On the value of quality attributes for refactoring ATL model transformations: A multi-objective approach

Context: Model transformations play a fundamental role in Model-Driven Engineering (MDE) as they are used to manipulate models and to transform them between source and target metamodels. However, model transformation programs lack significant support to maintain good quality which is in contrast to established programming paradigms such as object-oriented programming. In order to improve the quality of model transformations, the majority of existing studies suggest manual support for the developers to execute a number of refactoring types on model transformation programs. Other recent studies aimed to automate the refactoring of model transformation programs, mostly focusing on the ATLAS Transformation Language (ATL), by improving mainly few quality metrics using a number of refactoring types. Objective: In this paper, we propose a novel set of quality attributes to evaluate refactored An programs based on the hierarchical quality model QMOOD. Method: We used the proposed quality attributes to guide the selection of the best refactorings to improve ATL programs using multi-objective search. Results: We validate our approach on a comprehensive dataset of model transformations. The statistical analysis of our experiments on 30 runs shows that our automated approach recommended useful refactorings based on a benchmark of An transformations and compared to random search, mono-objective search formulation, a previous work based on a different formulation of multi-objective search with few quality metrics, and a semi-automated refactoring approach not based on heuristic search. Conclusion: All these existing studies did not use our QMOOD adaptation for An which confirms the relevance of our quality attributes to guide the search for good refactoring suggestions.