Towards Model Driven Architectures for Human Language Technologies

Model-Driven Development (MDD) is becoming increasingly popular as a choice for developing information systems. Tools that support the principles of MDD are also growing in number and variety of available functionality. MetaEdit+ is a meta-modeling tool used for developing Domain Specific Languages and is identified as an MDD tool. The Eclipse Modeling Framework (EMF) and Graphical Modeling Project (GMP) are two Eclipse projects that provide plug-ins to support the principles of MDD. In this paper, we report on our experience in using MetaEdit+ and the Eclipse plug-ins for developing a graphical editor for the unifying meta-model, which is an MDD approach that extends the traditional view of MDD to cover Enterprise Modeling. The two modeling environments are reviewed using quality areas that are identified by the research community as necessary in MDD tools. This report will provide useful insights for researchers and practitioners alike concerning the use of MetaEdit+ and the Eclipse plug-ins as MDD tools.

Model Driven Architectures and Model Driven Development (MDD) have been used in information system (IS) development projects for almost a decade. While the methodological support for the MDD process is important, the success of a project taking the model driven approach to development also heavily depends on the tool. The tool simply needs to support a set of key functionalities, such as an appropriate level of model abstraction, the refinement of models and finally the execution of models. In this paper we analyze a new MDD tool, namely Mendix, with respect to a number of functionality areas needed to achieve success in a project and capitalize on the benefits of MDD. Our findings are that Mendix use a well selected set of models and that these models are well integrated and suitable for the construction of small systems. Based on the key functionality areas we also point out the weaknesses of the tool.KeywordsModel Driven ArchitectureModel Driven DevelopmentMDDCASE tools

Content management systems (CMSs) such as Joomla and WordPress dominate today’s web. Enabled by standardized extensions, administrators can build powerful web applications for diverse customer demands. However, developing CMS extensions requires sophisticated technical knowledge, and the complex code structure of an extension gives rise to errors during typical development and migration scenarios. Model-driven development (MDD) seems to be a promising paradigm to address these challenges; however, it has not found adoption in the CMS domain yet. Systematic evidence of the benefit of applying MDD in this domain could facilitate its adoption; however, an empirical investigation of this benefit is currently lacking. In this paper, we present a mixed-method empirical investigation of applying MDD in the CMS domain, based on an interview suite, a controlled experiment, a field experiment, and case studies. During the experiments, we used JooMDD, an MDD infrastructure instantiation for CMS extensions. This infrastructure, which is also presented in this work, consists of a DSL with model editors, code generators, and reverse engineering facilities. We consider three scenarios of developing new (both independent and dependent) CMS extensions and of migrating existing ones to a new major platform version. The experienced developers in our interviews acknowledge the relevance of these scenarios and report on experiences that render them suitable candidates for a successful application of MDD. We found a particularly high relevance of the migration scenario. Our experiments largely confirm the potentials and limits of MDD as identified for other domains. In particular, we found a productivity increase up to factor 11.7 and a quality increase up to factor 2.4 during the development of CMS extensions. Furthermore, our observations highlight the importance of good tooling that seamlessly integrates with already used tool environments and processes.

Model Driven Development (MDD) requires proper tools to derive the implementation code from the application models. However, the integration of handwritten and generated code is a long-standing issue that affects the adoption of MDD in the industry. This article presents a model and code co-evolution approach that addresses such a problem a posteriori , using the standard collision detection capabilities of Version Control Systems to support the semi-automatic merge of the two types of code. We assess the proposed approach by contrasting it with the more traditional template-based, forward-engineering process, adopted by most MDD tools.

