Assessing quality and effort of applying aspect state machines for robustness testing: A controlled experiment

Aspect-Oriented Modeling (AOM) has been the subject of intense research over the last decade and aims to provide numerous benefits to modeling, such as enhanced modularization, easier evolution, higher quality as well as reduced modeling effort. However, these benefits can only be obtained at the cost of learning and applying new modeling approaches. Studying their applicability is therefore important to assess whether they are worth using in practice. In this paper, we report a controlled experiment to assess the applicability of AOM, focusing on a recently published UML profile (AspectSM). This profile was originally designed to support model-based robustness testing in an industrial context but is applicable to the behavioral modeling of other crosscutting concerns. This experiment assesses the applicability of AspectSM from two aspects: the quality of derived state machines and the effort required to build them. With AspectSM, a crosscutting behavior is modeled using an 'aspect state machine'. The applicability of aspect state machines is evaluated by comparing them with standard UML state machines that directly model the entire system behavior, including crosscutting concerns. The quality of both aspect and standard UML state machines derived by subjects is measured by comparing them against predefined reference state machines. Results show that aspect state machines derived with AspectSM are significantly more complete and correct though AspectSM took significantly more time than the standard approach.