(SMT) solvers is becoming an attractive alternative to traditional algorithmic test generation methods, especially when testingīoolean expressions. In the context of automatic test generation, the use of propositional satisfiability (SAT) and Satisfiability Modulo Theories The technique also lets us model count formulas over floating-point constraints, which we demonstrate with an application to a vulnerability in differential privacy mechanisms. Experimental results show that the implementation is faster than the most similar previous approaches which used simpler refinement strategies. We implement this approach, with an approximate probability model, as a wrapper around an off-the-shelf SMT solver or SAT solver. We propose an approach inspired by statistical estimation to continually refine a probabilistic estimate of the model count for a formula, so that each XOR-streamlined query yields as much information as possible. 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. The technique also lets us model count formulas over floating-point constraints, which we demonstrate with an application to a vulnerability in differential privacy mechanisms.Īpproximate 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.
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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.