Decision Reuse in an Interactive Model Transformation

Siikarla, Mika; Systä, Tarja

doi:10.1109/csmr.2008.4493307

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Therefore, creating MT rules may become a complex task [22]. On the other hand, it is often easier for experts to show transformation examples than to express complete and consistent transformation rules [14].…”

Section: Problem Statementmentioning

confidence: 99%

Search-based model transformation by example

et al. 2010

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Model transformation (MT) has become an important concern in software engineering. In addition to its role in model-driven development, it is useful in many other situations such as measurement, refactoring, and test-case generation. Roughly speaking, MT aims to derive a target model from a source model by following some rules or principles. So far, the contributions in MT have mostly relied on defining languages to express transformation rules. However, the task of defining, expressing, and maintaining these rules can be difficult, especially for proprietary and non-widely used formalisms. In some situations, companies have accumulated examples from past experiences. Our work starts from these observations to view the transformation problem as one to solve with fragmentary knowledge, i.e. with only examples of source-to-target MTs. Our approach has two main advantages: (1) it always proposes a transformation for a source model, even when rule induction is impossible or difficult to achieve; (2) it is independent from the source and target formalisms; aside from the examples, no extra information is needed. In this context, we propose an Communicated by Prof. Krzysztof Czarnecki.optimization-based approach that consists of finding in the examples combinations of transformation fragments that best cover the source model. To that end, we use two strategies based on two search-based algorithms: particle swarm optimization and simulated annealing. The results of validating our approach on industrial projects show that the obtained models are accurate.

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Section: Problem Statementmentioning

confidence: 99%

Search-based model transformation by example

et al. 2010

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“…As such, several transformation languages and tools have been made available to MDE practitioners. Unfortunately, developing MTs remains a challenging task [Siikarla and Systa 2008] as it requires many types of knowledge, for example, semantic equivalence between source and target domains, source and target metamodels, model transformation language, and so on, that domain experts do not generally have. Moreover, developing large sets of transformations requires a lot of effort as candidate transformation rules need to be written and debugged iteratively in order to produce the correct output.…”

Section: Introductionmentioning

confidence: 99%

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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User Driven Evolution of User Interface Models – The FLEPR Approach

Hennig¹,

Bergh²,

Luyten³

et al. 2011

Human-Computer Interaction – INTERACT 2011

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Abstract.In model-based user interface development, models at different levels of abstraction are used. While ideas may initially only be expressed in more abstract models, modifications and improvements according to user's feedback will likely be made at the concrete level, which may lead to model inconsistencies that need to be fixed in every iteration. Transformations form the bridge between these models. Because one-to-one mappings between models cannot always be defined, these transformations are completely manual or they require manual post-treatment.We propose interactive but automatic transformations to address the mapping problem while still allowing designer's creativity. To manage consistency and semantic correctness within and between models and therefore to foster iterative development processes, we are combining these with techniques to track decisions and modifications and techniques of intra-and inter-model validation. Our approach has been implemented for abstract and concrete user interface models using Eclipse-based frameworks for model-driven engineering. Our approach and tool support is illustrated by a case study.

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Decision Reuse in an Interactive Model Transformation

Abstract: Abstract

Cited by 3 publications

References 6 publications

Search-based model transformation by example

Search-based model transformation by example

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

User Driven Evolution of User Interface Models – The FLEPR Approach

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