Uniform and Scalable SAT-Sampling for Configurable Systems
- Rubén Heradio
- David Fernandez-Amoros
- José A. Galindo
- David Felipe Benavides Cuevas
- Rafael Capilla (coord.)
- Maider Azanza (coord.)
- Miguel Rodríguez Luaces (coord.)
- María del Mar Roldán García (coord.)
- Loli Burgueño (coord.)
- José Raúl Romero (coord.)
- José Antonio Parejo Maestre (coord.)
- José Francisco Chicano García (coord.)
- Marcela Genero (coord.)
- Oscar Díaz (coord.)
- José González Enríquez (coord.)
- Mª Carmen Penadés Gramaje (coord.)
- Silvia Abrahão (col.)
Editorial: Sociedad de Ingeniería de Software y Tecnologías de Desarrollo de Software (SISTEDES)
Año de publicación: 2021
Congreso: Jornadas de Ingeniería del Software y Bases de Datos (JISBD) (25. 2021. Malaga)
Tipo: Aportación congreso
Resumen
Several 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 +AGAAYA-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.