What are the differences between equivalence partitioning and boundary value analysis?
An example of an AI chatbot conversation about differences in testing techniques.
What are the differences between equivalence partitioning and boundary value analysis?
Equivalence partitioning divides input data into valid and invalid partitions, while boundary value analysis focuses on values at the edges of these partitions.
Can you give me an example of each?
Sure! For equivalence partitioning, if a field accepts ages 18-65, valid partitions could be ages 18-30, 31-50, and 51-65. For boundary value analysis, you'd test ages 17, 18, 65, and 66.
Why is it important to use both techniques?
Using both techniques helps ensure comprehensive test coverage, identifying potential errors at both the boundaries and within the partitions.
Are there any scenarios where one is preferred over the other?
Yes, boundary value analysis is often preferred when dealing with ranges, while equivalence partitioning is useful for categorizing inputs.
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