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In presence of multiple objectives to be optimized in Search-Based SoftwareEngineering (SBSE), Pareto search has been commonly adopted. It searches for agood approximation of the problem's Pareto optimal solutions, from which thestakeholders choose the most preferred solution according to their preferences.However, when clear preferences of the stakeholders (e.g., a set of weightswhich reflect relative importance between objectives) are available prior tothe search, weighted search is believed to be the first choice since itsimplifies the search via converting the original multi-objective problem intoa single-objective one and enable the search to focus on what only thestakeholders are interested in.  This paper questions such a "weighted search first" belief. We show that theweights can, in fact, be harmful to the search process even in the presence ofclear preferences. Specifically, we conduct a large scale empirical study whichconsists of 38 systems/projects from three representative SBSE problems,together with two types of search budget and nine sets of weights, leading to604 cases of comparisons. Our key finding is that weighted search reaches acertain level of solution quality by consuming relatively less resources at theearly stage of the search; however, Pareto search is at the majority of thetime (up to 77% of the cases) significantly better than its weightedcounterpart, as long as we allow a sufficient, but not unrealistic searchbudget. This, together with other findings and actionable suggestions in thepaper, allows us to codify pragmatic and comprehensive guidance on choosingweighted and Pareto search for SBSE under the circumstance that clearpreferences are available. All code and data can be accessed at:https://github.com/ideas-labo/pareto-vs-weight-for-sbse.

The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering