A Visual Analysis Approach to Update Systematic Reviews
This addresses the issue of outdated SRs for researchers and practitioners, but it is incremental as it builds on existing visual text mining techniques.
The paper tackles the problem of updating systematic reviews (SRs), which is a time-consuming manual task, by proposing USR-VTM, a visual text mining approach that increases the number of correctly included studies compared to the traditional method.
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 Thus, many SRs have not been updated as they should be and, therefore, they are currently outdated.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.