Ptolemaic Metamodelling?: The Need for a Paradigm Shift

1. Introduction

Model-Driven Engineering (MDE) focuses on models and model transformations. At the very heart of modelling is the need for models to be depicted in some way by using modelling languages (e.g. Selic, 2011), typically specified by a metamodel (e.g. Sanchez Cuadrado, de Lara, & Guerra, 2012). Metamodels are thus a (or maybe the) core element in MDE – current and future metamodelling approaches are the topic of this chapter.

The scientific approach to knowledge creation and acquisition has been the topic of many books and papers. It embodies a combination of theory building and theory testing and the ability to replace old theories when newer data and/or newer theories offer a more consistent and scientific explanation of the topic under consideration. The framework in which the theory resides also implies a philosophical underpinning, a paradigmatic choice that is seldom discussed or even appreciated. This interaction between theory and empirically obtained data for testing that theory is crucial in science and, hence also, in all branches of computing (e.g. Genova, 2010). Notwithstanding, there is an unfortunate trend in journal reviewing of software engineering research to reject papers that have little or no empirical support (as observed by Genova, 2010). Indeed, Broy (2011) observes that “in the long run, software engineering cannot be established without a solid body of scientific theory.” When scientific frameworks are totally reconstructed, often because they have degraded to a point where their coherence is challenged, it is said that a paradigm shift has occurred (Kuhn, 1962).

In this chapter, we focus on models and metamodels. Models have a long history in scientific research: “If you want to understand some aspect of the Universe, it helps if you simplify it as much as possible, and include only those properties and characteristics that are essential to understanding. If you want to determine how an object drops, you don’t concern yourself with whether it is new or old, is red or green, has an odour or not. You eliminate those things and thus do not needlessly complicate matters. The simplification you can call a model or a simulation and you can present it either as an actual representation on a computer screen, or as a mathematical relationship” (Asimov, 1988). However, even with this illustrious and lengthy history, Ritchey (2012) argues that there is still no general theory of modelling.

Here, we take a largely theoretical approach, where our evaluation approach uses examples taken from considering the real world and asking whether each model provides a good or a better appreciation of the target problem domain. In order to create such a comprehensive modelling approach, we integrate knowledge from various sub-fields of computing: software engineering, modelling (especially the use of UML and, more generally, conceptual modelling), as well as considering research knowledge from outside the traditional context of computing: ontology engineering, philosophy, mathematics, language use (especially speech acts) and cognitive linguistics. For the non-computing fields, we note that each has its own conferences and journals and, importantly, their own communities, which tend often to be ‘clubs’ or ‘closed shops’ with their own internal terminology immediately understandable by members of the club but unintelligible to those outside (e.g. Kuhn, 1962). To improve our models and our modelling axioms and constraints, we need to break into each and every of these clubs.