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Rahul Sharma and Alex Aiken (Stanford University) 1.

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Presentation on theme: "Rahul Sharma and Alex Aiken (Stanford University) 1."— Presentation transcript:

1 Rahul Sharma and Alex Aiken (Stanford University) 1

2 x = i; y = j; while y!=0 do x = x-1; y = y-1; if( i==j ) assert x==0 2

3 NumericalArrays Heap Strings PLDI08-1 PLDI08-2 PLDI08-3 PLDI08-4 synergy-1 synergy-2 TACAS06 NECLA-1 NECLA-2 NECLA-3 SVCOMP-1 SVCOMP-2 SVCOMP-3 SVCOMP-4 monniaux nested init init-nc init-p init-e 2darray copy copy-p copy-o reverse swap d-swap strcpy strlen memcpy find find-n append merge alloc-f alloc-nf delete delete-all find filter last reverse length replace index substring 3

4 assume P while B do S assert Q Find a valuation of unknown predicates that makes the verification conditions (VCs) valid 4

5 Is it possible to have a general search procedure? 5

6  (Domain-specific) Checker + (General) Search = Inference  To obtain an invariant inference engine  Instantiate the search with a search space  An SMT solver to check 6

7  A generally applicable randomized search  Numerical, array, linked lists, and strings  Competitive performance with specialized approaches 7

8  Markov Chain Monte Carlo (MCMC) sampling  The only known tractable solution method for high dimensional irregular search spaces [andrieu 03][chenney 00] 8

9 9 73 47 42 29 37 17 23

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12 I gb s t Efficient to evaluate Incremental feedback 12

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20  Operations that intermix strings and integers  length(s), indexOf(s1, s2), substr(s1, i1, i2), …  Search space: Boolean combinations of predicates  Z3-Str (FSE’13) for check 20

21  Static invariant inference is a hard problem, made easier by separating search and check  Search based techniques can work  Competitive with other methods  Easier to retarget to new domains  Future work, scale MCMC to full program proofs 21

22  Pranav Garg, Christof Löding, P. Madhusudan, Daniel Neider: ICE: A Robust Framework for Learning Invariants. CAV 2014  Shachar Itzhaky, Nikolaj Bjørner, Thomas W. Reps, Mooly Sagiv, Aditya V. Thakur: Property-Directed Shape Analysis. CAV 2014  Rajeev Alur, Rastislav Bodík, Garvit Juniwal, Milo M. K. Martin, Mukund Raghothaman, Sanjit A. Seshia, Rishabh Singh, Armando Solar-Lezama, Emina Torlak, Abhishek Udupa: Syntax-guided synthesis. FMCAD 2013  Ashutosh Gupta, Rupak Majumdar, Andrey Rybalchenko: From tests to proofs. STTT 15(4) (2013)  Yungbum Jung, Soonho Kong, Bow-Yaw Wang, Kwangkeun Yi: Deriving Invariants by Algorithmic Learning, Decision Procedures, and Predicate Abstraction. VMCAI 2010  Sumit Gulwani, Nebojsa Jojic: Program verification as probabilistic inference. POPL 2007: 277-289 22


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