Enterprises today face many challenges related to lack of interoperability. Enterprise applications and software systems need to be interoperable in order to achieve seamless business across organisational boundaries and thus achieve virtual networked organisations. IEEE defines interoperability as “the ability of two or more systems or components to exchange information and to use the information that has been exchanged”. MDA is the OMG instantiation of an approach to software development kown as Model Driven Engineering (MDE) or Model Driven Development (MDD). MDD focuses on Models as the primary artefacts in the development process, with Transformations as the primary operation on models, used to map information from one model to another. There is presently an important paradigm shift in the field of software engineering that may have important consequences on the way information systems are built and maintained. Model-driven development (MDD), and in particular OMG's Model-Driven Architecture ® (MDA®), is emerging as the state of practice for developing modern enterprise applications and software systems. The MDD paradigm improves the way of addressing and solving interoperability issues compared to earlier non-modelling approaches. However, developing correct and useful models is not an easy task. We believe that there is a need for an interoperability framework that provides guidance on how MDD should be applied to address interoperability. A key to the success of MDD is the development of ontologies supporting the mapping from one model to another, either at the same level of abstraction or at different levels. Various approaches have been proposed and tested, starting from common ontologies, shared by all the models, to local ontologies, specific to each software. We propose here to discuss the applicability of MDD to the urban civil engineering (UCE) field. This should help to establish the requirements for ontologies to be applied for interoperability of systems commonly used in this domain. In the first section of this paper, we briefly introduce some key principles of the MDA approach and the role of ontologies in model transformation approaches. The following section describes the Model Driven Development (MDD) interoperability framework. The last part presents a way of applying the MDA techniques to urban civil engineering projects, with the objective of testing the feasibility and relevancy of the approach to this domain.

The language is a symbolic system unique to human being. The acquisition of language, which has its meanings in the real world, is important for robots to understand the environment and communicate with us in our daily life. This paper proposes a novel approach to establish a fundamental framework for the robots which can understand language through their whole body motions. The proposed framework is composed of three modules: “motion symbol”, “motion language model”, and “natural language model”. In the motion symbol module, motion data are symbolized by Hidden Markov Models (HMMs). Each HMM represents abstract motion patterns. Then the HMMs are defined as motion symbols. The motion language model is stochastically designed for links between motion symbols and words. This model consists of three layers of motion symbols, latent states and words. The connections between the motion symbol and the latent state, and between the latent state and the words are denoted by two kinds of probabilities respectively. One connection is represented by the probability that the motion symbol generates the latent state, and the other connection is represented by the probability that the latent state generates the word. Therefore, the motion language model can connect the motion symbols to the words through the latent state. The natural language model stochastically represents sequences of words. In this paper, a bigram, which is a special case of N-gram model, is adopted as the natural language model. This model has the words as nodes and transitions between two words as edges. Therefore sentence structure is expressed as transitions among words. The integration of the motion language model and natural language model can be implemented by the search computation for sentences corresponding to motions and for motions corresponding to sentences. Especially, the usage of the bigram as the natural language model provides a simple search computation so that appropriate and fast bidirectional computation between the motions and language can be achieved. Our approach makes it possible for humanoid robots not only to interpret motions as sentences but also to generate motions from sentences. The tests by using various motions and words validate our framework for the language acquisition of humanoid robots.

This paper describes a novel approach to linguistic mutual inference, which enables robots not only to linguistically interpret the motion patterns in the form of sentences but also to generate the motions from the sentences. The inference can be established based on two modules, the motion language model and the natural language model. The motion language model stochastically represents an association structure between symbols of motion patterns and the words in sentences assigned to the motion. This is a statistical model with a three layered structure of motion symbols, latent states and words. The natural language model statistically represents a structure of sentences based on word bigrams. The motion language model and the natural language model correspond to semantics and syntax respectively. An approach to the integration of motion language model with the natural language model allows the linguistic mutual inference for the robots. The two kinds of inference can be made by solving search problems, search for a sequence of words corresponding to a motion and search for a symbol of motion pattern corresponding to a sentence. The proposed approach to interpretation of motion patterns as sentences and generation of motion patterns from the sentences through the integration of motion language model with the natural language model is validated by an experiment on the human behavioral data.

In order to resolve the problems of model-driven software development, a model-driven approach for the development of Web application system is proposed in this paper. The development process starts from describing of platform independent models. Then, mapping relations from the source model to the target model are built according to the syntactic structure and semantic features of both metamodels, and model transformation as well as code generation can be achieved subsequently. ASP.NET is used as a target platform in the experiment which shows that this approach follows the essence, process and requirements of model-driven software development, and thus can make an effect support for model driven software engineering.

