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 …

Inferring loop invariants is one of the most challenging problems in program verification. It is
highly desired to incorporate machine learning when inferring. This paper presents a …

In software development, formal program specifications play a crucial role in various stages.
However, manually crafting formal program specifications is rather difficult, making the job …

Most of the existing smart contract symbolic execution tools perform analysis on bytecode,
which loses high-level semantic information presented in source code. This makes …

Loop invariant generation has long been a challenging problem. Black-box learning has
recently emerged as a promising method for inferring loop invariants. However, the …

We present a new technique for synthesizing dynamic, constraint-based visual layouts from
examples. Our technique tackles two major challenges of layout synthesis. First, realistic …

In the deductive verification of programs written in the imperative programming languages,
the generation and proving of the verification conditions corresponding to loops are of …

Smart contracts are computerized transaction protocols built on top of blockchain networks.
Users are charged with fees, aka gas in Ethereum, when they create, deploy or execute …

Loop invariant plays an important role in program analysis and verification. Equipping each
loop with a sound and useful invariant is a crucial step for full program verification and …

Termination is a fundamental liveness property for program verification. In this paper, we
revisit the problem of non-termination analysis and propose the first data-driven learning …