Improving the accuracy of UML metamodel extensions by introducing induced associations

In the process of extending the UML metamodel for a specific domain, the metamodel specifier introduces frequently some metaassociations at MOF level M2 with the aim that they induce some specific associations at MOF level M1. For instance, if a metamodel for software process modelling states that a “Role” is responsible for an “Artifact”, we can interpret that its specifier intended to model two aspects: (1) the implications of this metaassociation at level M1 (e.g., the specific instance of Role “TestEngineer” is responsible for the specific instance of Artifact “TestPlans”); and (2) the implications of this metaassociation at level M0 (e.g., “John Doe” is the responsible test engineer for elaborating the test plans for the package “Foo”). Unfortunately, the second aspect is often not enforced by the metamodel and, as a result, the models which are defined as its instances may not incorporate it. This problem, consequence of the so-called “shallow instantiation” in Atkinson and Kühne (Procs. UML’01, LNCS 2185, Springer, 2001), prevents these models from being accurate enough in the sense that they do not express all the information intended by the metamodel specifier and consequently do not distinguish metaassociations that induce associations at M1 from those that do not. In this article we introduce the concept of induced association that may come up when an extension of the UML metamodel is developed. The implications that this concept has both in the extended metamodel and in its instances are discussed. We also present a methodology to enforce that M1 models incorporate the associations induced by the metamodel which they are instances from. Next, as an example of application we present a quality metamodel for software artifacts which makes intensive use of induced associations. Finally, we introduce a software tool to assist the development of quality models as correct instantiations of the metamodel, assuring the proper application of the induced associations as required by the metamodel.     

Error-correcting Petri nets

The paper introduces error-correcting Petri nets, an algebraic methodology for designing synthetic biologic systems with monitoring capabilities. Linear error-correcting codes are used to extend the net’s structure in a way that allows for the algebraic detection and correction of non-reachable net markings. The presented methodology is based on modulo-p Hamming codes—which are optimal for the modulo-p correction of single errors—but also works with any other linear error-correcting code.     

Metamodeling using extended radial basis functions: a comparative approach

The process of constructing computationally benign approximations of expensive computer simulation codes, or metamodeling, is a critical component of several large-scale multidisciplinary design optimization (MDO) approaches. Such applications typically involve complex models, such as finite elements, computational fluid dynamics, or chemical processes. The decision regarding the most appropriate metamodeling approach usually depends on the type of application. However, several newly proposed kernel-based metamodeling approaches can provide consistently accurate performance for a wide variety of applications. The authors recently proposed one such novel and effective metamodeling approach—the extended radial basis function (E-RBF) approach—and reported highly promising results. To further understand the advantages and limitations of this new approach, we compare its performance to that of the typical RBF approach, and another closely related method—kriging. Several test functions with varying problem dimensions and degrees of nonlinearity are used to compare the accuracies of the metamodels using these metamodeling approaches. We consider several performance criteria such as metamodel accuracy, effect of sampling technique, effect of sample size, effect of problem dimension, and computational complexity. The results suggest that the E-RBF approach is a potentially powerful metamodeling approach for MDO-based applications, as well as other classes of computationally intensive applications.     

Stable computation of Gaussian interpolants—a heuristic approach

Approximations play an important role in optimization to reduce the number of expensive analysis runs. In particular, metamodels are one way to reduce CPU time for multidisciplinary optimization and are sometimes the enabler for huge optimization projects. One type of approximation is interpolation and radial basis functions (RBFs) are an example for it. In general, a large number of radial basis functions have the attractive property that—independently from the arrangement of sampling points—the existence of a unique solution can be guaranteed for the linear equation system that is solved to determine the coefficients. However, this doesn’t mean that the handling is uncritical, as can be seen for radial basis functions of Gaussian type (GRBFs). In this case ill-conditioning and Runge-type oscillations spoil the tuning of the interpolant’s shape parameter and make its general application as a metamodel impossible. We introduce a heuristic approach to modify the GRBFs in a way that allows the shape parameter to be optimized within a much larger range before ill-conditioning appears. It seems that also the appearance of the Runge-type oscillations are solved by this approach.     

Correctly defined concrete syntax

Due to their complexity, the syntax of modern modeling languages is preferably defined in two steps. The abstract syntax identifies all modeling concepts whereas the concrete syntax should clarify how these concepts are rendered by graphical and/or textual elements. While the abstract syntax is often defined in form of a metamodel, there does not exist such standard format yet for concrete syntax definitions. The diversity of definition formats—ranging from EBNF grammars to informal text—is becoming a major obstacle for advances in modeling language engineering, including the automatic generation of editors. In this paper, we propose a uniform format for concrete syntax definitions. Our approach captures both textual and graphical model representations and even allows to assign more than one rendering to the same modeling concept. Consequently, following our approach, a model can have multiple, fully equivalent representations, but—in order to avoid ambiguities when reading a model representation—two different models should always have distinguishable representations. We call a syntax definition correct, if all well-formed models are represented in a non-ambiguous way. As the main contribution of this paper, we present a rigorous analysis technique to check the correctness of concrete syntax definitions.

