RuleMerger: Automatic Construction of Variability-Based Model Transformation Rules

Abstract. Unifying similar model transformation rules into variabilitybased ones can improve both the maintainability and the performance of a model transformation system. Yet, manual identification and unification of such similar rules is a tedious and error-prone task. In this paper, we propose a novel merge-refactoring approach for automating this task. The approach employs clone detection for identifying overlapping rule portions and clustering for selecting groups of rules to be unified. Our instantiation of the approach harnesses state-of-the-art clone detection and clustering techniques and includes a specialized merge construction algorithm. We formally prove correctness of the approach and demonstrate its ability to produce high-quality outcomes in two real-life case-studies.

• Type: Journal
• Year: 2016
• Where: FASE
• Authors: Daniel Strüber 0001, Julia Rubin, Thorsten Arendt, Marsha Chechik, Gabriele Taentzer, Jennifer Plöger
• Keywords: FASE 2016, Software Engineering