A fundamental problem in program verification concerns inferring loop invariants. The
problem is undecidable and even practical instances are challenging. Inspired by how …

Deep neural networks are revolutionizing the way complex systems are developed.
However, these automatically-generated networks are opaque to humans, making it difficult …

The first attempts to apply the k-induction method to software verification are only recent. In
this paper, we present a novel proof by induction algorithm, which is built on the top of a …

We show how interpolants can be viewed as classifiers in supervised machine learning.
This view has several advantages: First, we are able to use off-the-shelf classification …

Loops are challenging structures for program analysis, especially when loops contain
multiple paths with complex interleaving executions among these paths. In this paper, we …

While model checking safety of infinite-state systems by inferring state invariants has
steadily improved recently, most verification tools still rely on a technique based on bounded …

Infinite-state systems such as distributed protocols are challenging to verify using interactive
theorem provers or automatic verification tools. Of these techniques, deductive verification is …

Automatic loop-invariant generation is important in program analysis and verification. In this
paper, we propose to generate loop-invariants automatically through learning and …

Many software model checkers only detect counterexamples with deep loops after exploring
numerous spurious and increasingly longer counterexamples. We propose a technique that …

In program verification, one method for reasoning about loops is to convert them into sets of
recurrences, and then try to solve these recurrences by computing their closed-form …