Z Zvezdan Protic

Transforming Process Algebra Models into UML State Machines: Bridging a Semantic Gap?

There exist many formalisms for modeling the behavior of (software) systems. These formalisms serve different purposes. Process algebras are used for algebraic and axiomatic reasoning about the behavior of distributed systems. UML state machines are suitable for automatic software generation. We have developed a transformation from the process algebra ACP into UML state machines to enable automatic software generation from process algebra models. This transformation needs to preserve both behavioral and structural properties. The combination of these preservation requirements gives rise to a semantic gap. It implies that we cannot transform ACP models into UML state machines on a syntactic level only. We address this semantic gap and propose a way of bridging it. To validate our proposal, we have implemented a tool for automatic transformation of ACP process algebra models into UML state machines.

Model-Driven Software Engineering

Software plays an important role in designing and operating warehouses. However, traditional software engineering methods for designing warehouse software are not able to cope with the complexity, size, and increase of automation in modern warehouses. This chapter describes Model-Driven Software Engineering (MDSE), a discipline aimed at dealing with the increased complexity of software by focusing on the problem domain rather than on the solution domain. In warehouse design, this is achieved by using formal models to describe warehouses, and by using model transformations to transforms those models to, e.g. source code. MDSE relies on tools more than traditional software engineering. Thus, to reap the full benefits of MDSE, tools for developing, managing, and transforming models should be designed, implemented, used, and validated.

A generic solution for syntax-driven model co-evolution

In this paper we discuss, and provide a generic solution to the problem referred to as model co-evolution: How to evolve models in case their metamodels evolve? We solve this problem by extending a traditional three-step approach. In the first step, differences between an original and an evolved metamodel are determined. Unlike traditional approaches, we treat metamodels as models conforming to a special metamodel, thus the same difference representation and calculation mechanisms for metamodels as for models are used in our approach. In the second step, metamodel differences are classified into four groups based on their possible influence on co-evolving models, and the possibilities of handling them automatically. We adopt two of these groups (non-breaking and breaking and resolvable differences) from the existing co-evolution approaches, and we introduce two new groups (breaking and semi-resolvable and breaking and human-resolvable differences). In the third step, based on the determined metamodel differences, a generic co-evolution transformation is invoked. This transformation takes the metamodel differences, and a model as arguments, and returns an adapted model. We validated our approach by incorporating our method into a prototype tool for generic model co-evolution, and by testing this tool on a large set of metamodels and models.