Essential tasks for the verification of probabilistic programs include bounding expected outcomes and proving termination in finite expected runtime. We contribute a simple yet effective inductive synthesis approach for proving such quantitative reachability properties by generating inductive invariants on source-code level. Our implementation shows promise: It finds invariants for (in)finite-state programs, can beat state-of-the-art probabilistic model checkers, and is competitive with modern tools dedicated to invariant synthesis and expected runtime reasoning.
CITATION STYLE
Batz, K., Chen, M., Junges, S., Kaminski, B. L., Katoen, J. P., & Matheja, C. (2023). Probabilistic Program Verification via Inductive Synthesis of Inductive Invariants. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13994 LNCS, pp. 410–429). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-30820-8_25
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