Flexible Proof Production in an Industrial-Strength SMT Solver

Haniel Barbosa; Andrew Reynolds; Gereon Kremer; Hanna Lachnitt; Aina Niemetz; Andres Nötzli; Alex Ozdemir; Mathias Preiner; Arjun Viswanathan; Scott Viteri; Yoni Zohar; Cesare Tinelli; Clark Barrett

doi:10.1007/978-3-031-10769-6_3

Flexible Proof Production in an Industrial-Strength SMT Solver

Haniel Barbosa, Andrew Reynolds, Gereon Kremer, Hanna Lachnitt, Aina Niemetz, Andres Nötzli, Alex Ozdemir, Mathias Preiner, Arjun Viswanathan, Scott Viteri, Yoni Zohar, Cesare Tinelli, Clark Barrett

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

Abstract

Proof production for SMT solvers is paramount to ensure their correctness independently from implementations, which are often prohibitively difficult to verify. Historically, however, SMT proof production has struggled with performance and coverage issues, resulting in the disabling of many crucial solving techniques and in coarse-grained (and thus hard to check) proofs. We present a flexible proof-production architecture designed to handle the complexity of versatile, industrial-strength SMT solvers and show how we leverage it to produce detailed proofs, including for components previously unsupported by any solver. The architecture allows proofs to be produced modularly, lazily, and with numerous safeguards for correctness. This architecture has been implemented in the state-of-the-art SMT solver cvc5. We evaluate its proofs for SMT-LIB benchmarks and show that the new architecture produces better coverage than previous approaches, has acceptable performance overhead, and supports detailed proofs for most solving components.

Original language	English
Title of host publication	Automated Reasoning - 11th International Joint Conference, IJCAR 2022, Proceedings
Editors	Jasmin Blanchette, Laura Kovács, Dirk Pattinson
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	15-35
Number of pages	21
ISBN (Print)	9783031107689
DOIs	https://doi.org/10.1007/978-3-031-10769-6_3
State	Published - 2022
Event	11th International Joint Conference on Automated Reasoning, IJCAR 2022, part of the Federated Logic Conference, FLoC 2022 - Haifa, Israel Duration: 8 Aug 2022 → 10 Aug 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13385 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Joint Conference on Automated Reasoning, IJCAR 2022, part of the Federated Logic Conference, FLoC 2022
Country/Territory	Israel
City	Haifa
Period	8/08/22 → 10/08/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

Funding

This work was partially supported by the Office of Naval Research (Contract No. 68335-17-C-0558), a gift from Amazon Web Services, and by NSF-BSF grant numbers 2110397 (NSF) and 2020704 (BSF).

Funders	Funder number
NSF-BSF	2020704, 2110397
Office of Naval Research	68335-17-C-0558
United States-Israel Binational Science Foundation

Access to Document

10.1007/978-3-031-10769-6_3

Cite this

Barbosa, H., Reynolds, A., Kremer, G., Lachnitt, H., Niemetz, A., Nötzli, A., Ozdemir, A., Preiner, M., Viswanathan, A., Viteri, S., Zohar, Y., Tinelli, C., & Barrett, C. (2022). Flexible Proof Production in an Industrial-Strength SMT Solver. In J. Blanchette, L. Kovács, & D. Pattinson (Eds.), Automated Reasoning - 11th International Joint Conference, IJCAR 2022, Proceedings (pp. 15-35). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13385 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-10769-6_3

Barbosa, Haniel ; Reynolds, Andrew ; Kremer, Gereon et al. / Flexible Proof Production in an Industrial-Strength SMT Solver. Automated Reasoning - 11th International Joint Conference, IJCAR 2022, Proceedings. editor / Jasmin Blanchette ; Laura Kovács ; Dirk Pattinson. Springer Science and Business Media Deutschland GmbH, 2022. pp. 15-35 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Barbosa, H, Reynolds, A, Kremer, G, Lachnitt, H, Niemetz, A, Nötzli, A, Ozdemir, A, Preiner, M, Viswanathan, A, Viteri, S, Zohar, Y, Tinelli, C & Barrett, C 2022, Flexible Proof Production in an Industrial-Strength SMT Solver. in J Blanchette, L Kovács & D Pattinson (eds), Automated Reasoning - 11th International Joint Conference, IJCAR 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13385 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 15-35, 11th International Joint Conference on Automated Reasoning, IJCAR 2022, part of the Federated Logic Conference, FLoC 2022, Haifa, Israel, 8/08/22. https://doi.org/10.1007/978-3-031-10769-6_3

Flexible Proof Production in an Industrial-Strength SMT Solver. / Barbosa, Haniel; Reynolds, Andrew; Kremer, Gereon et al.
Automated Reasoning - 11th International Joint Conference, IJCAR 2022, Proceedings. ed. / Jasmin Blanchette; Laura Kovács; Dirk Pattinson. Springer Science and Business Media Deutschland GmbH, 2022. p. 15-35 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13385 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Barbosa H, Reynolds A, Kremer G, Lachnitt H, Niemetz A, Nötzli A et al. Flexible Proof Production in an Industrial-Strength SMT Solver. In Blanchette J, Kovács L, Pattinson D, editors, Automated Reasoning - 11th International Joint Conference, IJCAR 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 15-35. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-10769-6_3