'Uninterpreted functions' presentation slideshows

Satisfiability Modulo Theories: An Appetizer SBMF 2009 - Gramado

1.4k views • 117 slides

SMT@Microsoft Midwest Verification Day, Iowa, 2009

1.29k views • 115 slides

SMT and Its Application in Software Verification

SMT and Its Application in Software Verification. Yu-Fang Chen IIS, Academia Sinica Based on the slides of Barrett, Sanjit , Kroening , Rummer , Sinha , Jhala , and Majumdar. Assertion in C. Useful tool for debugging. Can be used to describe pre- and post-conditions of a function.

1.04k views • 94 slides

STP: A Decision Procedure for Bit-vectors and Arrays

David L. Dill Stanford University. STP: A Decision Procedure for Bit-vectors and Arrays. Software analysis tools present unique challenges for decision procedures. Theories must match programming language semantics Operations are on bit-vectors, not integers

539 views • 38 slides

Random Interpretation

Random Interpretation. Sumit Gulwani. UC-Berkeley. Program Analysis. Applications in all aspects of software development, e.g. Program correctness Compiler optimizations Translation validation Parameters Completeness (precision, no false positives) Computational complexity

423 views • 30 slides

Path-Sensitive Analysis for Linear Arithmetic and Uninterpreted Functions

Path-Sensitive Analysis for Linear Arithmetic and Uninterpreted Functions. SAS 2004 Sumit Gulwani George Necula EECS Department University of California, Berkeley. Example. All 3 asserts are true. False. True. a=2?. y := a; z := 2;. y := 2; z := a;. True. False. a=2?.

358 views • 25 slides

The SMT solver Z3 Lecture 3, 2012

697 views • 24 slides

Precise Interprocedural Analysis using Random Interpretation

Precise Interprocedural Analysis using Random Interpretation. Sumit Gulwani George Necula UC-Berkeley. Random Interpretation. = Random Testing + Abstract Interpretation Almost as simple as random testing but better soundness guarantees.

374 views • 23 slides

Synthesis, Analysis, and Verification Lecture 04c

325 views • 23 slides

Propositional Encoding - Decision Procedure

Propositional Encoding - Decision Procedure. Daniel Kroening , Ofer Strichman Presented by Changki Hong. Combination of theories. Approach 1 : Combine decision procedures of the individual theories. Nelson- Oppen method

325 views • 22 slides

Cover Algorithms and Their Combination

Cover Algorithms and Their Combination. Sumit Gulwani, Madan Musuvathi Microsoft Research, Redmond. Cover Definition. Cover operation is useful for simplifying a formula by discarding facts related to a set of variables Given A quantifier-free formula ? in theory T A set of symbols V

276 views • 20 slides

Cover Algorithms and Their Combination

Cover Algorithms and Their Combination. Sumit Gulwani, Madan Musuvathi Microsoft Research, Redmond. Cover Definition. Cover operation is useful for simplifying a formula by discarding facts related to a set of variables Given A quantifier-free formula ? in theory T A set of symbols V

248 views • 20 slides

Combining Abstract Interpreters

Combining Abstract Interpreters. Ashish Tiwari SRI. Sumit Gulwani Microsoft Research Redmond, Group. R A D. Motivation. a 1 := 0; a 2 := 0; b 1 := 1; b 2 := F(1); c 1 := 2; c 2 := 2;. a 1 := a 1 +1; a 2 := a 2 +2; b 1 := F(b 1 ); b 2 := F(b 2 );

279 views • 19 slides

Recursive Proofs for Inductive Tree Data-Structures

Recursive Proofs for Inductive Tree Data-Structures. xiaokang qiu with P. Madhusudan Andrei Stefanescu University of Illinois at Urbana-Champaign POPL 2012, Philadelphia, PA, USA. functional verification of heap-manipulating programs. Expressive. keep expressiveness.

291 views • 19 slides

Assertion Checking over Combined Abstraction of Linear Arithmetic and Uninterpreted Functions

Assertion Checking over Combined Abstraction of Linear Arithmetic and Uninterpreted Functions. Sumit Gulwani Microsoft Research, Redmond Ashish Tiwari SRI. Precision of combined abstraction. a 1 := 0; a 2 := 0; b 1 := 1; b 2 := F(1); c 1 := 2; c 2 := 2;.

279 views • 18 slides

Path-Sensitive Analysis for Linear Arithmetic and Uninterpreted Functions

Path-Sensitive Analysis for Linear Arithmetic and Uninterpreted Functions. SAS 2004 Sumit Gulwani George Necula EECS Department University of California, Berkeley. Example. All 3 asserts are true. False. True. a=2?. x := a; y := 2;. x := 2; y := a;. True. False. a=2?.

291 views • 17 slides

Join Algorithms for the Theory of Uninterpreted Functions

Join Algorithms for the Theory of Uninterpreted Functions. Sumit Gulwani Ashish Tiwari George Necula UC-Berkeley SRI UC-Berkeley. Definition: Join in theory T. E = Join T ( E 1 , E 2 ) iff E 1 ) T E and E 2 ) T E

318 views • 16 slides

Join Algorithms for the Theory of Uninterpreted Functions

Join Algorithms for the Theory of Uninterpreted Functions. Sumit Gulwani Ashish Tiwari George Necula UC-Berkeley SRI UC-Berkeley. Definition: Join in theory T. E = Join T ( E 1 , E 2 ) iff E 1 ) T E and E 2 ) T E

221 views • 16 slides

Join Algorithms for the Theory of Uninterpreted Functions

Join Algorithms for the Theory of Uninterpreted Functions. Sumit Gulwani Ashish Tiwari George Necula UC-Berkeley SRI UC-Berkeley. Definition: Join in theory T. E = Join T ( E 1 , E 2 ) iff E 1 ) T E and E 2 ) T E

236 views • 16 slides

Decision Procedures in First Order Logic

Decision Procedures in First Order Logic. Decision Procedures for Equality Logic. Outline. Introduction Definition, complexity Reducing Uninterpreted Functions to Equality Logic Using Uninterpreted Functions in proofs Simplifications Introduction to the decision procedures

175 views • 9 slides