This document discusses all-pairs testing, also known as pairwise or combinatorial testing. It is a technique that generates a subset of combinations to test all possible pairs of input parameters in order to achieve high test coverage with fewer tests. The document provides an example of applying all-pairs testing to test the parameters of a car ordering application, including order category, location, car brand, registration number, order type, and order time. By using two representative values each for registration number and order time, the number of possible test case combinations is reduced from 240000 to 96, and further reduced to 8 cases by using all-pairs testing.

1. All pairs testing
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2.  Called ‘Combinatorial Testing’ technique
 Smart testing???
 Pairwise technique that delivers hundred
percent test coverage
 Execute discrete combinations of each pair
of input parameters.
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3.  a subset of combinations is generated !!
 helpful for designing tests for applications
involving multiple parameters
 not exhaustive & effective in finding bugs
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4.  Car Ordering Application:
 The car ordering application allows for Buying and Selling
cars. It should support trading in Delhi and Mumbai.
 The application should have registration numbers, may be
valid or invalid. It should allow the trade of following cars:
BMW, Audi, and Mercedes.
 Two types of booking can be done: E-booking and In Store.
 Orders can be placed only during trading hours.
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5.  1) Order category
a. Buy
b. Sell
 2) Location
a. Delhi
b. Mumbai
 3) Car brand
a. BMW
b. Audi
c. Mercedes
 4) Registration numbers
a. Valid (5000)
b. Invalid
 5) Order type
a. E-Booking
b. In-store
 6) Order time
a. Working hours
b. Non-working hours
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6. 2 X 2 X 3 X 5000 X 2 X 2= 240000
Valid test cases combinations
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7.  Reduce Registration Number to Two(by using
smart representative sample)
 Valid registration number
 Invalid registration number
 Now let’s calculate the number of possible
combinations:
= 2 X 2 X 3 X 2 X 2 X 2
= 96
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8. 8

9. 9

10.  For each set of values in column 1, we put both values of column 2.
Repeat the same for column 3.
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11.  repeat the same steps for column 3 and 4.
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12. Hence, all pairs in 8 cases, instead of all
combinations in 96!
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13.  It fails when the values selected for testing
are incorrect.
 It fails when highly probable combinations
get too little attention.
 It fails when interactions between the
variables are not understood well.
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14.  dramatically reduce the number of
combinations to be covered
 win-win situation for both test effort and
test effectiveness.
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