Design principles for autonomous agents: A case study of classification

Building artificial lifelike autonomous agents is still considered an art, rather than a science. A generally accepted precise methodology is missing, and—given the properties of the real world—it is doubtful whether such a methodology will ever be developed. Nevertheless, it is possible to define criteria and provide heuristics for good designs. We have developed a number of design principles which, when applied, lead to what we would consider good designs from a cognitive science or artificial life (ALife) perspective. The paper illustrates some of these principles using a case study of classification. Presented at the Internatial Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

Survey of modern technologies of simulation-based verification of hardware

Comparative analysis of modern approaches to simulation-based verification (testing) of hardware models—AVM (advanced verification methodology) developed by the Mentor Graphics Corporation, OVM (open verification methodology) developed jointly by Mentor Graphics and Cadence Design Systems, and the UniTESK (unified testing and specification tool kit) technology developed at the Institute for System Programming of Russian Academy of Sciences—is given. In the paper, advantages and disadvantages of these approaches are analyzed, and architectures of the test systems are compared. Recommendations on the development of the UniTESK technology and its unification with the OVM methodology, which is currently widely used and pretends to be a standard in the field of hardware verification, are given.     

Space partitioning in engineering design via metamodel acceptance score distribution

Metamodels for simulators are used to reduce computational costs in engineering system design. In general, metamodels have different fit accuracy levels over different regions in the design variables space, especially for highly nonlinear responses over wide spaces. A metamodeling strategy should place less emphasis on sub regions in the design variables space requiring relatively less complex metamodels. In this paper, we present a graphically based methodology that can be used to partition the space for piecewise metamodel building. The method is based on analyzing an initial global metamodel for acceptability in terms of prediction accuracy over the whole space; then generating acceptance score distribution (ASD) plots. Visual inspection of the ASD plots is used as a guidance to partition the design variables space, leading to a metamodel with fair prediction accuracy on a piece-by-piece basis, not just globally on average. The proposed methodology is general and can be applied to various metamodel types. It is tested on a number of problems, including some of the most highly nonlinear test problems used in the literature.

A transformation model from DEVS to SMP2 based on MDA

Simulation Model Portability standard 2 (SMP2) is a successful simulation model reuse standard in European Space Agency. Discrete Event System Specification (DEVS) is one of most common and powerful simulation model formalisms. Transforming a simulation model from DEVS representation to SMP2 representation is of great significance for model reuse. A Model Driven Architecture (MDA) based transformation methodology is presented first. According to this methodology, a MOF based DEVS metamodel is created and the mappings from DEVS metamodel to SMP2 metamodel are established. A transformation model from DEVS to SMP2 is described based on Query/View/Transformation (QVT) language. An illustrative example is detailed to demonstrate the application of the transformation model. The transformation model’s further application strategy and our next work are discussed in the final part.     

A technique for defining agent-oriented engineering processes with tool support

The formalization of engineering processes is necessary for guaranteeing the quality and cost of the products involved. Agent-oriented engineering has already proposed development processes that still need to be further formalized to be applicable by non-researchers. This paper proposes a technique to instantiate processes for specific agent-oriented methodologies. This technique is based on three orthogonal views that are respectively related with lifecycles, disciplines and guidances. In addition, processes are modeled with a tool, which is automatically generated from a process metamodel inspired by the software & systems process engineering metamodel. Accordingly, engineers can choose the methodology–process pair best-suited for the characteristics of their project. The paper illustrates the approach based on the unified development process and the scrum process for the INGENIAS methodology and compares the results with other existing alternatives.     

A graph grammar-based formal validation of object-process diagrams

Two basic requirements from a system’s conceptual model are correctness and comprehensibility. Most modeling methodologies satisfy only one of these apparently contradicting requirements, usually comprehensibility, leaving aside problems of correctness and ambiguousness that are associated with expressiveness. Some formal modeling languages do exist, but in these languages a complete model of a complex system is fairly complicated to understand. Object-process methodology (OPM) is a holistic systems modeling methodology that combines the two major aspects of a system—structure and behavior—in one model, providing mechanisms to manage the complexity of the model using refinement-abstraction operations, which divide a complex system into many interconnected diagrams. Although the basic syntax and semantics of an OPM model are defined, they are incomplete and leave room for incorrect or ambiguous models. This work advances the formal definition of OPM by providing a graph grammar for creating and checking OPM diagrams. The grammar provides a validation methodology of the semantic and syntactic correctness of a single object-process diagram.     

Introducing contextual laddering to evaluate the likeability of games with children

It becomes more and more recognized that children should be involved in a product’s design and evaluation process. Many findings report on the methodology for usability research with children. However, there has been relatively little analysis of likeability research with children. In this paper, we propose the laddering method—traditionally a marketing method among adults—for likeability research in the domain of child–computer interaction. Three exploratory cases will be described. The cases report on the use of the laddering method with children aged between 7 and 16 to evaluate the likeability of two games. The lessons learnt about the use of the laddering method will be discussed in great detail. In order to adapt the laddering method to work with children, we recommend a variation of this method and call it the ‘contextual laddering method’.     

