An Open Visualization Framework for Metamodel-Based Modeling Languages

In the paper, we propose an automated, SVG-based visualization framework for modeling languages defined by metamodeling techniques. Our framework combines XML standards with existing graph transformation and graph drawing technologies in order to provide an open, tool-independent architecture.     

Towards Symbolic Analysis of Visual Modeling Languages

Graph transformation has recently become more and more popular as a general, rule-based visual specification paradigm to formally capture the operational semantics of modeling languages based on metamodeling techniques as demonstrated by benchmark applications focusing on the formal treatment of the Unified Modeling Language (UML). In the paper, we enable model checking-based symbolic verification for such modeling languages by providing a meta-level transformation of well-formed model instances into SAL specifications [4]. We also discuss several optimizations in the translation process that makes our approach efficient and independent of the SAL framework.     

Metamodels for variable importance decomposition with applications to probabilistic engineering design

It is routine in probabilistic engineering design to conduct modeling studies to determine the influence of an input variable (or a combination) on the output variable(s). The output or the response can then be fine-tuned by changing the design parameters based on this information. However, simply fine-tuning the output to the desired or target value is not adequate. Robust design principles suggest that we not only study the mean response for a given input vector but also the variance in the output attributed to noise and other unaccounted factors. Given our desire to reduce variability in any process, it is also important to understand which of the input factors affect the variability in the output the most. Given the significant computational overhead associated with most Computer Aided Engineering models, it is becoming popular to conduct such analysis through surrogate models built using a variety of metamodeling techniques. In this regard, existing literature on metamodeling and sensitivity analysis techniques provides useful insights into the various scenarios that they suit the best. However, there has been a limitation of studies that simultaneously consider the combination of metamodeling and sensitivity analysis and the environments in which they operate the best. This paper aims at contributing to reduce this limitation by basing the study on multiple metrics and using two test problems. Two test functions have been used to build metamodels, using three popular metamodeling techniques: Kriging, Radial-Basis Function (RBF) networks, and Support Vector Machines (SVMs). The metamodels are then used for sensitivity analysis, using two popular sensitivity analysis methods, Fourier Amplitude Sensitivity Test (FAST) and Sobol, to determine the influence of variance in the input variables on the variance of the output variables. The advantages and disadvantages of the different metamodeling techniques, in combination with the sensitivity analysis methods, in determining the extent to which the variabilities in the input affect the variabilities in the output are analyzed.     

A model-driven approach for the derivation of architectural requirements of software product lines

The alignment of the software architecture and the functional requirements of a system is a demanding task because of the difficulty in tracing design elements to requirements. The four-step rule set (4SRS) is a unified modeling language (UML)-based model-driven method for single system development which provides support to the software architect in this task. This paper presents an evolution of the 4SRS method aimed at software product lines. In particular, we describe how to address the transformation of functional requirements (use cases) into component-based requirements for the product line architecture. The result is a UML-based model-driven method that can be applied in combination with metamodeling tools such as the eclipse modeling framework (EMF) to derive the architecture of software product lines. We present our approach in a practical way and illustrate it with an example. We also discuss how our proposals are related to the work of other authors.     

Concurrent treatment of parametric uncertainty and metamodeling uncertainty in robust design

Robust design is an effective approach to design under uncertainty. Many works exist on mitigating the influence of parametric uncertainty associated with design or noise variables. However, simulation models are often computationally expensive and need to be replaced by metamodels created using limited samples. This introduces the so-called metamodeling uncertainty. Previous metamodel-based robust designs often treat a metamodel as the real model and ignore the influence of metamodeling uncertainty. In this study, we introduce a new uncertainty quantification method to evaluate the compound effect of both parametric uncertainty and metamodeling uncertainty. Then the new uncertainty quantification method is used for robust design. Simplified expressions of the response mean and variance is derived for a Kriging metamodel. Furthermore, the concept of robust design is extended for metamodel-based robust design accounting for both sources of uncertainty. To validate the benefits of our method, two mathematical examples without constraints are first illustrated. Results show that a robust design solution can be misleading without considering the metamodeling uncertainty. The proposed uncertainty quantification method for robust design is shown to be effective in mitigating the effect of metamodeling uncertainty, and the obtained solution is found to be more “robust” compared to the conventional approach. An automotive crashworthiness example, a highly expensive and non-linear problem, is used to illustrate the benefits of considering both sources of uncertainty in robust design with constraints. Results indicate that the proposed method can reduce the risk of constraint violation due to metamodel uncertainty and results in a “safer” robust solution.     

