| Summary: | This article describes TagML, a method to generate collections of XML documents using model-to-model (M2M) transformations. To accomplish this goal, we define the TagML meta-model and the TagML-to-XML model-to-text transformation. While TagML models represent the essential characteristics of collections of XML documents, the TagML-to-XML transformation generates the textual representation of collections of XML documents from TagML models. This approach enables developers to define model-to-model transformations to generate TagML models. These models are turned into text applying the TagML-to-XML transformation. Consequently, developers are able to use declarative languages to define model-to-text transformations that generate XML documents, instead of traditional archetype-based languages to define model-to-text transformations that generate collections of XML documents. The TagML model editor as well as the TagML-to-XML transformation were developed as Eclipse plugins using the Eclipse Modeling Framework. The plugin has been developed following the Object Modeling Group standards to ensure the compatibility with legacy tools. Using TagML, unlike other previous proposals, implies the use of model-to-model transformations to generate XML documents, instead of model-to-text transformations, which results on an improvement of the transformation readability and reliability, as well as a reduction of the transformation maintenance costs. The proposed approach helps developers to define transformations less prone to errors than using the traditional approach. The novelty of this approach is based on the way XML documents are generated using model-to-model transformations instead of traditional model-to-text transformations. Moreover, the simplicity of the proposed approach enables the generation of XML documents without the need for any transformation configuration, which does not penalize the model reuse. To illustrate the features of the proposal, we present the generation of XHTML documents using UML class diagrams as input models. The evaluation section demonstrates that the proposed method is less prone to errors than the traditional one.
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