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Deriving Invariants by Algorithmic Learning, Decision Procedures, and Predicate Abstraction [chapter]

Yungbum Jung, Soonho Kong, Bow-Yaw Wang, Kwangkeun Yi
2010 Lecture Notes in Computer Science  
By combining algorithmic learning, decision procedures, and predicate abstraction, we present an automated technique for finding loop invariants in propositional formulae.  ...  Given invariant approximations derived from pre-and post-conditions, our new technique exploits the flexibility in invariants by a simple randomized mechanism.  ...  Acknowledgment We would like to thank Wontae Choi, Deokhwan Kim, Will Klieber, Sasa Misailovic, Bruno Oliveira, Corneliu Popeea, Hongseok Yang, and Karen Zee for their detailed comments and helpful suggestions.We  ... 
doi:10.1007/978-3-642-11319-2_15 fatcat:zdcqbxfnxncvphqzheiax2qhs4

Automatically inferring loop invariants via algorithmic learning

YUNGBUM JUNG, SOONHO KONG, CRISTINA DAVID, BOW-YAW WANG, KWANGKEUN YI
2014 Mathematical Structures in Computer Science  
By combining algorithmic learning, decision procedures, predicate abstraction and simple templates for quantified formulae, we present an automated technique for finding loop invariants.  ...  and exploit the flexibility in invariants by a simple randomized mechanism.  ...  We show that the four technologies (algorithmic learning, decision procedures, predicate abstraction and simple templates) can be arranged in concert to derive loop invariants in first-order (or, quantified  ... 
doi:10.1017/s0960129513000078 fatcat:opbe2lm32jf5fpfbyd2dj72iia

A methodology for the generation of efficient error detection mechanisms

Matthew Leeke, Saima Arif, Arshad Jhumka, Sarabjot Singh Anand
2011 2011 IEEE/IFIP 41st International Conference on Dependable Systems & Networks (DSN)  
Here, a symbolic pattern learning algorithm, such as decision tree induction or rule induction, is chosen in order to derive and evaluate a first-order predicate over the variables whose values were captured  ...  The reason for choosing symbolic machine learning algorithms is because symbolic learning algorithms learn concepts by constructing a symbolic expression (such as a decision tree) that describes a class  ... 
doi:10.1109/dsn.2011.5958204 dblp:conf/dsn/LeekeAJA11 fatcat:wa5y7o3bq5artjxka6rudyhkxq

On Symmetry and Quantification: A New Approach to Verify Distributed Protocols

Aman Goel, Karem A. Sakallah
2021 Zenodo  
We propose symmetric incremental induction, an extension of the finite-domain IC3/PDR algorithm, that automatically derives the required quantified inductive invariant by exploiting the connection between  ...  of clause learning during incremental induction.  ...  -A quantifier inference procedure that expresses ϕ's orbit by an automaticallyderived compact quantified predicate Φ.  ... 
doi:10.5281/zenodo.4641704 fatcat:ln5my5563fce7c52pcp2ilsklu

Learning Invariants using Decision Trees [article]

Siddharth Krishna, Christian Puhrsch, Thomas Wies
2015 arXiv   pre-print
In this paper, we propose a new algorithm that uses decision trees to learn candidate invariants in the form of arbitrary Boolean combinations of numerical inequalities.  ...  The algorithm is able to infer safe invariants for a range of challenging benchmarks and compares favorably to other ML-based invariant inference techniques.  ...  In particular, the algorithm proposed in [28] only learns formulas that fall into a finite abstract domain (Boolean combinations of a given finite set of predicates), whereas we use decision trees to  ... 
arXiv:1501.04725v1 fatcat:kkt6iuwsrfajbpvpisc24wb7zm

Decision Procedures and Abstract Interpretation (Dagstuhl Seminar 14351)

Daniel Kroening, Thomas W. Reps, Sanjit A. Seshia, Aditya Thakur, Marc Herbstritt
2014 Dagstuhl Reports  
The seminar brought together practitioners and reseachers in abstract interpretation and decision procedures.  ...  This report documents the program and the outcomes of Dagstuhl Seminar 14351 "Decision Procedures and Abstract Interpretation".  ...  The second is the use of abstract interpretation to support decision procedures better (e. g., by reverseengineering existing decision procedures to identify uses of abstract domains, which allows them  ... 
doi:10.4230/dagrep.4.8.89 dblp:journals/dagstuhl-reports/KroeningRST14 fatcat:krpfysvrdjfidnjxnrxp77w7yu

From Invariant Checking to Invariant Inference Using Randomized Search [chapter]

Rahul Sharma, Alex Aiken
2014 Lecture Notes in Computer Science  
Given a checker and a language of possible invariants, c2i generates an inference procedure that iteratively invokes two phases.  ...  We describe a general framework c2i for generating an invariant inference procedure from an invariant checking procedure.  ...  This work was supported by NSF grant CCF-1160904, a Microsoft fellowship, and the Air Force Research Laboratory under agreement number FA8750-12-2-0020. The U.S.  ... 
doi:10.1007/978-3-319-08867-9_6 fatcat:f4apdyguw5h2zp6ykwtq5ug2ma

