Multi-perspective enterprise modeling: foundational concepts,prospects and future research challenges

The paper presents a method for multi- perspective enterprise modeling (MEMO) and a corresponding (meta-) modeling environment. An extensive analysis of requirements for enterprise modeling serves to motivate and assess the method. The method is based on an elaborate conception of multi-perspective enterprise models and on an extensible language architecture. The language architecture is comprised of a meta modeling language and an extensible set of integrated domain-specific modeling languages (DSML). The DSML are supplemented with process models and with guidelines for their reflective use. The corresponding modeling environment integrates editors for various DSML into multi-language model editors. It includes a meta model editor which enables the convenient use, development and extension of the set of supported DSML and supports the generation of respective graphical model editors. Thus, it also serves as a foundation for method engineering. MEMO covers both software engineering as well as social, managerial and economic aspects of the firm. The presentation of MEMO is supplemented with a comparative overview of other approaches to enterprise modeling. The paper concludes bys summarizing fundamental technical, epistemological and political challenges for enterprise modeling research and discusses potential paths for future research.     

Automating the construction of domain-specific modeling languages for object-oriented frameworks

The extension of frameworks with domain-specific modeling languages (DSML) has proved to be an effective way of improving the productivity in software product-line engineering. However, developing and evolving a DSML is typically a difficult and time-consuming task because it requires to develop and maintain a code generator, which transforms application models into framework-based code. In this paper, we propose a new approach for extending object-oriented frameworks that aims to alleviate this problem. The approach is based on developing an additional aspect-oriented layer that encodes a DSML for building framework-based applications, eliminating the need of implementing a code generator. We further show how a language workbench is capable of automating the construction of DSMLs using the proposed layer.     

On the modeling and generation of service-oriented tool chains

Tool chains have grown from ad-hoc solutions to complex software systems, which often have a service-oriented architecture. With service-oriented tool integration, development tools are made available as services, which can be orchestrated to form tool chains. Due to the increasing sophistication and size of tool chains, there is a need for a systematic development approach for service-oriented tool chains. We propose a domain-specific modeling language (DSML) that allows us to describe the tool chain on an appropriate level of abstraction. We present how this language supports three activities when developing service-oriented tool chains: communication, design and realization. A generative approach supports the realization of the tool chain using the service component architecture. We present experiences from an industrial case study, which applies the DSML to support the creation of a service-oriented tool chain. We evaluate the approach both qualitatively and quantitatively by comparing it with a traditional development approach.     

Composing domain-specific physical models with general-purpose software modules in embedded control software

A considerable portion of software systems today are adopted in the embedded control domain. Embedded control software deals with controlling a physical system, and as such models of physical characteristics become part of the embedded control software. In current practices, usually general-purpose languages (GPL), such as C/C++ are used for embedded systems development. Although a GPL is suitable for expressing general-purpose computation, it falls short in expressing the models of physical characteristics as desired. This reduces not only the readability of the code but also hampers reuse due to the lack of dedicated abstractions and composition operators. Moreover, domain-specific static and dynamic checks may not be applied effectively. There exist domain-specific modeling languages (DSML) and tools to specify models of physical characteristics. Although they are commonly used for simulation and documentation of physical systems, they are often not used to implement embedded control software. This is due to the fact that these DSMLs are not suitable to express the general-purpose computation and they cannot be easily composed with other software modules that are implemented in GPL. This paper presents a novel approach to combine a DSML to model physical characteristics and a GPL to implement general-purpose computation. The composition filters model is used to compose models specified in the DSML with modules specified in the GPL at the abstraction level of both languages. As such, this approach combines the benefits of using a DSML to model physical characteristics with the freedom of a GPL to implement general-purpose computation. The approach is illustrated using two industrial case studies from the printing systems domain.     

