Aspect-Oriented Compositions for Dynamic Behavior Models

Abstract

The crosscutting problem can be observed at scenario modeling level where one model may present several tangled concerns, compromising requirements and system evolution. To avoid this problem, we can deal with aspectual scenarios by modularizing and thus separating them from other scenarios. Also, it is desirable to analyze how the crosscutting scenarios interact with other scenarios at early stages of software development; otherwise these interactions will only become clear during later stages when problems are much more expensive to solve. But to achieve that scenario modularization is not enough, we need efficient model composition mechanisms to allow the system to be analyzed and validated in its entirety. We introduce the Modeling Aspects Using a Transformation Approach (MATA), an expressive technique based on graph transformations, where aspectual scenarios, here represented as sequence diagram and state machines, will be modeled and composed efficiently. An example, based on a common case study, illustrates the usage of MATA’s modularization and composition mechanisms.