Identifying invariants is an important program analysis task with applications towards
program understanding, bug finding, vulnerability analysis, and formal verification. Existing …

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 …

In recent years, the notion of local robustness (or robustness for short) has emerged as a
desirable property of deep neural networks. Intuitively, robustness means that small …

We extend the data-driven approach to inferring preconditions for code from a set of test
executions. Prior work requires a fixed set of features, atomic predicates that define the …

In recent years they have been numerous works that aim to automate relational verification.
Meanwhile, although Constrained Horn Clauses (CHCs CHCs) empower a wide range of …

Recent advances in deep learning have enabled data-driven controller design for
autonomous systems. However, verifying safety of such controllers, which are often hard-to …

We describe a Guess-and-Check algorithm for computing algebraic equation invariants of
the form∧ ifi (x 1,…, xn)= 0, where each fi is a polynomial over the variables x 1,…, xn of the …

We describe a general framework c2i for generating an invariant inference procedure from
an invariant checking procedure. Given a checker and a language of possible invariants, c2i …

We present a data-driven technique to solve Constrained Horn Clauses (CHCs) that encode
verification conditions of programs containing unconstrained loops and recursions. Our CHC …