Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for
deployment in a wide range of applications, such as autonomous systems, medical …

Inductive invariants can be robustly synthesized using a learning model where the teacher is
a program verifier who instructs the learner through concrete program configurations …

We introduce ICE, a robust learning paradigm for synthesizing invariants, that learns using
examples, counter-examples, and implications, and show that it admits honest teachers and …

The rise in efficiency of Satisfiability Modulo Theories (SMT) solvers has created numerous
uses for them in software verification, program synthesis, functional programming …

Program proving can be viewed as solving for unknown relations (such as loop invariants,
procedure summaries and so on) that occur in the logical verification conditions of a …

Quantified first-order formulas, often with quantifier alternations, are increasingly used in the
verification of complex systems. While automated theorem provers for first-order logic are …

Real-life programs contain multiple operations whose semantics are unavailable to
verification engines, like third-party library calls, inline assembly and SIMD instructions …

Abstract interpretation is a sound-by-construction method for program verification: any
erroneous program will raise some alarm. However, the verification of correct programs may …

Typical CEGAR-based verification methods refine the abstract domain based on full
counterexample traces. The finite state model checking algorithm IC3 introduced the …

Satisfiability modulo theory (SMT) consists in testing the satisfiability of first-order formulas
over linear integer or real arithmetic, or other theories. In this survey, we explain the …