Program Verification via Predicate Constraint Satisfiability Modulo Theories

TL;DR

This paper introduces a new verification framework using predicate Constraint Satisfaction Problems (pCSP) that generalizes existing methods and can encode complex verification problems like termination and modal mu-calculus model checking.

Contribution

It proposes a novel class of predicate CSPs, extends CLP with muCLP for richer expressiveness, and develops a sound, complete reduction and a stratified CEGIS-based solving method.

Findings

Implemented framework shows promising results on diverse verification problems.

pCSP can encode a wide range of verification tasks including termination and modal mu-calculus.

The approach outperforms previous CHC-based frameworks in expressiveness and efficiency.

Abstract

This paper presents a verification framework based on a new class of predicate Constraint Satisfaction Problems called pCSP where constraints are represented as clauses modulo first-order theories over function variables and predicate variables that may represent well-founded predicates. The verification framework generalizes an existing one based on Constrained Horn Clauses (CHCs) to arbitrary clauses, function variables, and well-foundedness constraints. While it is known that the satisfiability of CHCs and the validity of queries for Constrained Logic Programs (CLP) are inter-reducible, we show that, thanks to the added expressiveness, pCSP is expressive enough to express muCLP queries. muCLP itself is a new extension of CLP that we propose in this paper. It extends CLP with arbitrarily nested inductive and co-inductive predicates and is equi-expressive as first-order fixpoint logic. We show that muCLP can naturally encode a wide variety of verification problems including but not limited to termination/non-termination verification and even full modal mu-calculus model checking of programs written in various languages. To establish our verification framework, we present (1) a sound and complete reduction algorithm from muCLP to pCSP and (2) a constraint solving method for pCSP based on stratified CounterExample-Guided Inductive Synthesis (CEGIS) of (co-)inductive invariants, ranking functions, and Skolem functions witnessing existential quantifiers. Stratified CEGIS combines CEGIS with stratified families of templates to achieve relative completeness and faster and stable convergence of CEGIS by avoiding the overfitting problem. We have implemented the proposed framework and obtained promising results on diverse verification problems that are beyond the scope of the previous verification frameworks based on CHCs.