1 research outputs found
Uniform and scalable sampling of highly configurable systems
Many analyses on confgurable software systems are intractable when confronted with
colossal and highly-constrained confguration spaces. These analyses could instead use
statistical inference, where a tractable sample accurately predicts results for the entire
space. To do so, the laws of statistical inference requires each member of the population
to be equally likely to be included in the sample, i.e., the sampling process needs to be
“uniform”. SAT-samplers have been developed to generate uniform random samples at a
reasonable computational cost. However, there is a lack of experimental validation over
colossal spaces to show whether the samplers indeed produce uniform samples or not. This
paper (i) proposes a new sampler named BDDSampler, (ii) presents a new statistical test
to verify sampler uniformity, and (iii) reports the evaluation of BDDSampler and fve
other state-of-the-art samplers: KUS, QuickSampler, Smarch, Spur, and Unigen2. Our
experimental results show only BDDSampler satisfes both scalability and uniformity.Universidad Nacional de Educación a Distancia (UNED) OPTIVAC 096-034091 2021V/PUNED/008Ministerio de Ciencia, Innovación y Universidades RTI2018-101204-B-C22 (OPHELIA)Comunidad Autónoma de Madrid ROBOCITY2030-DIH-CM S2018/NMT-4331Agencia Estatal de Investigación TIN2017-90644-RED