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Deriving Invariants by Algorithmic Learning, Decision Procedures, and Predicate Abstraction
[chapter]
2010
Lecture Notes in Computer Science
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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
2014
Mathematical Structures in Computer Science
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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
2011
2011 IEEE/IFIP 41st International Conference on Dependable Systems & Networks (DSN)
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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
2021
Zenodo
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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]
2015
arXiv
pre-print
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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)
2014
Dagstuhl Reports
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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]
2014
Lecture Notes in Computer Science
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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
2016
Formal methods in system design
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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]
2010
Lecture Notes in Computer Science
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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]
2012
Lecture Notes in Computer Science
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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
2008
Proceedings of the 2008 international symposium on Software testing and analysis - ISSTA '08
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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
2007
Logical Methods in Computer Science
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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]
2014
Lecture Notes in Computer Science
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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
2012
Logical Methods in Computer Science
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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
2009
International Journal on Software Tools for Technology Transfer (STTT)
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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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