A Language Description is More than a Metamodel

Within the context of (software) language engineering, language descriptions are considered first class citizens. One of the ways to describe languages is by means of a metamodel, which represents the abstract syntax of the language. Unfortunately, in this process many language engineers forget the fact that a language also needs a concrete syntax and a semantics. In this paper I argue that neither of these can be discarded from a language description. In a good language description the abstract syntax is the central element, which functions as pivot between concrete syntax and semantics. Furthermore, both concrete syntax and semantics should be described in a well-defined formalism

Generating collaborative systems for digital libraries: A model-driven approach

This is an open access article shared under a Creative Commons Attribution 3.0 Licence (http://creativecommons.org/licenses/by/3.0/). Copyright @ 2010 The Authors.The design and development of a digital library involves different stakeholders, such as: information architects, librarians, and domain experts, who need to agree on a common language to describe, discuss, and negotiate the services the library has to offer. To this end, high-level, language-neutral models have to be devised. Metamodeling techniques favor the definition of domainspecific visual languages through which stakeholders can share their views and directly manipulate representations of the domain entities. This paper describes CRADLE (Cooperative-Relational Approach to Digital Library Environments), a metamodel-based framework and visual language for the definition of notions and services related to the development of digital libraries. A collection of tools allows the automatic generation of several services, defined with the CRADLE visual language, and of the graphical user interfaces providing access to them for the final user. The effectiveness of the approach is illustrated by presenting digital libraries generated with CRADLE, while the CRADLE environment has been evaluated by using the cognitive dimensions framework

FLEXIBLE METHOD ADAPTATION IN CASE: The Metamodeling Approach

There is an obvious need to improve the adaptation of methods into Computer Aided Software/Systems Engineering (CASE) tools. This paper presents a new approach to adapting methods into CASE environments, called metamodeling. It applies a new generation CASE tool or CASE shell to offer flexible mechanisms to specify and implement methods, or to modify existing ones in tools. This allows customization of CASE support for local needs. Metamodeling is a key step in such a customization and adaptation task. During metamodeling a formal model of the method to be supported is derived. This paper offers guidelines for a method adaptation process based on metamodeling. The goal of the process is to examine and improve methods so as to adapt them flexibly and successfully into the contingent local needs in order to achieve a sufficient fit between users\u27 cognitive skills, special methods, and tool support. The practicality of the method adaptation guidelines is demonstrated by reporting a case study where a \u27manual\u27 method called Activity Modelling was adapted into a CASE shell called MetaEdit. We also suggest criteria to evaluate the adaptation outcome and illuminate them in our adaptation case. The paper ends up with a speculation of how the nature of IS development methods is likely to change due to increased computer support. This will make the dominating \u27paperand- pencil\u27 mentality obsolete, and introduce more flexible, complex and versatile methods which are supported by powerful analytical tools offering unprecented functionality such as simulation or hypertext features. We believe that the metamodeling approach will form an essential core of method development and use in years to come, as it can be used to extend and modify organizations\u27 knowledge about methods and to make them learn more rapidly

Evolutionary model type selection for global surrogate modeling

Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type

Screening and metamodeling of computer experiments with functional outputs. Application to thermal-hydraulic computations

To perform uncertainty, sensitivity or optimization analysis on scalar variables calculated by a cpu time expensive computer code, a widely accepted methodology consists in first identifying the most influential uncertain inputs (by screening techniques), and then in replacing the cpu time expensive model by a cpu inexpensive mathematical function, called a metamodel. This paper extends this methodology to the functional output case, for instance when the model output variables are curves. The screening approach is based on the analysis of variance and principal component analysis of output curves. The functional metamodeling consists in a curve classification step, a dimension reduction step, then a classical metamodeling step. An industrial nuclear reactor application (dealing with uncertainties in the pressurized thermal shock analysis) illustrates all these steps