基于元建模的移动Ad Hoc网络性能优化

针对移动Ad Hoc网络性能优化过程中的仿真模型精确性与仿真模型运行效率之间的矛盾,提出了利用相关向量元模型来拟合网络仿真模型并进行优化的解决方法.重点研究了元建模方法中的实验设计方法、元模型拟合方法、模型验证与评估等关键技术.利用适度精确的元模型替代仿真模型进行设计空间探索和多目标优化,实验结果表明,基于元模型的优化可以成功应用于移动Ad Hoc网络等复杂系统建模、分析与优化,有效提高此类计算密集过程的计算效率.     

Nearly-orthogonal sampling and neural network metamodel driven conceptual design of multistage space launch vehicle

This paper presents a new design methodology for efficient conceptual design of complex systems involving multidisciplinary and computationally intensive analysis with large number of design variables. A nearly-orthogonal sampling of design space is proposed with good space filling properties to extract maximum useful information about system behavior using much lower number of trial designs. This sampled data is then used as training data for artificial neural network, which will act as a metamodel to represent the time consuming disciplinary analyses. A stage-wise interconnection of separate neural networks is also proposed for trajectory metamodel to offset dimensionality curse of neural networks. Genetic Algorithm performs global optimization by utilizing this metamodel and subsequently sequential quadratic programming performs the local optimization utilizing exact analyses. The performance of proposed methodology is investigated in this paper for the conceptual design optimization of multistage solid fueled space launch vehicle. The results show excellent approximation of highly non-linear functions using proposed sampling and drastic reduction in overall design optimization time, due to greatly reduced number of exact disciplinary analyses.     

A multi-surrogate approximation method for metamodeling

Metamodeling methods have been widely used in engineering applications to create surrogate models for complex systems. In the past, the input–output relationship of the complex system is usually approximated globally using only a single metamodel. In this research, a new metamodeling method, namely multi-surrogate approximation (MSA) metamodeling method, is developed using multiple metamodels when the sample data collected from different regions of the design space are of different characteristics. In this method, sample data are first classified into clusters based on their similarities in the design space, and a local metamodel is identified for each cluster of the sample data. A global metamodel is then built using these local metamodels considering the contributions of these local metamodels in different regions of the design space. Compared with the traditional approach of global metamodeling using only a single metamodel, this MSA metamodeling method can improve the modeling accuracy considerably. Applications of this metamodeling method have also been demonstrated in this research.     

Metamodeling live sequence charts for code generation

This article presents a metamodeling study for Live Sequence Charts (LSCs) and Message Sequence Charts (MSCs) with an emphasis on code generation. The article discusses specifically the following points: the approach to building a metamodel for MSCs and LSCs, a metamodel extension from MSC to LSC, support for model-based code generation, and finally action model and domain-specific data model integration. The metamodel is formulated in metaGME, the metamodel language for the Generic Modeling Environment.

A comparison of four process metamodels and the creation of a new generic standard

Software development processes and methodologies to date have frequently been described purely textually. However, more recently, a number of metamodels have been constructed to both underpin and begin to formalize these methodologies. We have critically examined four of these: the Object Management Group's Software Process Engineering Metamodel (SPEM), the OPEN Process Framework (OPF), the OOSPICE metamodel for capability assessment and the LiveNet approach for computer-supported collaborative work (CSCW). Based on this analysis, a new, combined metamodel, named Standard Metamodel for Software Development Methodologies (SMSDM) has been constructed which supports not only process but also products and capability assessment in the contexts of both software development and CSCW. As a proof of concept we conclude with a partial example to show how the SMSDM metamodel (and by inference the other metamodels) are used in practice by creating a simple yet usable methodology.     

A security-aware metamodel for multi-agent systems (MAS)

This paper adopts a model-based security (MBS) approach to identify security requirements during the early stages of multi-agent system development. Our adopted MBS approach is underpinned by a metamodel independent of any specific methodology. It allows for security considerations to be embedded within any situated agent methodology which then prescribes security considerations within its work products. Using a standard model-driven engineering (MDE) approach, these work products are initially constructed as high abstraction models and then transformed into more precise models until code-specific models can be produced. A multi-agent system case study is used to illustrate the applicability of the proposed security-aware metamodel.     

Visual analysis of image collections

Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image—Projection Explorer for Images—a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visual exploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.     

Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models

Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the posterior expected utility. Experimental confirmation of the models, however, has been claimed because of groups of agents that “probability match” the posterior. Probability matching only constitutes support for the Bayesian claim if additional unobvious and untested (but testable) assumptions are invoked. The alternative strategy of weakening the underlying notion of rationality no longer distinguishes the Bayesian model uniquely. A new account of rationality—either for inference or for decision-making—is required to successfully confirm Bayesian models in cognitive science.