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 …

We propose a method for automatically finding refinement types of higher-order function
programs. Our method is an extension of the Ice framework of Garg et al. for finding …

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

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 …

We present a technique for automatically generating features for data-driven program
analyses. Recently data-driven approaches for building a program analysis have been …

Programmers often leverage data structure libraries that provide useful and reusable
abstractions. Modular verification of programs that make use of these libraries naturally rely …

We present a data-driven verification framework to automatically prove memory safety of
heap-manipulating programs. Our core contribution is a novel statistical machine learning …

Knowing the shapes of dynamic data structures is key when formally reasoning about
pointer programs. While modern shape analysis tools employ symbolic execution and …

Static analyses aim at inferring semantic properties of programs. While many analyses
compute an over-approximation of reachable states, some analyses compute a description …

Refinement types enable lightweight verification of functional programs. Algorithms for
statically inferring refinement types typically work by reduction to solving systems of …