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Journal Articles Science of Computer Programming Year : 2023

BURST: Benchmarking Uniform Random Sampling Techniques

Abstract

BURST is a benchmarking platform for uniform random sampling (URS) techniques. Given: i) the description of a sampling space provided as a Boolean formula (DIMACS), and ii) a sampling budget (time and strength of uniformity), BURST evaluates ten samplers for scalability and uniformity. BURST measures scalability based on the time required to produce a sample, and uniformity based on the state-of-the-art and proven statistical test Barbarik. BURST is easily extendable to new samplers and offers: i) 128 feature models (for highly-configurable systems), ii) many other models mined from the artificial intelligence/satisfiability solving benchmarks. BURST envisions supporting URS assessment and design across multiple research communities.
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Dates and versions

hal-03897639 , version 1 (14-12-2022)

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Mathieu Acher, Gilles Perrouin, Maxime Cordy. BURST: Benchmarking Uniform Random Sampling Techniques. Science of Computer Programming, 2023, 226, pp.1-10. ⟨10.1016/j.scico.2022.102914⟩. ⟨hal-03897639⟩
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