LearnLib: A framework for extrapolating behavioral models

被引：53

作者：

Raffelt H. ^{[1
]}

Steffen B. ^{[1
]}

Berg T. ^{[2
]}

Margaria T. ^{[3
]}

机构：

[1] Department of Programming Systems, TU Dortmund, 44227 Dortmund

[2] Department of Information Technology, Uppsala University

[3] Department of Services and Software Engineering, Universität Potsdam, 14482 Potsdam

来源：

International Journal on Software Tools for Technology Transfer | 2009年 / 11卷 / 5期

关键词：

Automata learning; Domain-specific optimization; Experimentation; Grammar inference; Software library;

D O I：

10.1007/s10009-009-0111-8

中图分类号：

学科分类号：

摘要：

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. © Springer-Verlag 2009.

引用

页码：393 / 407

页数：14

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