CDCL-inspired Word-level Learning for Bit-vector Constraint Solving

TL;DR

This paper introduces a novel conflict-driven, word-level learning approach for bit-vector constraint solving, enhancing the ability to perform word-level reasoning and decision procedures in software verification.

Contribution

It presents a new word-level learning method for QF_BV problems, moving beyond traditional bit-level approaches to better utilize word-level propagations.

Findings

Enables more efficient bit-vector constraint solving.

Leverages recent advances in word-level propagations.

Improves decision procedures for software verification.

Abstract

The theory of quantifier-free bit-vectors (QF_BV) is of paramount importance in software verification. The standard approach for satisfiability checking reduces the bit-vector problem to a Boolean problem, leveraging the powerful SAT solving techniques and their conflict-driven clause learning (CDCL) mechanisms. Yet, this bit-level approach loses the structure of the initial bit-vector problem. We propose a conflict-driven, word-level, combinable constraints learning for the theory of quantifier-free bit-vectors. This work paves the way to truly word-level decision procedures for bit-vectors, taking full advantage of word-level propagations recently designed in CP and SMT communities.