Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis
Wed 25 Aug 2021 20:20 - 20:30 - Testing—Approximations in Testing and Analysis Chair(s): Shane McIntosh
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.
Wed 25 AugDisplayed time zone: Athens change
08:00 - 09:00 | Testing—Approximations in Testing and AnalysisResearch Papers +12h Chair(s): Mike Papadakis University of Luxembourg | ||
08:00 10mPaper | Skeletal Approximation Enumeration for SMT Solver Testing Research Papers Peisen Yao Hong Kong University of Science and Technology, Heqing Huang Hong Kong University of Science and Technology, Wensheng Tang Hong Kong University of Science and Technology, Qingkai Shi Purdue University, Rongxin Wu Xiamen University, Charles Zhang Hong Kong University of Science and Technology DOI | ||
08:10 10mPaper | Boosting Static Analysis Accuracy with Instrumented Test Executions Research Papers Tianyi Chen University of Southern California, Kihong Heo KAIST, Mukund Raghothaman University of Southern California DOI | ||
08:20 10mPaper | Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis Research Papers Yicheng Luo University College London, Antonio Filieri Imperial College London, Yuan Zhou University of Oxford DOI | ||
08:30 30mLive Q&A | Q&A (Testing—Approximations in Testing and Analysis) Research Papers | ||
20:00 - 21:00 | Testing—Approximations in Testing and AnalysisResearch Papers Chair(s): Shane McIntosh McGill University | ||
20:00 10mPaper | Skeletal Approximation Enumeration for SMT Solver Testing Research Papers Peisen Yao Hong Kong University of Science and Technology, Heqing Huang Hong Kong University of Science and Technology, Wensheng Tang Hong Kong University of Science and Technology, Qingkai Shi Purdue University, Rongxin Wu Xiamen University, Charles Zhang Hong Kong University of Science and Technology DOI | ||
20:10 10mPaper | Boosting Static Analysis Accuracy with Instrumented Test Executions Research Papers Tianyi Chen University of Southern California, Kihong Heo KAIST, Mukund Raghothaman University of Southern California DOI | ||
20:20 10mPaper | Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis Research Papers Yicheng Luo University College London, Antonio Filieri Imperial College London, Yuan Zhou University of Oxford DOI | ||
20:30 30mLive Q&A | Q&A (Testing—Approximations in Testing and Analysis) Research Papers | ||