  In recent years, significant advances have been made in the design and
analysis of fully dynamic maximal matching algorithms. However, these
theoretical results have received very little attention from the practical
perspective. Few of the algorithms are implemented and tested on real datasets,
and their practical potential is far from understood. In this paper, we attempt
to bridge the gap between theory and practice that is currently observed for
the fully dynamic maximal matching problem. We engineer several algorithms and
empirically study those algorithms on an extensive set of dynamic instances.
