Abstraction refinement via inductive learning

Alexey Loginov; Thomas Reps; Mooly Sagiv

doi:10.1007/11513988_50

Abstraction refinement via inductive learning

Alexey Loginov, Thomas Reps, Mooly Sagiv

School of Computer Science

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper concerns how to automatically create abstractions for program analysis. We show that inductive learning, the goal of which is to identify general rules from a set of observed instances, provides new leverage on the problem. An advantage of an approach based on inductive learning is that it does not require the use of a theorem prover.

Original language	English
Pages (from-to)	519-533
Number of pages	15
Journal	Lecture Notes in Computer Science
Volume	3576
DOIs	https://doi.org/10.1007/11513988_50
State	Published - 2005
Event	17th International Conference on Computer Aided Verification, CAV 2005 - Edinburgh, Scotland, United Kingdom Duration: 6 Jul 2005 → 10 Jul 2005

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10.1007/11513988_50

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title = "Abstraction refinement via inductive learning",

abstract = "This paper concerns how to automatically create abstractions for program analysis. We show that inductive learning, the goal of which is to identify general rules from a set of observed instances, provides new leverage on the problem. An advantage of an approach based on inductive learning is that it does not require the use of a theorem prover.",

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AB - This paper concerns how to automatically create abstractions for program analysis. We show that inductive learning, the goal of which is to identify general rules from a set of observed instances, provides new leverage on the problem. An advantage of an approach based on inductive learning is that it does not require the use of a theorem prover.

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M3 - מאמר מכנס

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JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

Y2 - 6 July 2005 through 10 July 2005

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Abstraction refinement via inductive learning

Abstract

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