A metamodeling language supporting subset and union properties

In this article, we describe successive versions of a metamodeling language using a set-theoretic formalization. We focus on language extension mechanisms, particularly on the relatively new subset and union properties of MOF 2.0 and the UML 2.0 Infrastructure. We use Liskov substitutability as the rationale for our formalization. We also show that property redefinitions are not a safe language extension mechanism. Each language version provides new features, and we note how such features cannot be mixed arbitrarily. Instead, constraints over the metamodel and model structures must be established. We expect that this article provides a better understanding of the foundations of MOF 2.0, which is necessary to define new extensions, model transformation languages and tools.     

Concept and pragmatics of an intuitive visualization-oriented metamodeling tool

In this article we present a metamodeling tool that is strictly oriented towards the needs of the working domain expert. The working domain expert looks for intuitive metamodeling features. In particular, these features include rich capabilities for specifying the visual appearance of models. Our research has identified an important design rationale for metamodeling tools that we call visual reification, which is the notion that metamodels are visualized the same way as their instances. Our tool supports both standard and innovative metamodeling features oriented towards the principle of visual reification. In this paper we present an unbiased discussion of the pragmatics of metamodeling tools against the background of this design rationale.     

An MDE-based method for bridging different design notations

Different communities have developed plenty of design notations for software engineering in support of practical (via UML) and rigorous (via formal methods) approaches. Hence the problem of bridging these notations rises. Model-driven engineering (MDE) is a new paradigm in software engineering, which treats models and model transformations as first class citizens. Furthermore, it is seen as a promising method for bridging heterogeneous platforms. In this paper, we provide an MDE-based approach to build bridges between informal, semi-formal and formal notations: Firstly, different notations are viewed as different domain specification languages (DSLs) and introduced into MDE, especially into the ATLAS Model Management Architecture (AMMA) platform, by metamodeling. Then, ATL transformation rules are built for semantics mapping. At last, TCS-based model-to-text syntax rules are developed, allowing one to map models to programs. Consequently, different design notations in both graphical style and grammatical style are bridged. A case study of bridging OMG SysML™ to LOTOS is also illustrated showing the validity and practicability of our approach.     

Security patterns modeling and formalization for pattern-based development of secure software systems

Pattern-based development of software systems has gained more attention recently by addressing new challenges such as security and dependability. However, there are still gaps in existing modeling languages and/or formalisms dedicated to modeling design patterns and the way how to reuse them in the automation of software development. The solution envisaged here is based on combining metamodeling techniques and formal methods to represent security patterns at two levels of abstraction to fostering reuse. The goal of the paper is to advance the state of the art in model and pattern-based security for software and systems engineering in three relevant areas: (1) develop a modeling language to support the definition of security patterns using metamodeling techniques; (2) provide a formal representation and its associated validation mechanisms for the verification of security properties; and (3) derive a set of guidelines for the modeling of security patterns within the integration of these two kinds of representations.     

Analysis of gene expression programming for approximation in engineering design

To reduce the computational cost of implementing computer-based simulations and analyses in engineering design, a variety of metamodeling techniques have been developed and used for the construction of metamodels. Metamodels, also called approximation models and surrogate models, can be used to make a replacement of the expensive simulation codes for design and optimization. In this paper, gene expression programming (GEP) algorithm in the evolutionary computing area is investigated as an alternative metamodeling technique to provide the approximation of a design space. The approximation performance of GEP is tested on some low-dimensional mathematical and engineering problems. A comparative study is conducted on GEP and three common metamodeling techniques in engineering design (i.e., response surface methodology (RSM), kriging and radial basis functions (RBF)) for the approximation of the low-dimensional design space. Multiple evaluation criteria are considered in the comparison: accuracy, robustness, transparency and efficiency. Two different sample sizes are adopted: small and large. Comparative results indicate that GEP can achieve the most accurate and robust approximation of a low-dimensional design space for small sample sets. For large sample sets, GEP also presents good prediction accuracy and high robustness. Moreover, the transparency of GEP is the best since it can provide clear function relationships and factor contributions by means of compact expressions. As a novel metamodeling technique, GEP shows great promise for metamodeling applications in a low-dimensional design space, especially when only a few sample points are selected and used for training.     

