Model transformation by demonstration

Sun, Yu

doi:10.1145/1639950.1640036

Cited by 19 publications

(25 citation statements)

References 19 publications

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“…The second step is problem correction. The colonial selection algorithm (Sun et al 2009) could be adapted for finding the best immune response, i.e. the one corresponding to the optimal sequence of corrections to apply for correcting errors by automatically regenerating some rules from examples.…”

Section: Discussionmentioning

confidence: 99%

“…Various such by-example approaches have been described in the literature (Varro and Balogh 2007;Kessentini et al 2008Kessentini et al , 2010Wimmer et al 2007;Sun et al 2009;France and Rumpe 2007). The most similar one is Model Transformation By Example (MTBE), which was proposed in Kessentini et al (2008), France and Rumpe (2007).…”

Section: By Example Model Transformationmentioning

confidence: 99%

“…The transformation is seen as an optimization problem where different transformation possibilities are evaluated and a quality associated to each one depending on its conformance with the examples at hand. A similar approach to MTBE, called Model Transformation By Demonstration (MTBD), was proposed in Sun et al (2009). Instead of the MTBE idea of inferring the rules from a prototypical set of mappings, users are asked to demonstrate how the model transformation should be done, through direct editing (e.g.…”

Section: By Example Model Transformationmentioning

confidence: 99%

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Example-based model-transformation testing

2011

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A major concern in model-driven engineering is how to ensure the quality of the model-transformation mechanisms. One validation method that is commonly used is model transformation testing. When using this method, two important issues need to be addressed: the efficient generation/selection of test cases and the definition of oracle functions that assess the validity of the transformed models. This work is concerned with the latter. We propose a novel oracle function for model transformation testing that relies on the premise that the more a transformation deviates from well-known good transformation examples, the more likely it is erroneous. More precisely, the proposed oracle function compares target test cases with a base of examples that contains good quality transformation traces, and then assigns a risk level to them accordingly. Our approach takes inspiration from the biological metaphor of immune systems, where pathogens are identified by their difference with normal body cells. A significant feature of the approach is that one no longer needs to define an expected model for each test case. Furthermore, the detected faulty candidates are ordered by degree of risk, which helps the tester inspect the results. The validation results on a transformation mechanism used by an industrial partner confirm the effectiveness of our approach.

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Section: Discussionmentioning

confidence: 99%

Section: By Example Model Transformationmentioning

confidence: 99%

Section: By Example Model Transformationmentioning

confidence: 99%

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Example-based model-transformation testing

2011

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“…Another vast branch of research is that of model transformation. MDE principles state that model transformations are the preferred means of synthesising arbitrary artifacts (whatever these may be) from models [3,6,8,33], and considerable efforts have targeted the development and study of graphical and textual model transformation languages and tooling [1,12,34,35,37]. However, very few researchers, other than Levendovszky et al [23], have reported the use of model transformations for artifact synthesis in industrially relevant contexts.…”

Section: Related Workmentioning

confidence: 99%

Modular artifact synthesis from domain-specific models

Mannadiar

Vangheluwe

2011

Innovations Syst Softw Eng

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Domain-specific modelling reduces the gap between problem domain and solution domain. It supports modelling using constructs familiar to experts of a specific domain. Domain-specific models (DSms) are (semi) automatically transformed to various lower-level artifacts, including configuration files, documentation and executable programs. Although various aspects of model-driven development have been investigated, such as model versioning, debugging and transformation, relatively not much attention has been paid to formalise how artifacts are synthesised from DSms. State-of-the-art approaches rely on ad hoc coded generators that essentially use modelling tool APIs to programmatically iterate through internal representations of DSm entities to produce target-platform artifacts. In this work, we propose a more structured approach to artifact generation, where layered model transformations are used to modularly isolate, compile and re-combine various concerns within DSms, while maintaining traceability links between corresponding constructs at different levels of abstraction. We study and demonstrate how our approach simplifies addressing non-functional requirements (e.g., timing and resource utilisation constraints) of modern embedded systems. This is achieved through the modular synthesis of performance models from DSms. We illustrate our work by means of the synthesis of fully functional Google Android applications, performance predictions, simulations and performance measurement facilities from DSms of mobile phone applications.

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“…Recently, a similar approach to MTBE, called Model Transformation By Demonstration (MTBD), was proposed [45]. Instead of the MTBE idea of inferring the rules from a prototypical set of mappings, users are asked to demonstrate how the MT should be done, through direct editing (e.g., add, delete, connect, update) of the source model, so as to simulate the transformation process.…”

Section: Model Transformationmentioning

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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Model transformation by demonstration

Cited by 19 publications

References 19 publications

Example-based model-transformation testing

Example-based model-transformation testing

Modular artifact synthesis from domain-specific models

Search-based model transformation by example

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