Live Model Transformations Driven by Incremental Pattern Matching

Ráth, István; Bergmann, Gábor; Ökrös, András; Varró, Dániel

doi:10.1007/978-3-540-69927-9_8

Cited by 37 publications

(37 citation statements)

References 18 publications

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“…Especially the work in [7] implements live incrementality, based on the Rete algorithm, a well-known technique in the field of rule-based systems. These graph transformation approaches focus on incremental pattern-matching to improve the performances of the transformation.…”

Section: Related Workmentioning

confidence: 99%

Towards Incremental Execution of ATL Transformations

Jouault

Tisi

2010

Lecture Notes in Computer Science

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International audienceUp to now, the execution of ATL transformations has always followed a two-step algorithm: 1) matching all rules, 2) applying all matched rules. This algorithm does not support incremental execution. For instance, if a source model is updated, the whole transformation must be executed again to get the updated target model. In this paper, we present an incremental execution algorithm for ATL, as well as a prototype. With it, changes in a source model are immediately propagated to the target model. Our approach leverages previous works of the community, notably on live transformations and incremental OCL. We achieve our goal on a subset of ATL, without requiring modifications to the language

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Section: Related Workmentioning

confidence: 99%

Towards Incremental Execution of ATL Transformations

Jouault

Tisi

2010

Lecture Notes in Computer Science

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“…• As a result, we obtain a declarative formalism for defining view models with execution semantics compliant with incremental and live graph transformations [20]: when a new activation of a forward rule is detected, the corresponding view elements are created and when a previously existing activation of a forward rule disappears the related view elements are removed. However, this is still a restricted subclass of model transformations since (1) each rule creates exactly one new element (object or reference) in the view model, and (2) the transformation is monotonic in the sense that a view element always depends on the existence of a match of a positive pattern (i.e.…”

Section: Definition Of View Modelsmentioning

confidence: 99%

Incremental backward change propagation of view models by logic solvers*

Semeráth

Debreceni

Horváth

et al. 2016

Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems

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View models are key concepts of domain-specific modeling to provide task-specific focus (e.g., power or communication architecture of a system) to the designers by highlighting only the relevant aspects of the system. View models can be specified by unidirectional forward transformations (frequently captured by graph queries), and automatically maintained upon changes of the underlying source model using incremental transformation techniques. However, tracing back complex changes from one or more abstract view to the underlying source model is a challenging task, which, in general, requires the simultaneous analysis of transformation specifications and well-formedness constraints to create valid changes in the source model. In this paper we introduce a novel delta-based backward transformation technique using SAT solvers to synthetize valid and consistent change candidates in the source model, where only forward transformation rules are specified for the view models.

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“…The Eclipse framework VIATRA2 by Rath et al [6] presents another approach for live model transformation. VIATRA2 can incrementally synchronize a target model to editing operations carried out on a source model.…”

Section: Related Workmentioning

confidence: 99%

Recommending Auto-completions for Software Modeling Activities

Kuschke

Mäder

Rempel

2013

Lecture Notes in Computer Science

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Abstract. Auto-completion of textual inputs benefits software developers using IDEs and editors. However, graphical modeling tools used to design software do not provide this functionality. The challenges of recommending auto-completions for graphical modeling activities are largely unexplored. Recommending auto-completions during modeling requires detecting meaningful partly completed activities, tolerating variance in user actions, and determining the most relevant activity that a user wants to perform. This paper proposes an approach that works in the background while a developer is creating or evolving a model and handles all these challenges. Editing operations are analyzed and matched to a predefined but extensible catalog of common modeling activities for structural UML models. In this paper we solely focus on determining recommendations rather than automatically completing an activity. We demonstrated the quality of recommendations generated by our approach in a controlled experiment with 16 students evolving models. We recommended 88% of the activities that a user wanted to perform within a short list of ten recommendations.

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Live Model Transformations Driven by Incremental Pattern Matching

Cited by 37 publications

References 18 publications

Towards Incremental Execution of ATL Transformations

Towards Incremental Execution of ATL Transformations

Incremental backward change propagation of view models by logic solvers*

Recommending Auto-completions for Software Modeling Activities

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