Evaluating Multi-variant Model-To-Text Transformations Realized by Generic Aspects

Abstract

The discipline model-driven product line engineering (MDPLE) aims at increasing the level of productivity when realizing a family of related products. Relying on model-driven software engineering (MDSE) seeks to support this effect by using models raising the level of abstraction. In MDSE model transformations are the key technology to transform in between different (model) representations. By now, model transformations are mature and successfully applied in many use cases. In annotative approaches to MDPLE model elements are typically augmented with variability annotations controlling in which products the elements are visible. For delivering products, source code is generated from the configured models in model-to-text (M2T) transformations. Applying a state-of-the-art model transformation on an annotated model, however, does not regard the annotations since such single-variant model transformations (SVMTs) are unaware of annotations and not able to transfer them to the output. In the present work we evaluate our solution which reuses the already existing SVMT support and propagates annotations orthogonally. In particular, a generic aspect, supporting any kind of input metamodel, augments the outcome of SVMTs with annotations. Comparing the transformation with the state-of-the-art approach of manually adding the annotations to the target model reveals not only $100\%$ accuracy regarding the similarity of the derived products. It also states a significant reduction of the user effort compared to the laborious task of manually annotating the target source code. In this way our approach helps to really increase the productivity in MDPLE.