A Comparison of Two-Level and Multi-level Modelling for Cloud-Based Applications

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-21151-0_2The Cloud Modelling Framework (CloudMF) is an approach to apply model-driven engineering principles to the specification and execution of cloud-based applications. It comprises a domain-specific language to model the deployment topology of multi-cloud applications, along with a models@run-time environment to facilitate reasoning and adaptation of these applications at run-time. This paper reports on some challenges encountered during the design of CloudMF, related to the adoption of the two-level modelling approach and especially the type-instance pattern. Moreover, it proposes the adoption of an alternative, multi-level modelling approach to tackle these challenges, and provides a set of criteria to compare both approaches.The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under grant agreement numbers 317715 (PaaSage), 318392 (Broker@Cloud), and 611125 (MONDO), the Spanish Ministry under project Go Lite (TIN2011-24139), and the Madrid Region under project SICOMORO (S2013/ICE-3006)

Multi-level model product lines: Open and closed variability for modelling language families

Modelling is an essential activity in software engineering processes. It typically involves two meta-levels: one includes meta-models that describe modelling languages, and the other contains models built by instantiating those meta-models. Multi-level modelling generalizes this approach by allowing models to span an arbitrary number of meta-levels. A scenario that profits from multi-level modelling is the definition of language families that become specialized by successive refinements at subsequent meta-levels, hence promoting language reuse. This enables an open set of variability options for the possible specializations of a given language. However, multi-level modelling lacks the ability to express closed variability regarding the supported language primitives and their realizations. This limits the reuse opportunities of a language family. To improve this situation, we propose a novel combination of product lines with multi-level modelling to cover both open and closed variability. Our proposal is backed by a formal theory that guarantees correctness, and is implemented atop the MetaDepth multi-level modelling tool.Work funded by the Spanish Ministry of Science (project MASSIVE, RTI2018-095255-B-I00) and the R&D programme of Madrid (project FORTE, P2018/TCS-4314)

DEVELOPMENT OF PROBLEM-SPECIFIC MODELING LANGUAGE TO SUPPORT SOFTWARE VARIABILITY IN "SMART HOME" SYSTEMS

Building conceptual models for software design, in particular for high-tech applications such as smart home systems, is a complex task that significantly affects the efficiency of their development processes. One of the innovative methods of solving this problem is the use of domain-specific modeling languages (DSMLs), which can reduce the time and other project resources required to create such systems. The subject of research in this paper is approaches to the development of DSML for Smart Home systems as a separate class of Internet of Things systems. The purpose of this work is to propose an approach to the development of DSMLs based on a model of variability of the properties of such a system. The following tasks are being solved: analysis of some existing approaches to the creation of DSMLs; construction of a multifaceted classification of requirements for them, application of these requirements to the design of the syntax of a specific DSML-V for the creation of variable software in smart home systems; development of a technological scheme and quantitative metrics for experimental evaluation of the effectiveness of the proposed approach. The following methods are used: variability modeling based on the property model, formal notations for describing the syntax of the DSML-V language, and the use of the open CASE tool metaDepth. Results: a multifaceted classification of requirements for a broad class of DSML languages is built; the basic syntactic constructions of the DSML-V language are developed to support the properties of software variability of "Smart Home" systems; a formal description of such syntax in the Backus-Naur notation is given; a technological scheme for compiling DSML-V specifications into the syntax of the language of the open CASE tool metaDepth is created; the effectiveness of the proposed approach using quantitative metrics is experimentally investigated. Conclusions: the proposed method of developing a specialized problem-oriented language for smart home systems allows for multilevel modeling of the variability properties of its software components and provides an increase in the efficiency of programming such models by about 14% compared to existing approaches

Automated reuse of model transformations through typing requirements models

Model transformations are key elements of model-driven engineering, where they are used to automate the manipulation of models. However, they are typed with respect to concrete source and target meta-models, making their reuse for other (even similar) meta-models challenging. To improve this situation, we propose capturing the typing requirements for reusing a transformation with other meta-models by the notion of a typing requirements model (TRM). A TRM describes the prerequisites that amodel transformation imposes on the source and targetmeta-models to obtain a correct typing. The key observation is that any meta-model pair that satisfies the TRM is a valid reuse context for the transformation at hand. A TRM is made of two domain requirement models (DRMs) describing the requirements for the source and target meta-models, and a compatibility model expressing dependencies between them. We define a notion of refinement between DRMs and see meta-models as a special case of DRM. We provide a catalogue of valid refinements and describe how to automatically extract a TRM from an ATL transformation. The approach is supported by our tool TOTEM. We report on two experiments-based on transformations developed by third parties and meta-model mutation techniques-validating the correctness and completeness of our TRM extraction procedure and confirming the power of TRMs to encode variability and support flexible reuseWork partially funded by the R&D programme of the Madrid Region (project FORTE, S2018/TCS4314), the Spanish Ministry of Science (project MASSIVE, RTI2018-095255-B-I00), the Spanish MINECO(project RECOM, TIN2015-73968-JIN, AEI/FEDER/UE), a Ramón y Cajal 2017 grant, and the European Union Horizon 2020 research and innovation programme through the Polyglot and Hybrid Persistence Architectures for Big Data Analytics (TYPHON) project (#780251

Model-driven engineering with domain-specific meta-modelling languages

The final publication is available at Springer via http://dx.doi.org/10.1007/s10270-013-0367-zDomain-specific modelling languages are normally defined through general-purpose meta-modelling languages like the MOF. While this is satisfactory for many model-driven engineering (MDE) projects, several researchers have identified the need for domain-specific meta-modelling (DSMM) languages. These provide customised domain-specific meta-modelling primitives aimed at the definition of modelling languages for a specific domain, as well as the construction of meta-model families. Unfortunately, current approaches to DSMM rely on ad hoc methods which add unnecessary complexity to the realization of DSMM in practice. Hence, the goal of this paper is to simplify the definition and usage of DSMM languages. For this purpose, we apply multi-level meta-modelling for the systematic engineering of DSMM architectures. Our method integrates techniques to control the meta-modelling primitives offered to the users of the DSMM languages, provides a flexible approach to define textual concrete syntaxes for DSMM languages, and extends existing model management languages (for model-to-model transformation, in-place transformation and code generation) to work in a multi-level setting, thus enabling the practical use of DSMM in MDE. As a proof of concept, we report on a working implementation of these ideas in the MetaDepth tool.We thank the referees for their detailed and useful comments. This work has been funded by the Spanish Ministry of Economy and Competitivity with project “Go Lite” (TIN2011-24139), and the R&D programme of Madrid Region with project “eMadrid” (S2009/TIC-1650)

Specification and Implementation of a Deep OCL Dialect

The deep modeling tool Multi-level Modeling and Ontology Engineering Environment (Melanee) developed by the Software Engineering Group of the University of Mannheim allows clean and strict meta-modeling across multiple classification levels within Eclipse. Modeling with Melanee comprises two dimensions, a linguistic and an ontological dimension. As Melanee is fully embedded in the Eclipse Modeling Framework, it is usable with the Object Constraint Language (OCL). However, the current OCL implementation is not aware of multi-level modeling features like the distinction between ontological and linguistic classification and the existence of multiple ontological levels. The aim of this thesis is to extend the current OCL implementation so that it is multi-level aware. First, a deep OCL dialect is elaborated which takes deep modeling features into consideration. Based on this dialect, an implementation of an interactive level-agnostic OCL Console will enable queries of different values of different model elements