Abstractcvc5 is the latest SMT solver in the cooperating validity checker series and builds on the successful code base of CVC4. This paper serves as a comprehensive system description of cvc5 ’s architectural design and highlights the major features and components introduced since CVC4 1.8. We evaluate cvc5 ’s performance on all benchmarks in SMT-LIB and provide a comparison against CVC4 and Z3.
Symbolic model checking is an important tool for finding bugs (or proving the absence of bugs) in modern system designs. Because of this, improving the ease of use, scalability, and performance of model checking tools and algorithms continues to be an important research direction. In service of this goal, we present , an open-source SMT-based model checker. is designed to be both a research platform for developing and improving model checking algorithms, as well as a performance-competitive tool that can be used for academic and industry verification applications. In addition to performance, prioritizes transparency (developed as an open-source project on GitHub), flexibility ( can be adapted to a variety of tasks by exploiting its general SMT-based interface), and extensibility (it is easy to add new algorithms and new back-end solvers). In this paper, we describe the design of the tool with a focus on the flexible and extensible architecture, cover its current capabilities, and demonstrate that is competitive with state-of-the-art tools.
We develop a framework for model checking infinite-state systems by automatically augmenting them with auxiliary variables, enabling quantifier-free induction proofs for systems that would otherwise require quantified invariants. We combine this mechanism with a counterexample-guided abstraction refinement scheme for the theory of arrays. Our framework can thus, in many cases, reduce inductive reasoning with quantifiers and arrays to quantifier-free and array-free reasoning. We evaluate the approach on a wide set of benchmarks from the literature. The results show that our implementation often outperforms state-of-the-art tools, demonstrating its practical potential.
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