3 research outputs found
A Deductive Verification Infrastructure for Probabilistic Programs
This paper presents a quantitative program verification infrastructure for discrete probabilistic programs. Our infrastructure can be viewed as the probabilistic analogue of Boogie: its central components are an intermediate verification language (IVL) together with a real-valued logic. Our IVL provides a programming-language-style for expressing verification conditions whose validity implies the correctness of a program under investigation. As our focus is on verifying quantitative properties such as bounds on expected outcomes, expected run-times, or termination probabilities, off-the-shelf IVLs based on Boolean first-order logic do not suffice. Instead, a paradigm shift from the standard Boolean to a real-valued domain is required.
Our IVL features quantitative generalizations of standard verification constructs such as assume- and assert-statements. Verification conditions are generated by a weakest-precondition-style semantics, based on our real-valued logic. We show that our verification infrastructure supports natural encodings of numerous verification techniques from the literature. With our SMT-based implementation, we automatically verify a variety of benchmarks. To the best of our knowledge, this establishes the first deductive verification infrastructure for expectation-based reasoning about probabilistic programs
A Deductive Verification Infrastructure for Probabilistic Programs
This paper presents a quantitative program verification infrastructure for
discrete probabilistic programs. Our infrastructure can be viewed as the
probabilistic analogue of Boogie: its central components are an intermediate
verification language (IVL) together with a real-valued logic. Our IVL provides
a programming-language-style for expressing verification conditions whose
validity implies the correctness of a program under investigation. As our focus
is on verifying quantitative properties such as bounds on expected outcomes,
expected run-times, or termination probabilities, off-the-shelf IVLs based on
Boolean first-order logic do not suffice. Instead, a paradigm shift from the
standard Boolean to a real-valued domain is required.
Our IVL features quantitative generalizations of standard verification
constructs such as assume- and assert-statements. Verification conditions are
generated by a weakest-precondition-style semantics, based on our real-valued
logic. We show that our verification infrastructure supports natural encodings
of numerous verification techniques from the literature. With our SMT-based
implementation, we automatically verify a variety of benchmarks. To the best of
our knowledge, this establishes the first deductive verification infrastructure
for expectation-based reasoning about probabilistic programs