  In presence of multiple objectives to be optimized in Search-Based Software
Engineering (SBSE), Pareto search has been commonly adopted. It searches for a
good approximation of the problem's Pareto optimal solutions, from which the
stakeholders choose the most preferred solution according to their preferences.
However, when clear preferences of the stakeholders (e.g., a set of weights
which reflect relative importance between objectives) are available prior to
the search, weighted search is believed to be the first choice since it
simplifies the search via converting the original multi-objective problem into
a single-objective one and enable the search to focus on what only the
stakeholders are interested in.
  This paper questions such a "weighted search first" belief. We show that the
weights can, in fact, be harmful to the search process even in the presence of
clear preferences. Specifically, we conduct a large scale empirical study which
consists of 38 systems/projects from three representative SBSE problems,
together with two types of search budget and nine sets of weights, leading to
604 cases of comparisons. Our key finding is that weighted search reaches a
certain level of solution quality by consuming relatively less resources at the
early stage of the search; however, Pareto search is at the majority of the
time (up to 77% of the cases) significantly better than its weighted
counterpart, as long as we allow a sufficient, but not unrealistic search
budget. This, together with other findings and actionable suggestions in the
paper, allows us to codify pragmatic and comprehensive guidance on choosing
weighted and Pareto search for SBSE under the circumstance that clear
preferences are available. All code and data can be accessed at:
https://github.com/ideas-labo/pareto-vs-weight-for-sbse.
