Persona:
Heradio Gil, Rubén

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0000-0002-7131-0482
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Heradio Gil
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Rubén
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2022 - 20232
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Autor: Batory, Don ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Finding Near-optimal Configurations in Colossal Spaces with Statistical Guarantees
    (Association for Computing Machinery (ACM), 2023-11-23) Oh, Jeho; Batory, Don; Heradio Gil, Rubén
    A 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.
  • Miniatura
    Publicación
    Uniform and scalable sampling of highly configurable systems
    (Springer, 2022-01-21) Galindo, José A.; Benavides, David; Batory, Don; Heradio Gil, Rubén; Fernández Amoros, David José
    Many analyses on configurable software systems are intractable when confronted with colossal and highly-constrained configuration 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 five other state-of-the-art samplers: KUS, QuickSampler, Smarch, Spur, and Unigen2. Our experimental results show only BDDSampler satisfies both scalability and uniformity.