Generating realistic test models for model processing tools

Pietsch, Pit; Yazdi, Hamed Shariat; Kelter, Udo

doi:10.1109/ase.2011.6100140

Cited by 13 publications

(9 citation statements)

References 3 publications

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“…The SiDiff model generator (SMG) has been proposed by Pietsch et al [22]. It takes an existing model as input and manipulates it by applying model editing operations, configured by a stochastic controller.…”

Section: Rule-based Approachesmentioning

confidence: 99%

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%

Generating Large EMF Models Efficiently

Nassar

Kosiol

Kehrer

et al. 2020

Lecture Notes in Computer Science

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“…Ehrig et al [6] present an algorithm that can generate instances of metamodels by transforming it into a set of graph specification rules. Then, the rules are selected randomly in order to perform the generation Pietsch et al [11] present a generator of test models for model processing tools. They use a stochastic controller to apply low-level operations (create, delete, update, move) and more complex operations (composed of these low-level operations) to elements of the model.…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately, [9,6,11,2] are not adapted for the generation of behavioral models (e.g., process) and may produce unrealistic processes since the possibility of influencing the generation (add some ponderations) is either not available or handled only on given elements/attributes. The problem comes from the fact that these approaches aim at generating static models and are not tailored towards behavioral models generation.…”

Section: Related Workmentioning

confidence: 99%

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Generation of process using multi-objective genetic algorithm

Laurent

Bendraou

Gervais³

2013

Proceedings of the 2013 International Conference on Software and System Process

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The growing complexity of processes whatever their kind (i.e. business, software, medical, military) stimulates the adoption of process execution, analysis and verification techniques. However, such techniques cannot be accurately validated as it is not possible to obtain numerous and realistic process models in order to stress test them. The small set of samples and "toy" models publically available in the literature is usually insufficient to conduct serious empirical studies and thus, to validate thoroughly work around process analysis and verification. In this paper, we face this problem by proposing a process model generator using a multiobjective genetic algorithm. The originality of our approach comes from the fact that process models are built through a sequence of high-level operations inspired by the way a process modeler could have actually performed to model a process. A working generator prototype has been implemented and shows that it is possible to quickly generate huge, syntactically sound and user-tailored process models.

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“…While model generators [8,9] can be used to create realistic test models synthetically, configuring these tool is time intensive and requires in-depth knowledge of edit processes for a given domain.…”

Section: Motivationmentioning

confidence: 99%

Assessing the Quality of Model Differencing Engines

Pietsch¹,

Yazdi²,

Kelter³

et al. 2012

Softwaretechnik-Trends

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In recent years many tools and algorithms for model comparison and differencing were proposed. Typically, the main focus of the research laid on being able to compute the difference in the first place. Only very few papers addressed the quality of the delivered differences sufficiently. Hence, this is a general shortcoming in the state-of-the-art. Currently, there are no established community standards how to assess the quality of differences and it is neither possible to compare the quality of different algorithms, nor can developers decide whether or not an algorithm is able to produce adequate results in a given application scenario. We propose a parallel working session to be held to discuss this general problem and its implications. The goal of the working session is to achieve a common understanding of what the crucial factors in assessing the quality of differences are. Furthermore, it is planed to discuss possible solutions that help the research community as whole, e.g. by drafting the design of an initial benchmark corpus which later could be turned into a standardized, openly available benchmark set. MOTIVATIONMany different algorithms for model differencing were proposed in recent years; surveys can be found in [5,6,7]. Typically, the main emphasis of these approaches is focused on the algorithms used to compute the difference between two revisions of a model. The evaluation of the algorithms, if conducted at all, is usually based only on sets consisting of few, small and sometimes even specially created, test models. Obviously, such an evaluation is not enough to assess the quality of the algorithms objectively, nor is it possible to compare the quality of different algorithms this way. Hence, developers which must choose a model differencing engine in their day-to-day work can not make informed decisions which tool fits their needs best.The reasons why very few papers proposing model differencing algorithms address the quality of the delivered differences sufficiently are manifold:• Ultimately, the quality of a difference can only be assessed in the context of the use case in which the difference is used. If the difference between two models is to be displayed to developers then understandability and compactness [2] are highly relevant; in the context of merging, the avoidance of merge conflicts is important. If a difference is to be used for internal delta storage or for batch patching of models, none of the above properties are relevant and it is sufficient to simply save the smallest representation of the difference.• Properties such as quality and understandability are not generally defined and must be refined further for each specific model type and paradigm.• Test models where the evolution is sufficiently known and documented are not available for many domains. While model generators [8,9] can be used to create realistic test models synthetically, configuring these tool is time intensive and requires in-depth knowledge of edit processes for a given domain.• Some generic algorithms ma...

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Generating realistic test models for model processing tools

Cited by 13 publications

References 3 publications

Generating Large EMF Models Efficiently

Generating Large EMF Models Efficiently

Generation of process using multi-objective genetic algorithm

Assessing the Quality of Model Differencing Engines

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