There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

物件導向軟體之架構(Object-Oriented Software Construction)探討

Metamodeling is an essential foundation for MDD, but there's little consensus on the precise form it should take and role it should play. The authors analyze the underlying motivation for MDD and then derive a concrete set of requirements that a supporting infrastructure should satisfy. They discuss why the traditional "language definition" interpretation of metamodeling isn't a sufficient foundation and explain how it can be extended to unlock MDD's full potential.

https://homepages.ecs.vuw.ac.nz/~tk/publications/papers/mda-foundation.pdf

Model-driven development: a metamodeling foundation

[서평]「Component Software」 - Beyond Object-Oriented Programming -

This book discusses how model-based approaches can improve the daily practice of software professionals. This is known as Model-Driven Software Engineering (MDSE) or, simply, Model-Driven Engineering (MDE). MDSE practices have proved to increase efficiency and effectiveness in software development, as demonstrated by various quantitative and qualitative studies. MDSE adoption in the software industry is foreseen to grow exponentially in the near future, e.g., due to the convergence of software development and business analysis. The aim of this book is to provide you with an agile and flexible tool to introduce you to the MDSE world, thus allowing you to quickly understand its basic principles and techniques and to choose the right set of MDSE instruments for your needs so that you can start to benefit from MDSE right away. The book is organized into two main parts. The first part discusses the foundations of MDSE in terms of basic concepts (i.e., models and transformations), driving principles, application scenarios and current standards, like the well-known MDA initiative proposed by OMG (Object Management Group) as well as the practices on how to integrate MDSE in existing development processes. The second part deals with the technical aspects of MDSE, spanning from the basics on when and how to build a domain-specific modeling language, to the description of Model-to-Text and Model-to-Model transformations, and the tools that support the management of MDSE projects. The book is targeted to a diverse set of readers, spanning: professionals, CTOs, CIOs, and team managers that need to have a bird's eye vision on the matter, so as to take the appropriate decisions when it comes to choosing the best development techniques for their company or team; software analysts, developers, or designers that expect to use MDSE for improving everyday work productivity, either by applying the basic modeling techniques and notations or by defining new domain-specific modeling languages and applying end-to-end MDSE practices in the software factory; and academic teachers and students to address undergrad and postgrad courses on MDSE. In addition to the contents of the book, more resources are provided on the book's website http://www.mdse-book.com/, including the examples presented in the book. Table of Contents: Introduction / MDSE Principles / MDSE Use Cases / Model-Driven Architecture (MDA) / Integration of MDSE in your Development Process / Modeling Languages at a Glance / Developing your Own Modeling Language / Model-to-Model Transformations / Model-to-Text Transformations / Managing Models / Summary

Model-Driven Software Engineering in Practice

With the recent trend to model driven engineering a common understanding of basic notions such as “model” and “metamodel” becomes a pivotal issue. Even though these notions have been in widespread use for quite a while, there is still little consensus about when exactly it is appropriate to use them. The aim of this article is to start establishing a consensus about generally acceptable terminology. Its main contributions are the distinction between two fundamentally different kinds of model roles, i.e. “token model” versus “type model” (The terms “type” and “token” have been introduced by C.S. Peirce, 1839–1914.), a formal notion of “metaness”, and the consideration of “generalization” as yet another basic relationship between models. In particular, the recognition of the fundamental difference between the above mentioned two kinds of model roles is crucial in order to enable communication among the model driven engineering community that is free of both unnoticed misunderstandings and unnecessary disagreement.

/pdf/matters-of-meta-modeling-93ga86waw7.pdf

Matters of (Meta-) Modeling

As the UMLattempts to make the transition from a single, albeit extensible, language to a framework for a family of languages, the nature and form of the underlying meta-modeling architecture will assume growing importance. It is generally recognized that without a simple, clean and intuitive theory of how metamodel levels are created and related to one another, the UML2.0 vision of a coherent family of languages with a common core set of concepts will remain elusive. However, no entirely satisfactory metamodeling approach has yet been found. Current (meta-)modeling theories used or proposed for the UML all have at least one fundamental problem that makes them unsuitable in their present form. In this paper we bring these problems into focus, and present some fundamental principles for overcoming them. We believe that these principles need to be embodied within the metamodeling framework ultimately adopted for the UML2.0 standard.

The Essence of Multilevel Metamodeling

Metamodeling is one of the core foundations of computer-automated multiparadigm modeling. However, there is currently little agreement about what form the required metamodeling approach should take and precisely what role metamodels should play. This article addresses the problem by first describing some fundamental problems in the industry's leading metamodeling technology, the UML framework, and then explaining how this framework could be rearchitected to overcome these problems. Three main issues are identified in the current framework: the dual classification problem arising from the need to capture both the logical and physical classification of model elements, the class/object duality problem arising from the need to capture both the classlike and objectlike facets of some model elements, and the replication of concepts problem arising from the need to define certain concepts multiple times. Three main proposals for rearchitecting the UML framework to overcome these problems are then presented: the separation of logical and physical classification dimensions, the unification of the class and object facets of model elements, and the enhancement of the instantiation mechanism to allow definitions to transcend multiple levels. The article concludes with a discussion of other practical issues involved in rearchitecting the UML modeling framework in the proposed way.

/pdf/rearchitecting-the-uml-infrastructure-1lgrz5vkf2.pdf

Rearchitecting the UML infrastructure

A fundamental principle in engineering, including software engineering, is to minimize the amount of accidental complexity which is introduced into engineering solutions due to mismatches between a problem and the technology used to represent the problem. As model-driven development moves to the center stage of software engineering, it is particularly important that this principle be applied to the technologies used to create and manipulate models, especially models that are intended to be free of solution decisions. At present, however, there is a significant mismatch between the “two level” modeling paradigm used to construct mainstream domain models and the conceptual information such models are required to represent—a mismatch that makes such models more complex than they need be. In this paper, we identify the precise nature of the mismatch, discuss a number of more or less satisfactory workarounds, and show how it can be avoided.

/pdf/reducing-accidental-complexity-in-domain-models-zk6svow6oe.pdf

Thomas Kühne

Papers

Model-driven development: a metamodeling foundation

Matters of (Meta-) Modeling

The Essence of Multilevel Metamodeling

Rearchitecting the UML infrastructure

Reducing Accidental Complexity in Domain Models