Model Driven Development (MDD) refers to the systematic use of models as primary engineering artifacts throughout a software development life cycle. In recently years, MDD has been increasingly employed to guide development with a focus on system modeling, code generation from models and white-box analysis of models. However, compositional system analysis regarding early Non-Functional Aspects/Properties (NFP) remains difficult. In this paper, we critically review the state-of-the-art of MDD in the context of non-functional aspects and shed some lights on the following two questions: 1) How to model Non-Functional Aspect/Property (NFP). The focus is to understand the different subtypes of a non-functional aspects and its compositional and emergent nature. 2) How models can be used for analyzing Non-functional Aspect/Property (NFP). This focuses on the analysis models in the form of reasoning frameworks (both qualitative and quantitative) behind each non-functional aspect.

Model Driven Development (MDD) requires model-to-model and/or model-to-text transformations to produce application code from high level descriptions. Creating such transformations is in itself a complex task, which requires mastering meta-modeling, ad hoc transformation languages, and custom development tools. This paper presents ALMOsT.js, an agile, in-browser framework for the rapid prototyping of MDD transformations, which lowers the technical skills required for Web and mobile developers to start be proficient with modeling and code generation. ALMOsT.js is shown at work in the creation of IFMLedit.org, a browser-based, online/offline environment for the MDD specification and rapid prototyping of web and mobile applications.

The usage of models within model-driven software development aims at facilitating complexity management of the system under development and closing the gap between the problem and the solution domain. Utilizing model-driven software development (MDD) tools for agile development can also increase the complexity within a project. The huge number of different artifacts and relations, their different kinds, and the high degree of automation hinder the understanding, maintenance, and evolution within MDD projects. A systematic approach to understand and manage MDD projects with a focus on its artifacts and corresponding relations is necessary to handle the complexity. The artifact-based analysis presented in this paper is such an approach. This paper gives an overview of different contributions of the artifact-based analysis but focuses on a specific kind of analysis: architecture consistency checking of model-driven development projects. By applying this kind of analyses, differences between the desired architecture and the actual architecture of the project at a specific point in time can be revealed.

Tiwalayo Eisape, Michael Tessler, Ishita Dasgupta, Fei Sha, Sjoerd Steenkiste, Tal Linzen. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Bryan Li, Samar Haider, Chris Callison-Burch. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Background: In model-driven development, model transformation transforms one model to another between different phases of software engineering. In model transformation, metamodel plays a vital role which defines the abstract syntax of models and the interrelationship between their elements. A unified metamodel defines an abstract syntax for both source and target models when they share core elements. Theoretical approaches define language and platform independent representation of models in software engineering. This paper investigates the theoretical foundation to this unified meta-modelling for their consistent transformation. Objective: This paper aims to define the formal foundations to the unified metamodel for generating implementation from design specifications and model reusability. Method: In this paper, the study considers transformation from design to implementation and vice versa using theoretical foundations to build a verified software system. Results: The related tools provide a formal representation of the design phase for verification purpose. Our approach provides a set-theoretical foundation to the unified metamodel for model transformation from USE (UML/OCL) to Spec#. In other words, our approach defines the formal foundation to define a model which consists of all the required properties for verification at the design and implementation phase. Conclusion: This paper introduced a new set of the theoretical framework which acts as an interface between the design and implementation to generate verified software systems.

The Repository for Model-Driven Development (ReMoDD) contains artifacts that support Model-Driven Development (MDD) research and education. ReMoDD is collecting (1) documented MDD case studies, (2) examples of models reflecting good and bad modeling practices, (3) reference models (including metamodels) that can be used as the basis for comparing and evaluating MDD techniques, (4) generic models and transformations reflecting reusable modeling experience, (5) descriptions of modeling techniques, practices and experiences, and (6) modeling exercises and problems that can be used to develop classroom assignments and projects. ReMoDD provides a single point of access to shared artifacts reflecting high-quality MDD experience and knowledge from industry and academia. This access facilitates sharing of relevant knowledge and experience that improve MDD activities in research, education and industry.