Research at the intersection of machine learning, programming languages, and software
engineering has recently taken important steps in proposing learnable probabilistic models …

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

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

This paper presents an approach of safety verification based on neural networks for
continuous dynamical systems which are modeled as a system of ordinary differential …

Loop invariants are fundamental to reasoning about programs with loops. They establish
properties about a given loop's behavior. When they additionally are inductive, they become …

We design learning algorithms for synthesizing invariants using Horn implication
counterexamples (Horn-ICE), extending the ICE-learning model. In particular, we describe a …

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

We introduce a new dynamic analysis technique to discover invariants in separation logic for
heap-manipulating programs. First, we use a debugger to obtain rich program execution …

The cost of maintaining formally specified and verified software is widely considered
prohibitively high due to the need to constantly keep code and the proofs of its correctness in …

We introduce a novel approach to the automated termination analysis of computer
programs: we use neural networks to represent ranking functions. Ranking functions map …