Model Complexity

Modelling for testing has its roots in the modelling of physical systems. The key concept behind modelling is to abstract or focus on aspects of the system to be studied or tested instead of getting blinded by the details. It is easier said than done but in modelling for testing we have to keep in mind every day to model only relevant parts, i.e., to resist the temptation to spend too much time on details (= falling in love with our model) or cramming all functionality to be tested in one single model. Putting too many (or even too few) details in the model results in wrong abstraction level for given test objectives. You are not using modelling correctly when you (mis)use it as a form of graphical programming.

Document from ISTQB is a good read on what to expect from MBT tools such as Creator.

Now how do we know when have we gone too far and modelled too much detail? One way to get an understanding about that is by measuring model complexity. Measurement of model complexity does not provide all the answers but it also helps to create models that can be maintained by multiple people – a model that more people understand than just the author. Complex models usually lead to longer test generation times and longer turnaround times to analyze and validate your model based on test generation results. Therefore managing model complexity saves your valuable time.