The Design of a Conceptual Framework and Technical Infrastructure for Model Management Language Engineering

Paige, Richard F.; Kolovos, Dimitrios S.; Rose, Louis M.; Drivalos, Nicholas; Polack, Fiona

doi:10.1109/iceccs.2009.14

Cited by 61 publications

(53 citation statements)

References 14 publications

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“…Using a multipass model-to-model transformation, the annotated diagram is automatically transformed to an intermediate Muddle (the Muddle metamodel is given in Fig. 1), so it can be consumed by suites like the Epsilon platform [28] to perform model management operations (e.g. model-to-text transformation).…”

Section: Overviewmentioning

confidence: 99%

“…There are at least two different approaches for solving this problem. The first is the application of a mechanism that will check for untyped nodes on the example models when these are created and request from the domain experts to provide their types; a constraint and language with model repairing capabilities [28] can be used to support this. However, such an approach may force users to take decision on types when they are not ready to do so.…”

Section: Introductionmentioning

confidence: 99%

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Type inference in flexible model-driven engineering using classification algorithms

et al. 2018

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Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in the language definition process. In such approaches, no metamodel is created upfront, but instead the process starts with the definition of example models that will be used to infer the metamodel. Pre-defined metamodels created by MDE experts may miss important concepts of the domain and thus restrict their expressiveness. However, the lack of a metamodel, that encodes the semantics of conforming models has some drawbacks, among others that of having models with elements that are unintentionally left untyped. In this paper, we propose the use of classification algorithms to help with the inference of such untyped elements. We evaluate the proposed approach in a number of random generated example models from various domains. The correct type prediction varies from 23 to 100% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used.

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Section: Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Type inference in flexible model-driven engineering using classification algorithms

et al. 2018

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“…In addition, drawing tools do not require MDE-specific expertise. The elements (nodes and edges) of these flexible example models can have type annotations assigned to them to describe the domain concept they represent and can also be amenable to programmatic model management using MDE suites like Epsilon [33].…”

Section: E X a Mp L E Mo D E L D E F I N I T I O Nmentioning

confidence: 99%

“…A review of the available flexible or bottom-up methodologies and tools was also performed as part of the literature review for this research project. This thesis also contributes to the extension of a flexible modelling technique, called Muddles, which is part of the Epsilon MDE suite [33] built on top the Eclipse Modelling Framework (EMF) [34] and the GraphML file format [35].…”

Section: Research Contributionsmentioning

confidence: 99%

“…Among others the most widely used are the Epsilon Transformation Language [62], part of the Epsilon [33] suite, the Atlas Transformation Language (ATL) [63], the OMG's QVT [3] and VIATRA [64]. Languages like the Epsilon Generation Language (EGL) [65], Xpand [66,67] and Acceleo [68] can be used for model-to-text transformations specifically.…”

Section: Model Managementmentioning

confidence: 99%

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Type Inference in Flexible Model-Driven Engineering

Zolotas

Matragkas

Devlin

et al. 2015

Lecture Notes in Computer Science

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Model-driven Engineering (MDE) is an approach to software development that promises increased productivity and product quality. Domain models that conform to metamodels, both of which are the core artefacts in MDE approaches, are manipulated to perform different development processes using specific MDE tools.However, domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using MDE tools. Flexible or bottom-up Model-driven Engineering is an emerging approach to domain and systems modelling that tackles this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in MDE processes. In this approach, no metamodel is created upfront but instead the process starts with the definition of example models that will be used to infer a draft metamodel. When complete knowledge of the domain is acquired, a final metamodel is devised and a transition to traditional MDE approaches is possible. However, the lack of a metamodel that encodes the semantics of conforming models and of tools that impose these semantics bears some drawbacks, among others that of having models with nodes that are unintentionally left untyped. In this thesis we propose the use of approaches that use algorithms from three different research areas, that of classification algorithms, constraint programming and graph similarity to help with the type inference of such untyped nodes. We perform an evaluation of the proposed approaches in a number of randomly generated example models from 10 different domains with results suggesting that the approaches could be used for type inference both in an automatic or a semi-automatic style. 3For my parents Despoina and Michalis

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Configurable three‐way model merging

Sharbaf

Zamani

2020

Softw Pract Exp

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SummarySoftware development is a collaborative activity that requires teams of software engineers to cooperate and work in parallel on versions of models. However, model management techniques such as model differencing, merging, and versioning have turned out to be difficult challenges, due to the complexity of operations and graph‐like nature of models. Therefore, a well‐developed support for model merging process, as well as conflict management, is highly desired. This paper presents a novel process for model merging, called the Epsilon‐based Three‐way Merging Process (E3MP) process. Model merging is a significant problem where there are different versions of a system model amongst modeler teams. E3MP includes three components implemented into the Epsilon framework. First, modelers can define domain‐specific rules that customize the merging process. Second, E3MP enables an automated method for syntactic and semantic conflict detection amongst different versions of the system model. Third, E3MP puts forward a pattern‐based approach for conflict resolution. We applied two generic benchmarks to assess conflict detection and resolution capabilities of our approach and carried out an initial scalability evaluation for the model merge with large models and large change sets. The results of our experiments revealed that the proposed process allows generating consistent and semantically correct merged models.

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The Design of a Conceptual Framework and Technical Infrastructure for Model Management Language Engineering

Cited by 61 publications

References 14 publications

Type inference in flexible model-driven engineering using classification algorithms

Type inference in flexible model-driven engineering using classification algorithms

Type Inference in Flexible Model-Driven Engineering

Configurable three‐way model merging

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