  Search-based software testing (SBST) has been studied a lot in the
literature, lately. Since, in theory, the performance of meta-heuristic search
methods are highly dependent on their parameters, there is a need to study SBST
tuning. In this study, we partially replicate a previous paper on SBST tool
tuning and revisit some of the claims of that paper. In particular, unlike the
previous work, our results show that the tuning impact is very limited to only
a small portion of the classes in a project. We also argue the choice of
evaluation metric in the previous paper and show that even for the impacted
classes by tuning, the practical difference between the best and an average
configuration is minor. Finally, we will exhaustively explore the search space
of hyper-parameters and show that half of the studied configurations perform
the same or better than the baseline paper's default configuration.
