Heradio, RubenFernández Amorós, DavidGalindo Duarte, José ÁngelBenavides Cuevas, David Felipe2021-11-032021-11-032020Heradio, R., Fernández Amorós, D., Galindo Duarte, J.Á. y Benavides Cuevas, D.F. (2020). Uniform and scalable SAT-sampling for configurable systems. En SPLC 2020: 24th International Systems and Software Product Line Conference (1-11), Montreal, Quebec, Canada: Association for Computing Machinery (ACM).978-1-4503-7569-6https://hdl.handle.net/11441/127024Several relevant analyses on configurable software systems remain intractable because they require examining vast and highly-constrained configuration spaces. Those analyses could be addressed through statistical inference, i.e., working with a much more tractable sample that later supports generalizing the results obtained to the entire configuration space. To make this possible, the laws of statistical inference impose an indispensable requirement: each member of the population must be equally likely to be included in the sample, i.e., the sampling process needs to be "uniform". Various SAT-samplers have been developed for generating uniform random samples at a reasonable computational cost. Unfortunately, there is a lack of experimental validation over large configuration models to show whether the samplers indeed produce genuine uniform samples or not. This paper (i) presents a new statistical test to verify to what extent samplers accomplish uniformity and (ii) reports the evaluation of four state-of-the-art samplers: Spur, QuickSampler, Unigen2, and Smarch. According to our experimental results, only Spur satisfies both scalability and uniformity.application/pdf23engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1145/3382025.3414951