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@inproceedings{SGCM22,
  title={On Quantitative Testing of Uniform Samplers},
  author={
    Soos, Mate and Golia, Priyanka and Chakraborty, Sourav and Meel, Kuldeep S.
  },
  booktitle=CP,
  year={2022},
  bib2html_rescat={Sampling,Distribution Testing},
  bib2html_pubtype={Refereed Conference},
  month=aug,
  bib2html_dl_pdf={../Papers/cp22.pdf},
  abstract={
    The problem of uniform sampling is, given a formula F , sample solutions of
    F uniformly atrandom from the solution space of F .
    Uniform sampling is a fundamental problem with widespread applications,
    including configuration testing, bug synthesis, function synthesis, and many
    more. State-of-the-art approaches for uniform sampling have a trade-off
    between scalability and theoretical guarantees. Many state of the art
    uniform samplers do not provide any theoretical guarantees on the
    distribution of samples generated, however, empirically they have shown
    promising results. In such cases, the main challenge is to test whether the
    distribution according to which samples are generated is indeed uniform or
    not. Recently, Chakraborty and Meel (2019)
    designed the first scalable sampling tester, Barbarik, based on a grey-box
    sampling technique for testing if the distribution, according to which the
    given sampler is sampling, is close to the uniform or far from uniform. They
    were able to show that many off-the-self samplers are far from a uniform
    sampler. The availability of Barbarik increased the test-driven development
    of samplers. More recently, Golia, Soos, Chakraborty and Meel (2021),
    designed a uniform like sampler, CMSGen, which was shown to be accepted by
    Barbarik on all the instances. However, CMSGen does not provide any
    theoretical analysis of the sampling quality.
    CMSGen leads us to observe the need for a tester to provide a quantitative
    answer to determine the quality of underlying samplers instead of merely a
    qualitative answer of Accept or Reject. Towards this goal, we design a
    computational hardness-based tester ScalBarbarik that provides a more
    nuanced analysis of the quality of a sampler. ScalBarbarik allows more
    expressive measurement of the quality of the underlying samplers. We
    empirically show that the state-of-the-art sampler, CMSGen is not accepted
    as a uniform-like sampler by ScalBarbarik. Furthermore, we show that
    ScalBarbarik can be used to design a sampler that can achieve balance
    between scalability and uniformity.
  },
}