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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.  ...  The proposed technique is able to find quantified invariants for loops from the Linux source, as well as for the benchmark code used in the previous works.  ...  and templates can automatically infer quantified loop invariants.  ... 
doi:10.1007/978-3-642-17164-2_23 fatcat:mai7ucfe5rah5poselwekpenzi

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.  ...  In our study, the proposed technique was able to find quantified invariants for loops from the Linux source and other realistic programs.  ...  Contribution -We prove that a simple combination of algorithmic learning, decision procedures, predicate abstraction and templates can automatically infer first-order loop invariants.  ... 
doi:10.1017/s0960129513000078 fatcat:opbe2lm32jf5fpfbyd2dj72iia

Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference [chapter]

Yungbum Jung, Wonchan Lee, Bow-Yaw Wang, Kwangkuen Yi
2011 Lecture Notes in Computer Science  
Our technique is able to improve the effectiveness and efficiency of the learning-based loop invariant inference algorithm in [14] .  ...  We address the predicate generation problem in the context of loop invariant inference.  ...  If this simple strategy does not yield necessary atomic predicates to express loop invariants, the loop invariant inference algorithm will not be able to infer a loop invariant.  ... 
doi:10.1007/978-3-642-19835-9_17 fatcat:gj5a53yv2zf4zfy7oj54w5mcta

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].  ...  We address the predicate generation problem in the context of loop invariant inference.  ...  If this simple strategy does not yield necessary atomic predicates to express any loop invariants the loop invariant inference algorithm will not be able to infer a loop invariant.  ... 
doi:10.2168/lmcs-8(3:25)2012 fatcat:mcsiiatm5jecvadahrwdi2eyoq

Path invariants

Dirk Beyer, Thomas A. Henzinger, Rupak Majumdar, Andrey Rybalchenko
2007 Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation - PLDI '07  
Specifically, we use constraint-based invariant generation to automatically infer invariants of path programs -so-called path invariants.  ...  by a path program.  ...  The first author is supported in part by the grant SFU/PRG, 06-3. The second author is supported in part by the Swiss National Science Foundation.  ... 
doi:10.1145/1250734.1250769 dblp:conf/pldi/BeyerHMR07 fatcat:h6pofhspy5atvis35jrovhhusi

Path invariants

Dirk Beyer, Thomas A. Henzinger, Rupak Majumdar, Andrey Rybalchenko
2007 SIGPLAN notices  
Specifically, we use constraint-based invariant generation to automatically infer invariants of path programs -so-called path invariants.  ...  by a path program.  ...  The first author is supported in part by the grant SFU/PRG, 06-3. The second author is supported in part by the Swiss National Science Foundation.  ... 
doi:10.1145/1273442.1250769 fatcat:ajrdlcefafe2zdvcvvvhxs4uge

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.  ...  Contribution • We prove that algorithmic learning, decision procedures, and predicate abstraction in combination can automatically infer invariants in propositional formulae for programs in our simple  ... 
doi:10.1007/978-3-642-11319-2_15 fatcat:zdcqbxfnxncvphqzheiax2qhs4

ICE: A Robust Framework for Learning Invariants [chapter]

Pranav Garg, Christof Löding, P. Madhusudan, Daniel Neider
2014 Lecture Notes in Computer Science  
We develop new strongly convergent ICE-learning algorithms for two domains, one for learning Boolean combinations of numerical invariants for scalar variables and one for quantified invariants for arrays  ...  We observe that existing algorithms for black-box abstract interpretation can be interpreted as ICE-learning algorithms.  ...  This work was partially funded by NSF CAREER award #0747041 and NSF Expeditions in Computing ExCAPE Award #1138994.  ... 
doi:10.1007/978-3-319-08867-9_5 fatcat:mx6bx2cywremddw2decrainf3e

Simplifying Loop Invariant Generation Using Splitter Predicates [chapter]

Rahul Sharma, Isil Dillig, Thomas Dillig, Alex Aiken
2011 Lecture Notes in Computer Science  
Our technique is conceptually simple, easy to implement, and can be integrated into any automatic loop invariant generator.  ...  We present a novel static analysis technique that substantially improves the quality of invariants inferred by standard loop invariant generation techniques.  ...  Acknowledgments We would like to thank Denis Gopan, Francesco Logozzo, and Sumit Gulwani for their helpful pointers to benchmarks and invariant generation tools.  ... 
doi:10.1007/978-3-642-22110-1_57 fatcat:tv4iyc6egnegrfk26g7lfydcmu

