Assessing the Modeling of Aspect State Machines for Testing from the Perspective of Modelers

Aspect state machines (ASMs) are extended UML state machines that use stereotypes from a UML profile called AspectSM. In our previous experiments, we empirically evaluated ASMs from the perspectives of readability, comprehensibility, understand ability, modeling errors, and modeling effort and the results showed that ASMs are significantly better than the standard UML state machines for modeling robustness behavior for testing. However, a fundamental question still remained to be answered about how modelers/testers modeling ASMs feel about their use. With this in mind, we report results from a series of controlled experiments that were conducted to evaluate subjective opinions of modelers/testers from various perspectives using several questionnaires. The results of the experiment showed that the participants found it difficult to apply AspectSM and weren´t confident about their solutions. We further observed that the participants´ understand ability and experience of applying AspectSM improved after performing various modeling activities. Although, our results seem very generic, but notice that these results provide preliminary evidence about these observations, which is missing in the Aspect-Oriented Modeling literature.