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.     

Time-based metamodeling technique for vehicle crashworthiness optimization

In automotive industry, structural optimization for crashworthiness criteria is of special importance in the early design stage. To reduce the vehicle design cycle, metamodeling techniques have become so widespread... In this study, a time-based metamodeling technique is proposed for the vehicle design. The characteristics of the proposed method are the construction of a time-based objective function and establishment of a metamodel by support vector regression (SVR). Compared with other popular metamodel-based optimization methods, the design space of the proposed method is expanded to time domain. Thus, more information and features can be extracted in the expanded time domain. To validate the performance of the time-based metamodeling technique, cylinder impacting and full vehicle frontal collision are optimized by the proposed method. The results demonstrate that the proposed method has potential capability to solve the crashworthiness vehicle design.     

Visualizing program dependencies: An experimental study

This paper addresses the problem of visualizing program dependencies (i.e. entities and their relations). A code visualization tool that maintains a repository of structural and functional dependencies for C programs is described. Visualization of such dependencies is accomplished by using a presentation model which combines data and control flow information. Moreover, transformation mechanisms and partitioning techniques used by the tool provide the means for managing large graphical representations. The quantitative results from an experimental study using this tool indicate that the productivity of its users was increased and that the quality of changes made during a program modification exercise was improved. Furthermore, the qualitative results have shown that its presentation model, transformation mechanisms and partitioning techniques constitute a promising platform for the comprehension and maintenance of C programs. Finally, the outcome of an empirical evaluation of the tool and the enhancement of its functionality and user interface are also discussed in this paper.     

Metamodeling for high dimensional design problems by multi-fidelity simulations

Multi-fidelity metamodeling provides an efficient way to approximate expensive black-box problems by utilizing the samples of multiple fidelities. While it still faces the challenge of “curse-of-dimensionality” when used in approximating high dimensional problems. On the other hand, the high dimensional model representation (HDMR) method, as an efficient tool to tackle high dimensional problems, can only handle single-fidelity samples in approximation. Therefore, a hybrid metamodel which combines Cut-HDMR with Co-kriging and kriging is proposed to improve the metamodeling efficiency for high dimensional problems. The developed HDMR, termed as MF-HDMR, can efficiently use multi-fidelity samples to approximate black-box problems by using a two stage metamodeling strategy. It can naturally explore and exploit the linearity/nonlinearity and correlations among variables of underlying problems, which are unknown or computationally expensive. Besides, to further improve the efficiency of MF-HDMR, an extended maximin distance sequential sampling method is proposed to add new sample points of different fidelities in the metamodeling process. Moreover, a mathematical function is used to illustrate the modeling theory and procedures of MF-HDMR. In order to validate the proposed method, it is tested by several numerical benchmark problems and successfully applied in the optimal design of a long cylinder pressure vessel. Moreover, an overall comparison between the proposed method and several other metamodeling methods has been made. Results show that the proposed method is very efficient in approximating high dimensional problems by using multi-fidelity samples, thus making it particularly suitable for high dimensional engineering design problems involving computationally expensive simulations.     