Safety Verification and Refutation by k-Invariants and k-Induction [chapter]

Martin Brain, Saurabh Joshi, Daniel Kroening, Peter Schrammel
2015 Lecture Notes in Computer Science  
Generalising Hoare logic's ideas of loop invariants, k-induction can prove true properties, and, in some cases provide counterexamples to false ones.  ...  For example, concrete counterexample generation in model checking, invariant inference in abstract interpretation and completeness via annotation for deductive verification.  ...  A new, unified, simple and elegant algorithm, kIkI, for integrated invariant inference and counterexample generation is presented in Sect. 2.  ... 
doi:10.1007/978-3-662-48288-9_9 fatcat:psozjas6trdvlemq2ti6ai7t6i

Dependent Array Type Inference from Tests [chapter]

He Zhu, Aditya V. Nori, Suresh Jagannathan
2015 Lecture Notes in Computer Science  
Second, without imposing an annotation burden for quantified invariants on array indices, we automatically infer useful array invariants by initially guessing very coarse invariant templates, using test  ...  These inferred invariants are subsequently encoded as dependent types for validation.  ...  Array Invariant Template Inference In guessT , our algorithm traverses wp and generates invariant templates (defined in Section 3) for each of its simple relational predicates Q(ā[ ];x)), denoted as p,  ... 
doi:10.1007/978-3-662-46081-8_23 fatcat:ys64anqtprhbbn2v73pifxd24e

Learning Invariants using Decision Trees [article]

Siddharth Krishna, Christian Puhrsch, Thomas Wies
2015 arXiv   pre-print
The algorithm is able to infer safe invariants for a range of challenging benchmarks and compares favorably to other ML-based invariant inference techniques.  ...  We have used our algorithm to verify C programs taken from the literature.  ...  Instead, the DT learner automatically infers those parameters from the sample data.  ... 
arXiv:1501.04725v1 fatcat:kkt6iuwsrfajbpvpisc24wb7zm

Finding Invariants of Distributed Systems: It's a Small (Enough) World After All

Travis Hance, Marijn Heule, Ruben Martins, Bryan Parno
2021 Symposium on Networked Systems Design and Implementation  
Central to our ability to search efficiently is our algorithm's ability to learn from counterexamples whenever a candidate fails to be invariant, allowing us to check the remaining candidates more efficiently  ...  Our system performs an exhaustive search within a given space of candidate invariants in order to find and verify inductive invariants which suffice to prove the safety condition.  ...  This work was funded in part by the Alfred P.  ... 
dblp:conf/nsdi/HanceHMP21 fatcat:4dgzlmqufvajthmazykh2fqeze

Plain and Simple Inductive Invariant Inference for Distributed Protocols in TLA+ [article]

William Schultz, Ian Dardik, Stavros Tripakis
2022 arXiv   pre-print
We present a new technique for automatically inferring inductive invariants of parameterized distributed protocols specified in TLA+.  ...  To achieve this, we present a new algorithm for invariant inference that is based around a core procedure for generating plain, potentially non-inductive lemma invariants that are used as candidate conjuncts  ...  Both fol-ic3 and IC3PO attempt to learn the quantifier structure itself during counterexample generalization, and can infer both universal and existentially quantified invariants.  ... 
arXiv:2205.06360v1 fatcat:nqjahyharbazdlhsjers77nvvi

Safety Verification and Refutation by k-invariants and k-induction (extended version) [article]

Martin Brain, Saurabh Joshi, Daniel Kroening, Peter Schrammel
2015 arXiv   pre-print
For example, concrete counterexample generation in model checking, invariant inference in abstract interpretation and completeness via annotation for deductive verification.  ...  This paper presents a single, unified algorithm kIkI, which strictly generalises abstract interpretation, bounded model checking and k-induction.  ...  A new, unified, simple and elegant algorithm, kIkI, for integrated invariant inference and counterexample generation is presented in Section 2.  ... 
arXiv:1506.05671v2 fatcat:6o4gl4p3zvappic7jtuwk6xeua
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