Weaving variability into domain metamodels

Domain-specific modeling languages (DSMLs) are the essence of MDE. A DSML describes the concepts of a particular domain in a metamodel, as well as their relationships. Using a DSML, it is possible to describe a wide range of different models that often share a common base and vary on some parts. On the one hand, some current approaches tend to distinguish the variability language from the DSMLs themselves, implying greater learning curve for DSMLs stakeholders and a significant overhead in product line engineering. On the other hand, approaches integrating variability in DSMLs lack generality and tool support. We argue that aspect-oriented modeling techniques enabling flexible metamodel composition and results obtained by the software product line community to manage and resolve variability form the pillars for a solution for integrating variability into DSMLs. In this article, we consider variability as an independent and generic aspect to be woven into the DSML. In particular, we detail how variability is woven and how to perform product line derivation. We validate our approach through the weaving of variability into two different metamodels: Ecore??widely used for DSML definition??and SmartAdapters, our aspect model weaver. These results emphasize how new abilities of the language can be provided by this means.     

Proactive modeling: a new model intelligence technique

This article discusses a model intelligence technique called proactive modeling. The goal of proactive modeling is to reduce the amount of manual modeling required when using a graphical DSML and to assist in step-by-step creation of a model. Proactive modeling accomplishes this goal by examining the metamodels syntax and constraints, automatically executing model modifications, and prompting the modeler for assistance when more than one valid model modification exists, but none are necessary. We have integrated proactive modeling into the generic modeling environment (GME) as a generic add-on that can operate on any domain-specific modeling language implemented in GME. Lastly, results from applying proactive modeling to several DSMLs in GME show that it can reduce modeling effort.     

Debugging measurement systems using a domain-specific modeling language

Capturing physical data in the context of measurement systems is a demanding process that often requires many repetitions with different settings. To assist in this activity, a domain-specific modeling language (DSML) called Sequencer has been developed to enable the improved definition of measurement procedures. With Sequencer, the level of abstraction has been raised and sophisticated changes in measurement procedures are now enabled. Although there are numerous DSMLs like Sequencer in the existing literature, there are some obstacles working against the more widespread adoption of DSMLs in practice. One challenge is the lack of supporting tools for DSMLs, which would improve the capabilities of end-users of such languages. For instance, support for debugging a model expressed in a DSML is often neglected. The lack of a debugger at the proper abstraction level limits the domain experts in discovering and locating bugs in a model. In this paper, Sequencer is presented together with debugging facilities (called Ladybird) that are integrated in a modeling environment. Ladybird supports different execution modes (e.g., steps, breakpoints, animations, variable views, and stack traces) that can be helpful during the debugging of a model. Ladybird's primary contribution is in showing the value of error detection in complicated industrial environments, such as data acquisition in automotive testing. The paper contributes to a discussion of the implementation details of DSML debugging facilities and how such a debugger can be reused to support domains other than the measurement context of Sequencer.     

Model-driven generative development of measurement software

Metrics offer a practical approach to evaluate properties of domain-specific models. However, it is costly to develop and maintain measurement software for each domain-specific modeling language. In this paper, we present a model-driven and generative approach to measuring models. The approach is completely domain-independent and operationalized through a prototype that synthesizes a measurement infrastructure for a domain-specific modeling language. This model-driven measurement approach is model-driven from two viewpoints: (1) it measures models of a domain-specific modeling language; (2) it uses models as unique and consistent metric specifications, with respect to a metric specification metamodel which captures all the necessary concepts for model-driven specifications of metrics. The benefit from applying the approach is evaluated by four case studies. They indicate that this approach significantly eases the measurement activities of model-driven development processes.     

Synthesizing interpreted domain-specific models to manage smart microgrids

The increase in prominence of model-driven software development (MDSD) has placed emphasis on the use of domain-specific modeling languages (DSMLs) during the development process. DSMLs allow for domain concepts to be conceptualized and represented at a high level of abstraction. Currently, most DSML models are converted into high-level languages (HLLs) through a series of model-to-model and/or model-to-text transformations before they are executed. An alternative approach for model execution is the interpretation of models directly without converting them into an HLL. These models are created using interpreted DSMLs (i-DSMLs) and realized using a semantic-rich execution engine or domain-specific virtual machine (DSVM).In this article we present an approach for model synthesis, the first stage of model interpretation, that separates the domain-specific knowledge (DSK) from the model of execution (MoE). Previous work on model synthesis tightly couples the DSK and MoE reducing the ability for implementations of the DSVM to be easily reused in other domains. To illustrate how our approach to model synthesis works for i-DSMLs, we have created MGridML, an i-DSML for energy management in smart microgrids, and an MGridVM prototype, the DSVM for MGridML. We evaluated our approach by performing experiments on the model synthesis aspect of MGridVM and comparing the results to a DSVM from the user-centric communication domain.     

