Strüber, DanielRubin, JuliaArendt, ThorstenChechik, MarshaTaentzer, GabrielePlöger, JenniferJürjens, JanSchneider, Kurt2017-06-212017-06-212017978-3-88579-661-9We present a summary of our paper of the same title, published in the proceedings of the International Conference on Fundamental Approaches to Software Engineering (FASE) 2016. Unifying similar model transformation rules into variability-based ones can improve both the main- tainability 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 work, we propose a novel merging 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.enText/Conference Paper1617-5468