From invariant checking to invariant inference using randomized search

Rahul Sharma, Alex Aiken
2016 Formal methods in system design  
Given a checker and a language of possible invariants, c2i generates an inference procedure that iteratively invokes two phases.  ...  We describe a general framework c2i for generating an invariant inference procedure from an invariant checking procedure.  ...  This work was supported by NSF grant CCF-1160904, a Microsoft fellowship, and the Air Force Research Laboratory under agreement number FA8750-12-2-0020. The U.S.  ... 
doi:10.1007/s10703-016-0248-5 fatcat:re4evfgprbhtvmscgp26ak4vdy

Automatically Inferring Quantified Loop Invariants by Algorithmic Learning from Simple Templates [chapter]

Soonho Kong, Yungbum Jung, Cristina David, Bow-Yaw Wang, Kwangkeun Yi
2010 Lecture Notes in Computer Science  
By combining algorithmic learning, decision procedures, predicate abstraction, and simple templates, we present an automated technique for finding quantified loop invariants.  ...  Our technique can find arbitrary first-order invariants (modulo a fixed set of atomic propositions and an underlying SMT solver) in the form of the given template and exploits the flexibility in invariants  ...  Acknowledgment We are grateful to Wontae Choi, Suwon Jang, Will Klieber, Wonchan Lee, Ben Lickly, Bruno Oliveira, and Sungwoo Park for their detailed comments and helpful suggestions.  ... 
doi:10.1007/978-3-642-17164-2_23 fatcat:mai7ucfe5rah5poselwekpenzi

Termination Analysis with Algorithmic Learning [chapter]

Wonchan Lee, Bow-Yaw Wang, Kwangkeun Yi
2012 Lecture Notes in Computer Science  
The new technique combines transition predicate abstraction, algorithmic learning, and decision procedures to compute transition invariants as proofs of program termination.  ...  An algorithmic-learning-based termination analysis technique is presented.  ...  We would like to thank anonymous referees for their comments and appreciations.  ... 
doi:10.1007/978-3-642-31424-7_12 fatcat:wtbl7ehk5re37o5ea5pgs75vti

Dynamic inference of likely data preconditions over predicates by tree learning

Sriram Sankaranarayanan, Swarat Chaudhuri, Franjo Ivančić, Aarti Gupta
2008 Proceedings of the 2008 international symposium on Software testing and analysis - ISSTA '08  
Given a procedure and a set of predicates over its inputs, our technique enumerates different truth assignments to the predicates, deriving test cases from each feasible truth assignment.  ...  The predicates themselves are derived automatically using simple heuristics.  ...  It can be derived by using machine learning techniques discussed later.  ... 
doi:10.1145/1390630.1390666 dblp:conf/issta/SankaranarayananCIG08 fatcat:lqxl43hcx5a2vfnnupkbjtlcc4

Predicate Abstraction via Symbolic Decision Procedures

Shuvendu Lahiri, Thomas Ball, Byron Cook, Sriram Rajamani
2007 Logical Methods in Computer Science  
The result of the symbolic decision procedure is a shared expression (represented by a directed acyclic graph) that implicitly represents the answer to a predicate abstraction query.  ...  We present a new approach for performing predicate abstraction based on symbolic decision procedures.  ...  Symbolic Decision Procedures (SDP) We now show how to perform predicate abstraction using symbolic decision procedures.  ... 
doi:10.2168/lmcs-3(2:1)2007 fatcat:it5omjmevffgrebsp7xs4d64v4

Lazy Annotation Revisited [chapter]

Kenneth L. McMillan
2014 Lecture Notes in Computer Science  
The resulting algorithm is compared both conceptually and experimentally to two approaches based on similar principles but using different learning strategies: unfolding-based Bounded Model Checking and  ...  When the search backtracks, the program is annotated with a learned fact that constrains future search.  ...  The author would like to thank Akash Lal for assistance in using SDV and corral.  ... 
doi:10.1007/978-3-319-08867-9_16 fatcat:ma7r5ihrjzcdhpbgfwnueyz33u

Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference

Wonchan Lee, Yungbum Jung, Bow-yaw Wang, Kwangkuen Yi, Parosh Abdulla
2012 Logical Methods in Computer Science  
Our technique is able to improve the effectiveness and efficiency of the learning-based loop invariant inference algorithm in [14].  ...  Motivated by the interpolation-based abstraction refinement technique, we apply the interpolation theorem to synthesize predicates implicitly implied by program texts.  ...  The technique infers the transition invariant of a given loop as a proof of termination, by combining algorithmic learning and decision procedures.  ... 
doi:10.2168/lmcs-8(3:25)2012 fatcat:mcsiiatm5jecvadahrwdi2eyoq

Approximating the safely reusable set of learned facts

Domagoj Babić, Alan J. Hu
2009 International Journal on Software Tools for Technology Transfer (STTT)  
In this paper, we formalize the notion of shared structure among verification conditions, and propose a novel and efficient approach to exploit this sharing by safely reusing facts learned while checking  ...  Experimental results show that this approach can improve the performance of verification, even on path-and context-sensitive and dataflowintensive properties.  ...  Let n be some node in a maximally-shared graph and ψ an invariant derived by the decision procedure of the form n = constant. We shall say that n is fixed by the decision procedure.  ... 
doi:10.1007/s10009-009-0117-2 fatcat:od2zbd3oo5fc7irdskvmbr4rua
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