  Probabilistic software analysis aims at quantifying the probability of a
target event occurring during the execution of a program processing uncertain
incoming data or written itself using probabilistic programming constructs.
Recent techniques combine symbolic execution with model counting or solution
space quantification methods to obtain accurate estimates of the occurrence
probability of rare target events, such as failures in a mission-critical
system. However, they face several scalability and applicability limitations
when analyzing software processing with high-dimensional and correlated
multivariate input distributions. In this paper, we present SYMbolic Parallel
Adaptive Importance Sampling (SYMPAIS), a new inference method tailored to
analyze path conditions generated from the symbolic execution of programs with
high-dimensional, correlated input distributions. SYMPAIS combines results from
importance sampling and constraint solving to produce accurate estimates of the
satisfaction probability for a broad class of constraints that cannot be
analyzed by current solution space quantification methods. We demonstrate
SYMPAIS's generality and performance compared with state-of-the-art
alternatives on a set of problems from different application domains.
