Domain-specific modeling languages (DSMLs) promise clear advantages over general-purpose modeling languages. However, their design poses a fundamental challenge. While economies of scale advocate the development of DSMLs that can be used in a wide range of cases, modeling productivity demands more specific language concepts tuned to individual requirements. Inspired by the actual use of technical languages (German: “Fachsprachen”), this paper presents a novel multilevel modeling approach to conceptual modeling and to the design of information systems. Unlike traditional language architectures such as Meta Object Facility (MOF), it features a recursive architecture that allows for an arbitrary number of classification levels and, hence, for the design of hierarchies of DSMLs ranging from reference DSMLs to “local” DSMLs. It can not only diminish the conflict inherent in designing DSMLs, but enables the reuse and integration of software artifacts in general. It also helps reduce modeling complexity by relaxing the rigid dichotomy between specialization and instantiation. Furthermore, it integrates a meta-modeling language with a metamodel of a reflective meta-programming language, thereby allowing for executable models. The specification of the language architecture is supplemented by the description of use scenarios that illustrate the potential of multilevel modeling and a critical discussion of its peculiarities.

Multilevel Modeling: Toward a New Paradigm of Conceptual Modeling and Information Systems Design

It is common for organizations to maintain multiple variants of a given business process, such as multiple sales processes for different products or multiple bookkeeping processes for different countries. Conventional business process modeling languages do not explicitly support the representation of such families of process variants. This gap triggered significant research efforts over the past decade, leading to an array of approaches to business process variability modeling. In general, each of these approaches extends a conventional process modeling language with constructs to capture customizable process models. A customizable process model represents a family of process variants in a way that a model of each variant can be derived by adding or deleting fragments according to customization options or according to a domain model. This survey draws up a systematic inventory of approaches to customizable process modeling and provides a comparative evaluation with the aim of identifying common and differentiating modeling features, providing criteria for selecting among multiple approaches, and identifying gaps in the state of the art. The survey puts into evidence an abundance of customizable process-modeling languages, which contrasts with a relative scarcity of available tool support and empirical comparative evaluations.

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Business Process Variability Modeling: A Survey

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[서평]「Applying UML and Patterns - An Introduction to Object-Oriented Analysis and Design」

It’s a fact of life that organizations love to reorganize. Reorganizations have a profound effect on the way product lines are governed. We introduce the concept of the Responsibility, Authority, and Accountability (RAA) network. An RAA network assists in the governance process of product lines for internal information systems, even in the face of massive reorganization. Armour (“Reorg Cycle” [1]) describes the pressures of reorganization to balance the “dimensions of organization” (e.g., geography, customers, product technology); we apply polarity management to balance the dimensions. Armour describes the difficulty of applying hierarchical organization charts—“single dimension management structures”—to the above “multidimensional environments”; we apply lean RAA networks to span organization charts and provide the multidimensional view needed for product lines. Armour observes that network organization approaches “do not have a good track record”; our experience is that lean, resilient RAA networks document the product line’s governance architecture. These governance architecture patterns are applied repeatedly to the strata of products in the product line. We present the governance architect’s RAA to define, monitor, and sustain governance health using these tools: polarity maps, polarity networks, RAA maps, and RAA networks.

Software Product Lines

Since ontology-driven software development (ODSD) is an integration of ontology technologies and model-driven software development (MDSD), it is necessary to identify and analyse technologies applied in MDSD. We define basic concepts, such as model-driven engineering, metamodelling, model transformation and technological space, and describe the state-of-the-art implementations of these concepts.

Model-Driven Software Development

Although domain-specific modeling tools have come a long way since the modern era of model-driven development started in the early 1990s and now offer an impressive range of features, there is still significant room for enhancing the flexibility they offer to end users and for combining the advantages of domain-specific and general-purpose languages. To do this, however, it is necessary to enhance the way in which the current generation of tools view metamodeling and support the representation of the multiple, ?ontological? classification levels that often exist in subject domains. State-of-the-art tools essentially allow users to describe the abstract and concrete syntaxes of a language in the form of metamodels and to make statements in that language in the form of models. These statements typically convey information in terms of types and instances in the domain (e.g., the classes and objects of UML), but not in terms of types of types (i.e., domain metaclasses), and types of types of types, and so on, across multiple classification levels. In essence, therefore, while they provide rich support for ?linguistic? metamodeling, the current generation of tools provides little if any built-in support for modeling ?ontological? classification across more than one type/instance level in the subject domain. In this paper, we describe a prototype implementation of a new kind of modeling infrastructure that, by providing built-in support for multiple ontological as well as linguistic classification levels, offers various advantages over existing language engineering approaches and tools. These include the ability to view a single model from the perspective of both a general-purpose and a domain-specific modeling language, the ability to define constraints across multiple ontological classification levels, and the ability to tie the rendering of model elements to ontological as well as linguistic types over multiple classification levels. After first outlining the key conceptual ingredients of this new infrastructure and presenting the main elements of our current realization, we show these benefits through two small examples.

A Flexible Infrastructure for Multilevel Language Engineering

As models and ontologies assume an increasingly central role in enterprise systems engineering the question of how they compare and can be used together assumes growing importance. On the one hand, the semantic web community is starting to propose a central role for “ontologies” in software engineering, while on the other hand the software engineering community has over the last few years been enthusiastically embracing “models” as the core artefact in software development. Superficially, however, ontologies and models are very similar, and in fact are sometimes visualized using the same language (e.g. UML). This has given rise to a variety of different interpretations of the roles of the two technologies, and had generated a certain degree of confusion about the relationship between them. So what exactly is the difference between ontologies and models and why are both concepts needed? Are they basically the same thing viewed from different angles or is there some fundamental difference between them beyond the idiosyncrasies of current tools and languages? This paper discusses this question. After considering common informal distinctions between ontologies and models, and analyzing their fundamental definitions, we propose how they might usefully be distinguished in the future.

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On the relationship of Ontologies and Models

In this chapter we present a version of the Trading System case study modeled according to the KobrA approach. KobrA is a UML-based method for describing components and component-based systems developed at the Fraunhofer Institute for Experimental Software Engineering at the beginning of the decade. The acronym stands for the term " Ko mponenten b asie r te A nwendungsentwicklung" --- German for "Component-based Application Development". KobrA has been successfully used by a number of companies in industrial settings and has given rise to numerous specializations and offshoots (e.g. MARMOT [1] and MORABIT [2]). The original version of the method [3] was developed for the UML 1.x flavor of the UML, but in this chapter we introduce an updated version optimized for use with the 2.x versions of the UML [4] and its related standards such as OCL [5]. KobrA also provides support for other advanced software engineering approaches such as product-lines, but these are beyond the scope of this chapter. Here we focus on the component-modeling aspects of the method.

Modeling Components and Component-Based Systems in KobrA

The advantage of supporting a uniform modeling approach across multiple, logical (or ontological) instantiation levels has been well documented in the literature. However, the published approaches for achieving this have focused on making it possible for classes and objects to be treated uniformly across multiple instantiation levels, but have neglected the problems involved in doing the same thing for "connectors" (i.e. concepts rendered as edges in graph based depiction of models rather than nodes). On closer examination, this turns out to be a significant problem, because without an effective strategy for modeling connectors in a uniform way, multi-level modeling as a whole is not possible. In this paper we describe the problems arising from the way in which connectors (e.g. associations, links, generalizations etc.) are currently supported in mainstream modeling languages such as the UML and why they are incompatible with multi-level modeling. We then define three fundamental connector rendering and representation principles that rectify the identified problems.

A Systematic Approach to Connectors in a Multi-level Modeling Environment

In this paper we present an approach to validate OCL constraints in the context of Java Data Objects (JDO). Based on RECODER1, a Java framework for source code metaprogramming, we have implemented a source code precompiler. Its objective is to modify the source code of a single class by adding the code to handle the validation of OCL constraints. To enable the JDO runtime environment to check these constraints e.g. before a persistent capable object is stored, the pre-compiler implements the methods defined in the InstanceCallbacks interface. Since JDO represents a data store independent abstraction layer, this approach ensures data integrity for any arbitrary data store. Moreover, as long as the constraints are loaded dynamically from a configuration file at runtime, it ensures also an easy adjustment of OCL constraints without the need of recompilation.

Matthias Gutheil

Papers

A Flexible Infrastructure for Multilevel Language Engineering

On the relationship of Ontologies and Models

Modeling Components and Component-Based Systems in KobrA

A Systematic Approach to Connectors in a Multi-level Modeling Environment

Enable an Automatic Validation of OCL Constraints in the Context of Java Data Objects