It's common to have a test plan where the possible values of one parameter depend on the value of another parameter. There are many options for how you can represent this scenario in Hexawise, some options that involve using value expansions (when there is equivalence) and other options that do not use value expansions (when there is not equivalence).

Using Value Expansions in Hexawise

The general rule of thumb for value expansions is that they are for setting up equivalence classes. The key there being the equivalence. The expectations of the system should be the same for every value listed in that particular value expansion.

Let's consider a real world example involving a classification parameter with a value that is dependent on the value of a role parameter:

Inputs
Role: Student, Staff
Classification: Freshman, Sophomore, Junior, Senior, Adjunct, Assistant, Professor, Administrator

So if the Role parameter has a value of Student, then the Classification parameter must have a value of Freshman, Sophomore, Junior or Senior, but if the Role parameter has a value of Staff, then the Classification parameter must have a value of Adjunct, Assistant, Professor or Administrator.

Using value expansions in this case might be a good option. You could setup your inputs, value expansions and value pairs this way:

Inputs
Role: Student, Staff
Classification: Student Classification, Staff Classification

Value Expansion
Student Classification: Freshman, Sophomore, Junior, Senior
Staff Classification: Adjunct, Assistant, Professor, Administrator

Value Pairs
When Role=Student Always Classification=Student Classification
When Role=Staff Always Classification=Staff Classification

You would use this approach if there were no important differences in the business logic or expected behavior of the system when the different expansions of the value were used. If Freshman versus Sophomore is an important label for the users to be able to enter and see, but the system under test doesn't change its behavior based on which value is selected, then those expansions of the value are equivalent and don't need to be tested individually for how they might interact with other parts of the system and create bugs. If this equivalence scenario is true, then you will greatly simplify things for yourself and create fewer tests that are just as powerful by using value expansions.

In the scenario that would support using value expansions, the system might have different behavior for a Junior versus an Adjunct Professor, but not for a Freshman versus a Senior. A Freshman and a Senior are always equivalent in the system, so they can be combined in a value expansion.

However, if the expectations are not the same, then a value expansion should not be used. For example, let's suppose this hypothetical system has business logic giving priority class scheduling to Seniors and only last available scheduling priority to Administrators. In this case, using value expansions as described above would probably be a mistake. Why? Because a Sophomore and a Senior aren't treated the same way by the system, yet Hexawise considers all the expansions of the Student Classification value as equivalent. As long as you've got a test that has paired a value expansion of the Student Classification value with the Overbooked value of the Class Status parameter, then Hexawise won't insist on pairing all the other value expansions for the Student Classification value with Class Status = Overbooked in other tests. You could therefore miss a bug that only occurs when a Senior signs up for an overbooked class.

"One to many" or "multi-valued" married pair model

If the system under test does not consider the values to be equivalent and has requirements and business logic to behave differently, then by using value expansions to signal equivalency to Hexawise when there isn't equivalency is probably a mistake.

So what would you do in that case?

We've decided that it might be nice to be able to set up your inputs and value pairs like this:

Inputs
Role: Student, Staff
Classification: Freshman, Sophomore, Junior, Senior, Adjunct, Assistant, Professor, Administrator

Value Pairs
When Role=Student Always Classification=Freshman, Sophomore, Junior, or Senior When Role=Staff Always Classification=Adjunct, Assistant, Professor, or Administrator

Unfortunately, this kind of a "one to many" or "multi-valued" value pair is something we've only recently realized would be very helpful, and is something we have on the drawing board for Hexawise in the intermediate future, but is not a feature of Hexawise today. In the meantime, you could model it with three parameters:

Inputs
Role: Student, Staff
Student Classification: Freshman, Sophomore, Junior, Senior, N/A Staff Classification: Adjunct, Assistant, Professor, Administrator, N/A

Value Pairs
When Role=Student Always Staff Classification=N/A
When Role=Staff Always Student Classification=N/A

Another modeling option to consider, if there is only special logic for Administrator and for Seniors, but the rest of the values we've been discussing are equivalent, is to use value expansions for just the equivalent values:

Inputs
Role: Student, Staff
Classification: Underclassman, Senior, Professor, Administrator

Value Expansions
Underclassman: Freshman, Sophomore, Junior Professor: Adjunct, Assistant, Full

Value Pairs
When Role=Student Never Classification=Professor When Role=Student Never Classification=Administrator When Role=Staff Never Classification=Underclassman When Role=Staff Never Classification=Senior

I hope this helps you understand the role of value expansions in Hexawise, when to use them (in cases of equivalency), and when to avoid them, and how value pairs and value expansions can be used together to handle cases of dependent parameter values. Value Expansions are a powerful tool to help you decrease the number of tests you need to execute, so take advantage of them, and if you have any questions, just let us know!