Bit-Precise Reasoning via Int-Blasting

Yoni Zohar, Ahmed Irfan, Makai Mann, Aina Niemetz, Andres Nötzli, Mathias Preiner, Andrew Reynolds, Clark Barrett, Cesare Tinelli

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The state of the art for bit-precise reasoning in the context of Satisfiability Modulo Theories (SMT) is a SAT-based technique called bit-blasting where the input formula is first simplified and then translated to an equisatisfiable propositional formula. The main limitation of this technique is scalability, especially in the presence of large bit-widths and arithmetic operators. We introduce an alternative technique, which we call int-blasting, based on a translation to an extension of integer arithmetic rather than propositional logic. We present several translations, discuss their differences, and evaluate them on benchmarks that arise from the verification of rewrite rule candidates for bit-vector solving, as well as benchmarks from SMT-LIB. We also provide preliminary results on 35 benchmarks that arise from smart contract verification. The evaluation shows that this technique is particularly useful for benchmarks with large bit-widths and can solve benchmarks that the state of the art cannot.

Original languageEnglish
Title of host publicationVerification, Model Checking, and Abstract Interpretation - 23rd International Conference, VMCAI 2022, Proceedings
EditorsBernd Finkbeiner, Thomas Wies
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages23
ISBN (Print)9783030945824
StatePublished - 2022
Event23rd International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2022 - Philadelphia, United States
Duration: 16 Jan 202218 Jan 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13182 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2022
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This work was supported in part by DARPA (awards N66001-18-C-4012, FA8650-18-2-7854 and FA8650-18-2-7861), ONR (award N68335-17-C-0558), the Stanford Center for Blockchain Research, Certora Inc., and by an NSF Graduate Fellowship (to Makai Mann). A. Irfan—This author’s contributions were made while he was a postdoc at Stanford University.

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.


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