  When AI tools can generate many solutions, some human preference must be
applied to determine which solution is relevant to the current project. One way
to find those preferences is interactive search-based software engineering
(iSBSE) where humans can influence the search process. This paper argues that
when optimizing a model using human-in-the-loop, data mining methods such as
our SNEAK tool (that recurses into divisions of the data) perform better than
standard iSBSE methods (that mutates multiple candidate solutions over many
generations). For our case studies, SNEAK runs faster, asks fewer questions,
achieves better solutions (that are within 3% of the best solutions seen in our
sample space), and scales to large problems (in our experiments, models with
1000 variables can be explored with half a dozen interactions where, each time,
we ask only four questions). Accordingly, we recommend SNEAK as a baseline
against which future iSBSE work should be compared. To facilitate that, all our
scripts are online at https://github.com/ai-se/sneak.
