  Context: In order to preserve the value of Systematic Reviews (SRs), they
should be frequently updated considering new evidence that has been produced
since the completion of the previous version of the reviews. However, the
update of an SR is a time consuming, manual task. Thus, many SRs have not been
updated as they should be and, therefore, they are currently outdated.
Objective: The main contribution of this paper is to support the update of SRs.
Method: We propose USR-VTM, an approach based on Visual Text Mining (VTM)
techniques, to support selection of new evidence in the form of primary
studies. We then present a tool, named Revis, which supports our approach.
Finally, we evaluate our approach through a comparison of outcomes achieved
using USR-VTM versus the traditional (manual) approach. Results: Our results
show that USR-VTM increases the number of studies correctly included compared
to the traditional approach. Conclusions: USR-VTM effectively supports the
update of SRs.
