LearnLib: A framework for extrapolating behavioral models

Harald Raffelt; Bernhard Steffen; Therese Berg; Tiziana Margaria

doi:10.1007/s10009-009-0111-8

LearnLib: A framework for extrapolating behavioral models

Harald Raffelt, Bernhard Steffen, Therese Berg, Tiziana Margaria

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, we present the LearnLib, a library of tools for automata learning, which is explicitly designed for the systematic experimental analysis of the profile of available learning algorithms and corresponding optimizations. Its modular structure allows users to configure their own tailored learning scenarios, which exploit specific properties of their envisioned applications. As has been shown earlier, exploiting application-specific structural features enables optimizations that may lead to performance gains of several orders of magnitude, a necessary precondition to make automata learning applicable to realistic scenarios.

Original language	English
Pages (from-to)	393-407
Number of pages	15
Journal	International Journal on Software Tools for Technology Transfer
Volume	11
Issue number	5
DOIs	https://doi.org/10.1007/s10009-009-0111-8
Publication status	Published - 2009
Externally published	Yes

Keywords

Automata learning
Domain-specific optimization
Experimentation
Grammar inference
Software library

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10.1007/s10009-009-0111-8

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KW - Domain-specific optimization

KW - Experimentation

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LearnLib: A framework for extrapolating behavioral models

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Keywords

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