DSML4CP: A Domain-specific Modeling Language for Concurrent Programming

Nowadays, concurrent programs are an inevitable part of many software applications. They can increase the computation performance of the applications by parallelizing their computations. One of the approaches to realize the concurrency is using multi thread programming. However, these systems are structurally complex considering the control of the parallelism (such as thread synchronization and resource control) and also considering the interaction between their components. So, the design of these systems can be difficult and their implementation can be error-prone especially when the addressed system is big and complex. On the other hand, a Domain-specific Modeling Language (DSML) is one of the Model Driven Development (MDD) approaches which tackles this problem. Since DSMLs provide a higher abstraction level, they can lead to reduce the complexities of the concurrent programs. With increasing the abstraction level and generating the artifacts automatically, the performance of developing the software (both in design and implementation phases) is increased, and the efficiency is raised by reducing the probability of occurring errors. Thus, in this paper, a DSML is proposed for concurrent programs, called DSML4CP, to work in a higher level of abstraction than code level. To this end, the concepts of concurrent programs and their relationships are presented in a metamodel. The proposed metamodel provides a context for defining abstract syntax, and concrete syntax of the DSML4CP. This new language is supported by a graphical modeling tool which can visualize different instance models for domain problems. In order to clarify the expressions of the language; the static semantic controls are realized in the form of constraints. Finally, the architectural code generation is fulfilled via model transformation rules using the templates of the concurrent programs. To increase level of the DSML׳s leverage and to demonstrate the general support of concurrent programming by the DSML, the transformation mechanism of the tool supports two well-known and highly used programming languages for code generation; Java and C#. The performed experiments on two case studies indicate a high performance for proposed language. In this regard, the results show automatic generation of 79% of the final code and 86% of the functions/modules on average.     

A systematic approach to evaluating domain-specific modeling language environments for multi-agent systems

Multi-agent systems (MASs) include multiple interacting agents within an environment to provide a solution for complex systems that cannot be easily solved with individual agents or monolithic systems. However, the development of MASs is not trivial due to the various agent properties such as autonomy, responsiveness, and proactiveness, and the need for realization of the many different agent interactions. To support the development of MASs various domain-specific modeling languages (DSMLs) have been introduced that provide a declarative approach for modeling and supporting the generation of agent-based systems. To be effective, the proposed DSMLs need to meet the various stakeholder concerns and the related quality criteria for the corresponding MASs. Unfortunately, very often the evaluation of the DSML is completely missing or has been carried out in idiosyncratic approach. If the DSMLs are not well defined, then implicitly this will have an impact on the quality of the MASs. In this paper, we present an evaluation framework and systematic approach for assessing existing or newly defined DSMLs for MASs. The evaluation is specific for MAS DSMLs and targets both the language and the corresponding tools. To illustrate the evaluation approach, we first present SEA_ML, which is a model-driven MAS DSML for supporting the modeling and generation of agent-based systems. The evaluation of SEA_ML is based on a multi-case study research approach and provides both qualitative evaluation and quantitative analysis. We report on the lessons learned considering the adoption of the evaluation approach as well as the SEA_ML for supporting the generation of agent-based systems.     

Formalizing the structural semantics of domain-specific modeling languages

Model-based approaches to system design are now widespread and successful. These approaches make extensive use of model structure to describe systems using domain-specific abstractions, to specify and implement model transformations, and to analyze structural properties of models. In spite of its general importance the structural semantics of modeling languages are not well-understood. In this paper we develop the formal foundations for the structural semantics of domain-specific modeling languages (DSML), including the mechanisms by which metamodels specify the structural semantics of DSMLs. Additionally, we show how our formalization can complement existing tools, and how it yields algorithms for the analysis of DSMLs and model transformations.

