A Framework for Model Transformation By-Example: Concepts and Tool Support

Strommer, Michael; Wimmer, Manuel

doi:10.1007/978-3-540-69824-1_21

Cited by 24 publications

(9 citation statements)

References 13 publications

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“…Their contribution differs from Varró and Balogh with respect to transformation mappings, which are defined in a concrete rather than an abstract syntax. Wimmer et al's contribution is improved in and Strommer and Wimmer [2008] to also handle n-m rules with the mention of a basic string manipulation operator (lowercase). Similarly, García-Magariño et al [2009] propose an ad-hoc algorithm to derive ATL n-m rules using example pairs with their mappings.…”

Section: Related Workmentioning

confidence: 98%

Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples

Baki

Sahraoui

2016

ACM Trans. Softw. Eng. Methodol.

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Model-driven engineering promotes models as main development artifacts. As several models may be manipulated during the software-development life cycle, model transformations ensure their consistency by automating model generation and update tasks. However, writing model transformations requires much knowledge and effort that detract from their benefits. To address this issue, Model Transformation by Example (MTBE) aims to learn transformation programs from source and target model pairs supplied as examples. In this article, we tackle the fundamental issues that prevent the existing MTBE approaches from efficiently solving the problem of learning model transformations. We show that, when considering complex transformations, the search space is too large to be explored by naive search techniques. We propose an MTBE process to learn complex model transformations by considering three common requirements: element context and state dependencies and complex value derivation. Our process relies on two strategies to reduce the size of the search space and to better explore it, namely, multi-step learning and adaptive search. We experimentally evaluate our approach on seven model transformation problems. The learned transformation programs are able to produce perfect target models in three transformation cases, whereas precision and recall values larger than 90% are recorded for the four remaining cases. ACM Reference Format: Islem Baki and Houari Sahraoui. 2016. Multi-step learning and adaptive search for learning complex model transformations from examples.

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Section: Related Workmentioning

confidence: 98%

Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples

Baki

Sahraoui

2016

ACM Trans. Softw. Eng. Methodol.

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“…[17,21,32,34]) have been proposed for automatically deriving model transformation rules. These approaches rely on user-defined examples of input and output models, and the inference is based on various techniques such as metaheuristics, model comparison and induction.…”

Section: Related Workmentioning

confidence: 99%

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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“…Defining model transformations rules by using the abstract syntax of graphical modeling languages comes on the one hand with the benefit of the generic applicability. On the other hand the creation of such transformation rules is often complicated and their readability is much lower compared to working with the concrete syntax as has been reported in several papers [3,8,19,21]. As a solution, the usage of the concrete syntax for the definition of the transformation rules has be proposed like in ATOM 3 [8].…”

Section: Related Workmentioning

confidence: 99%

“…Strommer and Wimmer [19] as well as Varró [19] go one step further by defining transformations purely by-example, i.e., instead of developing transformation rules, an example input model and the corresponding output model are given. From these example pairs, the general transformation rules are derived by a reasoning component.…”

Section: Related Workmentioning

confidence: 99%

An Example Is Worth a Thousand Words: Composite Operation Modeling By-Example

Brosch

Langer

Seidl

et al. 2009

Model Driven Engineering Languages and Systems

Self Cite

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Abstract. Predefined composite operations are handy for efficient modeling, e.g., for the automatic execution of refactorings, and for the introduction of patterns in existing models. Some modeling environments provide an initial set of basic refactoring operations, but hardly offer any extension points for the user. Even if extension points exist, the introduction of new composite operations requires programming skills and deep knowledge of the respective metamodel. In this paper, we introduce a method for specifying composite operations within the user's modeling language and environment of choice. The user models the composite operation by-example, which enables the semi-automatic derivation of a generic composite operation specification. This specification may be used in further modeling scenarios, like model refactoring and model versioning. We implemented the approach in the Operation Recorder and performed an evaluation by defining multiple complex refactorings for UML diagrams.

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A Framework for Model Transformation By-Example: Concepts and Tool Support

Cited by 24 publications

References 13 publications

Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples

Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples

Type inference in flexible model-driven engineering using classification algorithms

An Example Is Worth a Thousand Words: Composite Operation Modeling By-Example

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