Boundary Analysis refinement of equivalence partitioning for which each edge of an

equivalence class is a representative element of the class invalid-input elements are found just beyond the ends

State Transition Testing use a model of the states the component may occupy, transitions

between those states, the events which cause those transitions, and the actions which may result from those transitions

the model shall comprise states, transitions, events, actions and their relationships

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Decision table Decision Table

A decision table is a tabular form that presents a set of conditions and their corresponding actions.

Condition Stubs Condition stubs describe the conditions or factors that will affect the decision

or policy. They are listed in the upper section of the decision table.

Action Stubs Action stubs describe, in the form of statements, the possible policy actions or

decisions. They are listed in the lower section of the decision table.

Rules Rules describe which actions are to be taken under a specific combination of

conditions. They are specified by first inserting different combinations of condition attribute values and then putting X's in the appropriate columns of the action section of the table.

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Decision Table Methodology1. Identify Conditions & Values Find the data attribute each condition tests and all of the attribute's

values.

2. Compute Max Number of Rules

Multiply the number of values for each condition data attribute by each other.

3. Identify Possible Actions Determine each independent action to be taken for the decision or policy.

4. Enter All Possible Rules Fill in the values of the condition data attributes in each numbered rule column.

5. Define Actions for each Rule For each rule, mark the appropriate actions with an X in the decision table.

6. Verify the Policy Review completed decision table with end-users.

7. Simplify the Table Eliminate and/or consolidate rules to reduce the number of columns.

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Example

A marketing company wishes to construct a decision table to decide how to treat clients according to three characteristics: Gender, City Dweller, and age group: A (under 30), B (between 30 and 60), C (over 60). The company has four products (W, X, Y and Z) to test market.

Product W will appeal to female city dwellers. Product X will appeal to young females. Product Y will appeal to Male middle aged shoppers

who do not live in cities. Product Z will appeal to all but older females.

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Example

1. Identify Conditions & Values gender: M, F city dweller: Y, N age group: A, B, C

2. Compute Maximum Number of Rules: 2 x 2 x 3 = 12

3. Identify Possible Actions: market product W market product X market product Y market product Z

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Example

4. Enter All Possible

Rules 1 2 3 4 5 6 7 8 9 10 11 12

Gender F M F M F M F M F M F M

City Y Y N N Y Y N N Y Y N N

Age A A A A B B B B C C C C

1 2 3 4 5 6 7 8 9 10 11 12

W X X X

X X X

Y X

Z X X X X X X X X X X

• 5. Define Actions for each Rule

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Example

Simplify the Table rules 2, 4, 6, 7, 10, 12 have

the same action pattern rules 2, 6 and 10 have two

of the three condition values (gender and city dweller) identical and all three of the values of the non- identical value (age) are covered, so they can be condensed into a single column 2

The rules 4 and 12 have identical action pattern, but they cannot be combined because the indifferent attribute "Age" does not have all its values covered in these two columns. Age group B is missing

Conditions

Gender F M F M F F M F F M

City Y Y N N Y N N Y N N

Age A - A A B B B C C C

Actions

W X X X

X X X

Y X

Z X X X X X X X X

1 2 3 4 5 6 7 8 9 10

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Conclusions

Advantages of Black Box Testing more effective on larger units of code than

glass box testing tester needs no knowledge of implementation,

including specific programming languages tester and programmer are independent of

each other tests are done from a user's point of view will help to expose any ambiguities or

inconsistencies in the specifications test cases can be designed as soon as the

specifications are complete

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Conclusions

Disadvantages of Black Box Testing only a small number of possible inputs can actually

be tested, to test every possible input stream would take nearly forever

without clear and concise specifications, test cases are hard to design

there may be unnecessary repetition of test inputs if the tester is not informed of test cases the programmer has already tried

may leave many program paths untested cannot be directed toward specific segments of

code which may be very complex (and therefore more error prone)

most testing related research has been directed toward glass box testing