Effective Object-Oriented Behavioural Modelling with State Nets

Systems analysis in software engineering is concerned with the modelling of both the static and dynamic aspects of the system under study. However, static modelling seems to have received more attention than behaviour modelling over the years. This is particularly true within the object-oriented paradigm, where behavioural models are generally extensions of finite state machines that relate only loosely to the underlying conceptual basis of object-orientation and may be limited in terms of computational and/or expressive power. The contribution of this paper is two-fold. First, it describes state nets, a novel, object-oriented behavioural model, in which behaviour is expressed by the states of objects, the triggers causing transitions between states and the actions performed by objects in states and transitions. Second, it shows that state nets are effective, in the sense that they are at least as powerful as Turing machines, i.e., they have sufficient computational power to capture arbitrary behaviours.