Union Models: Support for Efficient Reasoning About Model Families Over Space and Time

Alwidian, Sanaa; Amyot, Daniel

doi:10.1007/978-3-030-30690-8_12

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…with mc.struct the set of structural features (attributes and references) defined by the metaclass mc. The requirement of consistent deprecation given by (1) assures that whenever a metaclass mc is deprecated, its structural features are deprecated as well. The deprecation function is extended to models as well, as follows: given a model ∈ MM then for any metaclass mc ∈ MM depr (mc) ∧ ∈ mc.instances =⇒ depr ( )…”

Section: Running Examplementioning

confidence: 99%

“…Therefore, it might make sense to consider a preparatory stage during which the evolution of the metamodel is stepwise executed, allowing the designer to perform a sequence of smaller adaptations of the artifacts instead of an extended one. By maintaining both the initial and final version of a metamodel in a union, model [1] intermediate adaptation versions of the artifacts can be easily handled. Only when the migration is complete, the artifact changes are committed, providing the modeler with practical migration support, especially in the presence of breaking and unresolvable changes.…”

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confidence: 99%

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Metamodel deprecation to manage technical debt in model co-evolution

Iovino

Salle

Ruscio

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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Model-Driven Engineering helps formalize problem-domains by using metamodels. Modeling ecosystems consisting of purposely designed editors, transformations, and code generators are defined on top of the metamodels. Analogously to other software forms, metamodels can evolve-consequently, the validity of existing artifacts might be compromised. Coupled evolution provides techniques for restoring artifacts' validity in response to metamodel evolution. In this paper, we propose using deprecation in metamodeling to mitigate the difficulties in performing a class of adaptations that must be operated manually. Technical debt in co-evolution can be regarded as the outcome of procrastinating the migration of artifacts and, thus, must be reduced if not eliminated. Tool support for the adoption of deprecation and technical debt is used to demonstrate the feasibility of the methods. CCS CONCEPTS • Software and its engineering → Model-driven software engineering; Software system models; Software system structures; Software organization and properties.

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Section: Running Examplementioning

confidence: 99%

mentioning

confidence: 99%

Metamodel deprecation to manage technical debt in model co-evolution

Iovino

Salle

Ruscio

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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“…Lifting the related distinction from codelevel mechanisms, two main types are distinguished: Annotative mechanisms represent variability with an annotated integrated representation of all variants. Mechanism in this category are model templates [25,42,79] and union models [5]. Compositional mechanisms represent variability by composing variants from smaller sub-models.…”

Section: Introductionmentioning

confidence: 99%

Variability representations in class models

Strüber

Anjorin

Berger

2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems

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Owing to the ever-growing need for customization, software systems often exist in many different variants. To avoid the need to maintain many different copies of the same model, developers of modeling languages and tools have recently started to provide representations for such variant-rich systems, notably variability mechanisms that support the implementation of differences between model variants. Available mechanisms either follow the annotative or the compositional paradigm, each of them having unique benefits and drawbacks. Language and tool designers select the used variability mechanism often solely based on intuition. A better empirical understanding of the comprehension of variability mechanisms would help them in improving support for effective modeling.In this paper, we present an empirical assessment of annotative and compositional variability mechanisms for class models. We report and discuss findings from an experiment with 73 participants, in which we studied the impact of two selected variability mechanisms during model comprehension tasks. We find that, compared to the baseline of listing all model variants separately, the annotative technique did not affect developer performance. Use of the compositional mechanism correlated with impaired performance. For two out of three considered tasks, the annotative mechanism is preferred to the compositional one and the baseline. We present actionable recommendations concerning support of flexible, tasksspecific solutions, and the transfer of established best practices from the code domain to models. CCS CONCEPTS• Software and its engineering → Model-driven software engineering; Software product lines.

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Effects of variability in models: a family of experiments

et al. 2022

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The ever-growing need for customization creates a need to maintain software systems in many different variants. To avoid having to maintain different copies of the same model, developers of modeling languages and tools have recently started to provide implementation techniques for such variant-rich systems, notably variability mechanisms, which support implementing the differences between model variants. Available mechanisms either follow the annotative or the compositional paradigm, each of which have dedicated benefits and drawbacks. Currently, language and tool designers select the used variability mechanism often solely based on intuition. A better empirical understanding of the comprehension of variability mechanisms would help them in improving support for effective modeling. In this article, we present an empirical assessment of annotative and compositional variability mechanisms for three popular types of models. We report and discuss findings from a family of three experiments with 164 participants in total, in which we studied the impact of different variability mechanisms during model comprehension tasks. We experimented with three model types commonly found in modeling languages: class diagrams, state machine diagrams, and activity diagrams. We find that, in two out of three experiments, annotative technique lead to better developer performance. Use of the compositional mechanism correlated with impaired performance. For all three considered tasks, the annotative mechanism was preferred over the compositional one in all experiments. We present actionable recommendations concerning support of flexible, tasks-specific solutions, and the transfer of established best practices from the code domain to models.

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Union Models: Support for Efficient Reasoning About Model Families Over Space and Time

Cited by 9 publications

References 19 publications

Metamodel deprecation to manage technical debt in model co-evolution

Metamodel deprecation to manage technical debt in model co-evolution

Variability representations in class models

Effects of variability in models: a family of experiments

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