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

Zakaria Chihani (1) , François Bobot (1) , Sébastien Bardin (1)

1 LSL - Laboratoire Sûreté des Logiciels

The theory of quantifier-free bitvectors is of paramount importance in software verification. The standard approach for satisfiability checking reduces the bitvector 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 bitvector problem. We propose a conflict-driven, word-level, combinable constraints learning for the theory of quantifier-free bitvectors. This work paves the way to truly word-level decision procedures for bitvectors, taking full advantage of word-level propagations recently designed in CP and SMT communities.

Mots clés: Verification & Validation; SMT SOLVING; Constraint Programming CP; Bit vector

Domaines: Logique en informatique [cs.LO]

Zakaria Chihani, François Bobot, Sébastien Bardin. CDCL-inspired Word-level Learning for Bit-vector Constraint Solving. 2017. ⟨hal-01531336⟩