Affiliation:
1. Cornell University, USA
2. IIT Kanpur, India
Abstract
We propose a symbolic execution method for programs that can draw random samples. In contrast to existing work, our method can verify randomized programs with unknown inputs and can prove probabilistic properties that universally quantify over all possible inputs. Our technique augments standard symbolic execution with a new class of
probabilistic symbolic variables
, which represent the results of random draws, and computes symbolic expressions representing the probability of taking individual paths. We implement our method on top of the KLEE symbolic execution engine alongside multiple optimizations and use it to prove properties about probabilities and expected values for a range of challenging case studies written in C++, including Freivalds’ algorithm, randomized quicksort, and a randomized property-testing algorithm for monotonicity. We evaluate our method against Psi, an exact probabilistic symbolic inference engine, and Storm, a probabilistic model checker, and show that our method significantly outperforms both tools.
Publisher
Association for Computing Machinery (ACM)
Subject
Safety, Risk, Reliability and Quality,Software
Cited by
4 articles.
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3. A Theorem Proving Approach to Programming Language Semantics;2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET);2023-05
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