Equivalence class partioning and boundary value analysis

Data Driven Testing (DDT) is a proven method in software testing to efficiently execute test cases with different input data. The equivalence class partioning method helps to reduce the test set in a meaningful way without jeopardizing test coverage. In this article, we explain how this technique works and what benefits it offers in Data Driven testing. In addition, the method is combined with boundray value analysis to enable even more precise test case selection.

Basics of the equivalence class method

The equivalence class method is a test case derivation technique that aims to divide the input data of a system into groups or classes in which the values can be considered equivalent. Equivalent means that each value within a class should behave identically in the test case behavior, whereby it is sufficient to test with only one representative of each class.

Example of an equivalence class division

Assume a form accepts numbers between 1 and 100 as valid input:

Input values	Classification	Expected result
0	Lower limit -1	Defect
1	Lower limit	Success
100	Upper limit	Success
101	Upper limit +1	Defect

The combination of both methods ensures a more precise test coverage by testing representative values from each equivalence class as well as critical limit values.

Application in Data Driven Testing

Data Driven Testing is based on the idea of executing a large number of test cases with different input values. The equivalence class method helps to optimize the amount of test data.

Advantages of combining DDT with equivalence classes and limit value analysis

Reduction of redundant tests: Instead of testing all possible inputs, only representative values from each equivalence class and critical limits are selected.
Efficient test data management: Test data is more structured and easier to manage.
Higher test coverage with fewer test cases: Critical test cases are covered without performing unnecessary tests.
Increased defect detection: Tests at the edges of a class uncover potential limit value defects.
Automation-friendly: The combination with DDT makes automation simpler and easier to maintain.

Visualization of equivalence classes and boundray value analysis

Equivalence classes as a diagram

A diagram can illustrate the classification of the equivalence classes and the relevant limit values:

X-axis: Input values
Y-axis: Validity

Test data matrix for Data Driven Testing with boundary value analysis

Test case	Input values	Expected result
TC1	-10	Defect
TC2	0	Defect
TC3	1	Success
TC4	50	Success
TC5	100	Success
TC6	101	Defect

Example 1: Age check for the registration of an online platform

Suppose an online platform only allows users between the ages of 18 and 65 to register. The software validates the age based on the number entered by the user.

Equivalence class analysis

Input values	Equivalence class	Validity
0 – 17	Invalid	No
18 – 65	Valid	Yes
66 and higher	Invalid	No

Limit value analysis

Here we specifically test the values at the limits of the equivalence classes:

Test case	Input values	Expected result
TC1	17	Defect (too young)
TC2	18	Success (currently valid)
TC3	30	Success (middle of valid class)
TC4	65	Success (just valid)
TC5	66	Defect (too old)

Explanation of the test strategy

By combining the equivalence class method and boundary value analysis, we minimize the test cases while still ensuring maximum defect coverage.
Instead of testing all ages between 18 and 65, we choose only representative values.
The thresholds 17, 18, 65, 66 are critical as defects in the implementation often occur here.

This example shows how these simple methods help to efficiently test the quality of an application in practice.

The example above shows the simplest partioning into equivalence classes: a class with valid and a class with invalid representatives. The second example combines several data types with several equivalence classes.

Example 2: Flight ticket prices based on age and booking class

A flight booking system calculates the ticket price depending on the age of the passenger and the booking class. The prices and booking conditions are as follows:

Children (0-11 years) receive a 50% discount, but may only travel with an adult.
Teenagers (12-17 years) pay the full price.
Adults (18-64 years) pay the full price.
Seniors (65+ years) receive a 20% discount.
Normal prices apply in Economy class.
In Business Class there is a surcharge of 50% on the base fare.
In First Class there is a surcharge of 100% on the base fare.

Equivalence class analysis

Here we divide the possible inputs into equivalence classes for two data types:

Age of the passenger	Equivalence class	Permitted booking	Discount/surcharge
0 – 11 years	Kids	Only with adult	-50%
12 – 17 years	Teenagers	Yes	No discount
18 – 64 years	Adults	Yes	No discount
65+ years	Seniors	Yes	-20%

Booking class	Equivalent class	Price rule
Economy	Standard	Normal price
Business	Premium	+50%
First Class	Luxus	+100%

Boundary value analysis

Here we specifically test the boundary values between the equivalence classes:

Test case	Age	Booking class	Expected result
TC1	0	Economy	Defect (child without adult)
TC2	1	Economy	Defect (child without adult)
TC3	11	Economy	Defect (child without adult)
TC4	12	Economy	Success (transition child → teenager)
TC5	17	Economy	Success (last teenage stage)
TC6	18	Economy	Success (transition teenager → adult)
TC7	64	Economy	Success (last adult stage)
TC8	65	Economy	Success (transition adult → senior with 20% discount)
TC9	80	Economy	Success (Senior with 20% discount)
TC10	30	Business	Success (50% surcharge)
TC11	40	Frist Class	Success (100% surcharge)