DSMDiff: a differentiation tool for domain-specific models

Model differentiation techniques, which provide the capability to identify mappings and differences between models, are essential to many model development and management practices. There has been initial research toward model differentiation applied to Unified Modeling Language (UML) diagrams, but differentiation of domain-specific models has not been explored deeply in the modeling community. Traditional modeling practice using the UML relies on a single fixed general-purpose language (i.e., all UML diagrams conform to a single metamodel). In contrast, Domain-Specific Modeling (DSM) is an emerging model-driven paradigm in which multiple metamodels are used to define various modeling languages that represent the key concepts and abstractions for particular domains. Therefore, domain-specific models may conform to various metamodels, which requires model differentiation algorithms be metamodel-independent and able to apply to multiple domain-specific modeling languages. This paper presents metamodel-independent algorithms and associated tools for detecting mappings and differences between domain-specific models, with facilities for graphical visualization of the detected differences.     

Composing domain-specific design environments

Domain-specific integrated development environments can help capture specifications in the form of domain models. These tools support the design process by automating analysis and simulating essential system behavior. In addition, they can automatically generate, configure, and integrate target application components. The high cost of developing domain-specific, integrated modeling, analysis, and application-generation environments prevents their penetration into narrower engineering fields that have limited user bases. Model-integrated computing (MIC), an approach to model-based engineering that helps compose domain-specific design environments rapidly and cost effectively, is particularly relevant for specialized computer-based systems domains-perhaps even single projects. The authors describe how MIC provides a way to compose such environments cost effectively and rapidly by using a metalevel architecture to specify the domain-specific modeling language and integrity constraints. They also discuss the toolset that implements MIC and describe a practical application in which using the technology in a tool environment for the process industry led to significant reductions in development and maintenance costs     

Reusable model transformations

Model transformations written for an input metamodel may often apply to other metamodels that share similar concepts. For example, a transformation written to refactor Java models can be applicable to refactoring UML class diagrams as both languages share concepts such as classes, methods, attributes, and inheritance. Deriving motivation from this example, we present an approach to make model transformations reusable such that they function correctly across several similar metamodels. Our approach relies on these principal steps: (1) We analyze a transformation to obtain an effective subset of used concepts. We prune the input metamodel of the transformation to obtain an effective input metamodel containing the effective subset. The effective input metamodel represents the true input domain of transformation. (2) We adapt a target input metamodel by weaving it with aspects such as properties derived from the effective input metamodel. This adaptation makes the target metamodel a subtype of the effective input metamodel. The subtype property ensures that the transformation can process models conforming to the target input metamodel without any change in the transformation itself. We validate our approach by adapting well known refactoring transformations (Encapsulate Field, Move Method, and Pull Up Method) written for an in-house domain-specific modeling language (DSML) to three different industry standard metamodels (Java, MOF, and UML).     

Interpretable program specification language

In the paper, a domain-specific language of executable specifications is proposed. This language makes it possible to describe models of formalized subject domains in a graphical form, formulate computational problems on these models, and synthesize programs for solving these problems (including parallel ones) based on deductive inference in a special class of proposition calculus.     

Search-based metamodel matching with structural and syntactic measures

The use of different domain-specific modeling languages and diverse versions of the same modeling language often entails the need to translate models between the different languages and language versions. The first step in establishing a transformation between two languages is to find their corresponding concepts, i.e., finding correspondences between their metamodel elements. Although, metamodels use heterogeneous terminologies and structures, they often still describe similar language concepts. In this paper, we propose to combine structural metrics (e.g., number of properties per concept) and syntactic metrics to generate correspondences between metamodels. Because metamodel matching requires to cope with a huge search space of possible element combinations, we adapted a local and a global metaheuristic search algorithm to find the best set of correspondences between metamodels. The efficiency and effectiveness of our proposal is evaluated on different matching scenarios based on existing benchmarks. In addition, we compared our technique to state-of-the-art ontology matching and model matching approaches.