Automatically Deriving the Specification of Model Editing Operations from Meta-Models

Kehrer, Timo; Taentzer, Gabriele; Rindt, Michaela; Kelter, Udo

doi:10.1007/978-3-319-42064-6_12

Cited by 47 publications

(27 citation statements)

References 16 publications

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“…It takes an existing model as input and manipulates it by applying model editing operations, configured by a stochastic controller. On the meta-level, the SMG was integrated into the approach and tool presented by Kehrer et al [13,25], which generates a complete set of consistency-preserving edit operations for a given meta-model. It supports meta-models with somewhat restricted multiplicities, however.…”

Section: Rule-based Approachesmentioning

confidence: 99%

“…To answer RQ 1, we conducted two scalability experiments. We used 8 metamodels taken from the literature and projects, namely the Statechart meta-model of Magicdraw [13], Web model [5], Car Rental and Class model [2], Bugzilla, Latex, Warehouse, and GraphML (GML) [3]. The average size of the metamodels is 44 elements (16 nodes, 17 edges, 11 attributes) and the number of multiplicity bounds is 24 on average.…”

Section: Scalability Experimentsmentioning

confidence: 99%

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Generating Large EMF Models Efficiently

Nassar

Kosiol

Kehrer

et al. 2020

Lecture Notes in Computer Science

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There is a growing need for the automated generation of instance models to evaluate model-driven engineering techniques. Depending on a chosen application scenario, a model generator has to fulfill different requirements: As a modeling language is usually defined by a meta-model, all generated models are expected to conform to their metamodels. For performance tests of model-driven engineering techniques, the efficient generation of large models should be supported. When generating several models, the resulting set of models should show some diversity. Interactive model generation may help in producing relevant models. In this paper, we present a rule-based, configurable approach to automate model generation which addresses the stated requirements. Our model generator produces valid instance models of meta-models with multiplicities conforming to the Eclipse Modeling Framework (EMF). An evaluation of the model generator shows that large EMF models (with up to half a million elements) can be produced. Since the model generation is rule-based, it can be configured beforehand or during the generation process to produce sets of models that are diverse to a certain extent.

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Section: Rule-based Approachesmentioning

confidence: 99%

Section: Scalability Experimentsmentioning

confidence: 99%

Generating Large EMF Models Efficiently

Nassar

Kosiol

Kehrer

et al. 2020

Lecture Notes in Computer Science

Self Cite

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“…In earlier versions of the tool, the user was required to manually create the Henshin transformation rules and then specify them in the DSL configuration. In this paper we are using the SERGe [13] meta-tool to automatically generate the initial consistency preserving edit rules (CPERs). For each of the generated rules we then make a copy to which we apply a set of refinements to better guide the evolutionary process by ensuring that edit operations encoded in the rules can be applied to models conforming to both the problem and the solution meta-models.…”

Section: Searching Optimal Models With Generated Rulesmentioning

confidence: 99%

“…Previous work on automatic generation of transformation rules from metamodel information has been reported in [13]. SERGe is a meta-tool which generates a complete set of complete and consistency-preserving edit operations (CPEOs) for a given meta-model.…”

Section: Generating the Rulesmentioning

confidence: 99%

“…We show how a set of rules can be automatically generated from a meta-model, objective functions, and a set of additional constraints. The rule generation algorithm presented in this paper uses an extended variant of the SERGe (SiDiff Edit Rule Generator) algorithm presented in [13]. We demonstrate, using the CRA case study, that we are able to generate rules that enable efficient optimisation runs leading to good results.…”

Section: Introductionmentioning

confidence: 96%

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Towards Automatic Generation of Evolution Rules for Model-Driven Optimisation

Burdusel

Zschaler

2018

Software Technologies: Applications and Foundations

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Over recent years, optimisation and evolutionary search have seen substantial interest in the MDE research community. Many of these techniques require the specification of an optimisation problem to include a set of model transformations for deriving new solution candidates from existing ones. For some problems-for example, planning problems, where the domain only allows specific actions to be taken-this is an appropriate form of problem specification. However, for many optimisation problems there is no such domain constraint. In these cases providing the transformation rules over-specifies the problem. The choice of rules has a substantial impact on the efficiency of the search, and may even cause the search to get stuck in local optima. In this paper, we propose a new approach to specifying optimisation problems in an MDE context without the need to explicitly specify evolution rules. Instead, we demonstrate how these rules can be automatically generated from a problem description that consists of a meta-model for problems and candidate solutions, a list of meta-classes, instances of which describe potential solutions, a set of additional multiplicity constraints to be satisfied by candidate solutions, and a number of objective functions. We show that rules generated in this way lead to optimisation runs that are at least as efficient as those using hand-written rules.1 https://eclipse.org/modeling/emf/ 2 http://moeaframework.org/

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