The Open-Source LearnLib - A Framework for Active Automata Learning

In this paper, we present LearnLib, a library for active automata learning. The current, open-source version of LearnLib was completely rewritten from scratch, incorporating the lessons learned from the decade-spanning development process of the previous versions of LearnLib. Like its immediate predecessor, the open-source LearnLib is written in Java to enable a high degree of flexibility and extensibility, while at the same time providing a performance that allows for large-scale applications. Additionally, LearnLib provides facilities for visualizing the progress of learning algorithms in detail, thus complementing its applicability in research and industrial contexts with an educational aspect. Open image in new window

[1]  Tiziana Margaria,et al.  Knowledge-based relevance filtering for efficient system-level test-based model generation , 2005, Innovations in Systems and Software Engineering.

[2]  Bengt Jonsson,et al.  Generating models of infinite-state communication protocols using regular inference with abstraction , 2015, Formal Methods Syst. Des..

[3]  Tiziana Margaria,et al.  Automata Learning with On-the-Fly Direct Hypothesis Construction , 2011, ISoLA Workshops.

[4]  Ferhat Khendek,et al.  Test Selection Based on Finite State Models , 1991, IEEE Trans. Software Eng..

[5]  Zvonimir Rakamaric,et al.  Hybrid learning: interface generation through static, dynamic, and symbolic analysis , 2013, ISSTA.

[6]  Tiziana Margaria,et al.  Next Generation LearnLib , 2011, TACAS.

[7]  Zvonimir Rakamaric,et al.  Symbolic Learning of Component Interfaces , 2012, SAS.

[8]  Falk Howar,et al.  Active learning of interface programs , 2012 .

[9]  Tsun S. Chow,et al.  Testing Software Design Modeled by Finite-State Machines , 1978, IEEE Transactions on Software Engineering.

[10]  Bernhard Steffen,et al.  Reusing System States by Active Learning Algorithms , 2011, EternalS@FET.

[11]  Bernhard Steffen,et al.  Introduction to Active Automata Learning from a Practical Perspective , 2011, SFM.

[12]  Mihalis Yannakakis,et al.  Black Box Checking , 1999, FORTE.

[13]  Pavol Cerný,et al.  Synthesis of interface specifications for Java classes , 2005, POPL '05.

[14]  Jun Sun,et al.  TzuYu: Learning stateful typestates , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[15]  Tiziana Margaria,et al.  LearnLib: a framework for extrapolating behavioral models , 2009, International Journal on Software Tools for Technology Transfer.

[16]  Nelma Moreira,et al.  Testing the Equivalence of Regular Languages , 2010, J. Autom. Lang. Comb..

[17]  Bernhard Steffen,et al.  Learning register automata: from languages to program structures , 2014, Machine Learning.

[18]  Bengt Jonsson,et al.  Inferring Canonical Register Automata , 2012, VMCAI.

[19]  Benedikt Bollig,et al.  libalf: The Automata Learning Framework , 2010, CAV.

[20]  Frits W. Vaandrager,et al.  Automata Learning through Counterexample Guided Abstraction Refinement , 2012, FM.

[21]  Roland Groz,et al.  Angluin style finite state machine inference with non-optimal counterexamples , 2010, MIIT '10.

[22]  Dawn Xiaodong Song,et al.  Inference and analysis of formal models of botnet command and control protocols , 2010, CCS '10.

[23]  Ronald L. Rivest,et al.  Inference of finite automata using homing sequences , 1989, STOC '89.

[24]  Ramon Janssen,et al.  Learning Fragments of the TCP Network Protocol , 2014, FMICS.

[25]  Hardi Hungar,et al.  Model Generation by Moderated Regular Extrapolation , 2002, FASE.

[26]  Bernhard Steffen,et al.  Active continuous quality control , 2013, CBSE '13.

[27]  Tiziana Margaria,et al.  The Teachers' Crowd: The Impact of Distributed Oracles on Active Automata Learning , 2011, ISoLA Workshops.

[28]  Bernhard Steffen,et al.  An Abstract Framework for Counterexample Analysis in Active Automata Learning , 2014, ICGI.

[29]  Bernhard Steffen,et al.  The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning , 2014, RV.

[30]  J. Hopcroft,et al.  A Linear Algorithm for Testing Equivalence of Finite Automata. , 1971 .

[31]  Bengt Jonsson,et al.  Learning Extended Finite State Machines , 2014, SEFM.

[32]  Zdenek Kotásek,et al.  Automatic Construction of On-line Checking Circuits Based on Finite Automata , 2014, 2014 17th Euromicro Conference on Digital System Design.

[33]  Joeri de Ruiter,et al.  Formal Models of Bank Cards for Free , 2013, 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation Workshops.

[34]  Amir Pnueli,et al.  On the learnability of infinitary regular sets , 1991, COLT '91.

[35]  Hardi Hungar,et al.  Domain-Specific Optimization in Automata Learning , 2003, CAV.

[36]  Dana Angluin,et al.  Learning Regular Sets from Queries and Counterexamples , 1987, Inf. Comput..

[37]  Colin de la Higuera,et al.  A bibliographical study of grammatical inference , 2005, Pattern Recognit..

[38]  Joeri de Ruiter,et al.  Automated Reverse Engineering using Lego® , 2014, WOOT.

[39]  George C. Necula,et al.  Guided GUI testing of android apps with minimal restart and approximate learning , 2013, OOPSLA.

[40]  Umesh V. Vazirani,et al.  An Introduction to Computational Learning Theory , 1994 .