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Armando Solar-Lezama. Inala, Jeevana Priya Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2016-12-22T15:18:22Z 2016-12-22T15:18:22Z 2016 2016 http://hdl.handle.net/1721.1/106008 965799843 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 61-66). SMT solvers are at the heart of a number of software engineering tools. These SMT solvers use a SAT solver as the back-end and convert the high-level constraints given by the user down to low-level boolean formulas that can be efficiently mapped to CNF clauses and fed into a SAT solver. Current SMT solvers are designed to be general purpose solvers that are suited to a wide range of problems. However, SAT solvers are very non-deterministic and hence, it is difficult to optimize a general purpose solver across all different problems. In this thesis, we propose a system that can automatically generate parts of SMT solvers in a way that is tailored to particular problem domains. In particular, we target the translation from high-level constraints to CNF clauses which is one of the crucial parts of all SMT solvers. We achieve this goal by using a combination of program synthesis and machine learning techniques. We use a program synthesis tool called Sketch to generate optimal encoding rules for this translation and then use auto-tuning to only select the subset of these encodings that actually improve the performance for a particular class of problems. Using this technique, the thesis shows that we can improve upon the basic encoding strategy used by CVC4 (a state of the art SMT solver). We can automatically generate variants of the solver tailored to different domains of problems represented in the bit-vector benchmark suite from the SMT competition 2015. by Jeevana Priya Inala. M.Eng. 66 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Synthesis of domain specific CNF encoders for bit-vector solvers Synthesis of domain specific Clause Normal Form encoders for bit-vector solvers Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a413a5bc-92e4-442e-b84d-7e8e67df2f5a"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Synthesis of domain specific CNF encoders for bit-vector solvers</Title> <Subtitle>Synthesis of domain specific Clause Normal Form encoders for bit-vector solvers</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2016</PublicationDate> <Authors> <Author> <DisplayName>Inala, Jeevana Priya</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>SMT solvers are at the heart of a number of software engineering tools. These SMT solvers use a SAT solver as the back-end and convert the high-level constraints given by the user down to low-level boolean formulas that can be efficiently mapped to CNF clauses and fed into a SAT solver. Current SMT solvers are designed to be general purpose solvers that are suited to a wide range of problems. However, SAT solvers are very non-deterministic and hence, it is difficult to optimize a general purpose solver across all different problems. In this thesis, we propose a system that can automatically generate parts of SMT solvers in a way that is tailored to particular problem domains. In particular, we target the translation from high-level constraints to CNF clauses which is one of the crucial parts of all SMT solvers. We achieve this goal by using a combination of program synthesis and machine learning techniques. We use a program synthesis tool called Sketch to generate optimal encoding rules for this translation and then use auto-tuning to only select the subset of these encodings that actually improve the performance for a particular class of problems. Using this technique, the thesis shows that we can improve upon the basic encoding strategy used by CVC4 (a state of the art SMT solver). We can automatically generate variants of the solver tailored to different domains of problems represented in the bit-vector benchmark suite from the SMT competition 2015.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>