Oh, JehoBatory, DonHeradio Gil, Rubén2024-06-112024-06-112023-11-231049-331X; eISSN: 1049-331Xhttps://doi.org/10.1145/3611663https://hdl.handle.net/20.500.14468/22384A Software Product Line (SPL) is a family of similar programs. Each program is defined by a unique set of features, called a configuration, that satisfies all feature constraints. “What configuration achieves the best performance for a given workload?” is the SPLOptimization (SPLO) challenge. SPLO is daunting: just 80 unconstrained features yield 1024 unique configurations, which equals the estimated number of stars in the universe. We explain (a) how uniform random sampling and random search algorithms solve SPLO more efficiently and accurately than current machine-learned performance models and (b) how to compute statistical guarantees on the quality of a returned configuration; i.e., it is within x% of optimal with y% confidence.enAtribución-NoComercial-SinDerivadas 4.0 Internacionalinfo:eu-repo/semantics/openAccessartículosoftware product linesconfiguration optimizationproduct spacesmachine learninguniform random samplingrandom searchorder statistics