  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. Current iSBSE methods
can lead to cognitive fatigue (when they overwhelm humans with too many overly
elaborate questions). WHUN is an iSBSE algorithm that avoids that problem. Due
to its recursive clustering procedure, WHUN only pesters humans for
$O(log_2{N})$ interactions. Further, each interaction is mediated via a feature
selection procedure that reduces the number of asked questions. When compared
to prior state-of-the-art iSBSE systems, WHUN runs faster, asks fewer
questions, and achieves better solutions that are within $0.1\%$ of the best
solutions seen in our sample space. More importantly, WHUN 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 WHUN 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/whun.
