Probabilistic Program Verification via Inductive Synthesis of Inductive Invariants


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.

In TACAS 2023
Mingshuai Chen
Mingshuai Chen
ZJU100 Young Professor

My research interests include formal verification, programming theory, and logical aspects of computer science.