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Probabilistic software analysis aims at quantifying the probability of atarget event occurring during the execution of a program processing uncertainincoming data or written itself using probabilistic programming constructs.Recent techniques combine symbolic execution with model counting or solutionspace quantification methods to obtain accurate estimates of the occurrenceprobability of rare target events, such as failures in a mission-criticalsystem. However, they face several scalability and applicability limitationswhen analyzing software processing with high-dimensional and correlatedmultivariate input distributions. In this paper, we present SYMbolic ParallelAdaptive Importance Sampling (SYMPAIS), a new inference method tailored toanalyze path conditions generated from the symbolic execution of programs withhigh-dimensional, correlated input distributions. SYMPAIS combines results fromimportance sampling and constraint solving to produce accurate estimates of thesatisfaction probability for a broad class of constraints that cannot beanalyzed by current solution space quantification methods. We demonstrateSYMPAIS's generality and performance compared with state-of-the-artalternatives on a set of problems from different application domains.

Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis