Bit-Vector Model Counting using Statistical Estimation

@inproceedings{Kim2018BitVectorMC,
  title={Bit-Vector Model Counting using Statistical Estimation},
  author={Seonmo Kim and Stephen McCamant},
  booktitle={TACAS},
  year={2018}
}
Approximate model counting for bit-vector SMT formulas (generalizing #SAT) has many applications such as probabilistic inference and quantitative information-flow security, but it is computationally difficult. Adding random parity constraints (XOR streamlining) and then checking satisfiability is an effective approximation technique, but it requires a prior hypothesis about the model count to produce useful results. We propose an approach inspired by statistical estimation to continually refine… CONTINUE READING
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