元建模技术研究进展

随着UML(unified modeling language)与MDA(model driven architecture)的兴起和流行,模型已经成为软件开发的核心制品,而模型重要性的提升使得建模语言以及定义建模语言的元模型逐渐成为软件开发中的一个核心要素.软件开发往往涉及多个领域,而不同的领域往往需要不同的建模语言及其建模工具.但是,手工地为不同的建模语言开发建模工具代价高昂.元建模技术是解决这个问题的方法之一,通过元建模,可以根据领域需要定制合适的元模型以定义领域建模语言,进而自动生成支持该建模语言的建模工具.大量的工程实践表明,与领域建模以及MDA相结合,元建模可以大幅度地提高软件开发效率,基于元建模的MDA比基于通用建模语言的MDA更具潜力.在最近的几年中,元建模及其相关技术发展迅猛,不但在技术上取得了长足的进步,而且在产业界也开始出现大规模的商业应用.总结了元建模的现有研究成果,分析和比较了现有元建模工具,探讨了元建模的可能发展方向.对元建模中存在的问题进行分析,并指出了可能的解决途径.     

Constructing meta-CASE workbenches by exploiting visual language generators

In this paper, we propose an approach for the construction of meta-CASE workbenches, which suitably integrates the technology of visual language generation systems, UML metamodeling, and interoperability techniques based on the GXL (graph exchange language) format. The proposed system consists of two major components. Environments for single visual languages are generated by using the modeling language environment generator (MEG), which follows a metamodel/grammar-approach. The abstract syntax of a visual language is defined by UML class diagrams, which serve as a base for the grammar specification of the language. The workbench generator (WoG) allows designers to specify the target workbench by means of a process model given in terms of a suitable activity diagram. Starting from the supplied specification WoG generates the customized workbench by integrating the required environments.     

Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach

This article investigates metamodeling opportunities in buffer allocation and performance modeling in asynchronous assembly systems (AAS). Practical challenges to properly design these complex systems are emphasized. A critical review of various approaches in modeling and evaluation of assembly systems reported in the recently published literature, with a special emphasis on the buffer allocation problems, is given. Various applications of artificial intelligence techniques on manufacturing systems problems, particularly those related to artificial neural networks, are also reviewed. Advantages and the drawbacks of the metamodeling approach are discussed. In this context, a metamodeling application on AAS buffer design/performance modeling problems in an attempt to extend the application domain of metamodeling approach to manufacturing/assembly systems is presented. An artificial neural network (ANN) metamodel is developed for a simulation model of an AAS. The ANN and regression metamodels for each AAS are compared with respect to their deviations from the simulation results. The analysis shows that the ANN metamodels can successfully be used to model of AASs. Consequently, one concludes that practising engineers involved in assembly system design can potentially benefit from the advantages of the metamodeling approach.     

基于GRoundTram的软件产品线设计模型的同步方法

领域设计模型与应用系统设计模型是软件产品线开发与定制阶段的重要产物。在产品线的生命周期中,为 了保证这两个模型之间的一致性,需要采用自动化或半自动化的手段实现模型之间的同步。针对该问题,提出了一种 基于GRoundTram的软件产品线设计模型的同步方法,称为SPLSync-GRoundTram。该方法将领域设计模型与应用 系统设计模型之间的同步问题转换为基于图的模型双向变换问题,并使用GRound"hram实现自动化的同步操作。 出了该模型同步方法的具体操作步骤,并通过一个“网上书城”的设计模型实例展示其有效性。     

Parallel boundary and best neighbor searching sampling algorithm for drawbead design optimization in sheet metal forming

In the present paper, a Kriging-based metamodeling technique is used to minimize the risk of failure in a sheet metal forming process. The Kriging-based models are fitted to data that are obtained for larger experimental areas than the areas used in low-order polynomial regression metamodels. Therefore, computational time and memory requirement can be an obstacle for Kriging for data sets with many observations. To improve the usability of the Kriging-based metamodeling techniques, a parallel intelligent sampling approach: boundary and best neighbor searching (BBNS) (Wang et al., J Mater Process Technol 197(1–3):77–88, 2008a) is suggested. Compared with the serial BBNS version, the sampling procedure is performed synchronously. Thus, larger sample size should be considered for real-life problems when multiple processors are available. Furthermore, the parallel strategy is prone to converge based on more samples. The performance of the parallel approached is verified by means of nonlinear test functions. Moreover, the drawbead design in sheet metal forming is successfully optimized by the parallel BBNS approach and Kriging metamodeling technique. The optimization results demonstrate that the parallel BBNS approach improves the applicability of the Kriging metamodeling technique substantially.