Detecting complex changes and refactorings during (Meta)model evolution

In this paper, we propose a detection engine of complex changes that simultaneously addresses these two challenges of variability and overlap. We introduce three ranking heuristics to help users to decide which overlapping complex changes are likely to be correct. In our approach, we record the trace of atomic changes rather than computing them with the difference between the original and evolved metamodel. Thus, we have a complete and an ordered sequence of atomic changes without hidden changes. Furthermore, we consider the issue of undo operations (i.e. change canceling actions) while recording the sequence of atomic changes, and we illustrate how we cope with it. We validate our approach on 8 real case studies demonstrating its feasibility and its applicability. We observe that a full recall is always reached in all case studies and an average precision of 70.75%. The precision is improved by the heuristics up to 91% and 100% in some cases.