  Context: The constant growth of primary evidence and Systematic Literature
Reviews (SLRs) publications in the Software Engineering (SE) field leads to the
need for SLR Updates. However, searching and selecting evidence for SLR updates
demands significant effort from SE researchers. Objective: We present emerging
results on an automated approach to support searching and selecting studies for
SLR updates in SE. Method: We developed an automated tool prototype to perform
the snowballing search technique and support selecting relevant studies for SLR
updates using Machine Learning (ML) algorithms. We evaluated our automation
proposition through a small-scale evaluation with a reliable dataset from an
SLR replication and its update. Results: Effectively automating
snowballing-based search strategies showed feasibility with minor losses,
specifically related to papers without Digital Object Identifier (DOI). The ML
algorithm giving the highest performance to select studies for SLR updates was
Linear Support Vector Machine, with approximately 74% recall and 15% precision.
Using such algorithms with conservative thresholds to minimize the risk of
missing papers can significantly reduce evidence selection efforts. Conclusion:
The preliminary results of our evaluation point in promising directions,
indicating the potential of automating snowballing search efforts and of
reducing the number of papers to be manually analyzed by about 2.5 times when
selecting evidence for updating SLRs in SE.
