Benchmarks for Automata Learning and Conformance Testing

Neider, Daniel; Smetsers, Rick; Vaandrager, Frits; Kuppens, Harco

doi:10.1007/978-3-030-22348-9_23

Cited by 34 publications

(24 citation statements)

References 55 publications

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“…In the experiments we used 241 real FSMs from a benchmark [63]. We also used randomly generated FSMs, with this providing us with a larger set of subjects and also the ability to explore how performance changes as we vary FSM properties such as the alphabet size.…”

Section: Empirical Evaluationmentioning

confidence: 99%

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Using mutual information to test from Finite State Machines: Test suite selection

Ibias

Núñez

Hierons

2021

Information and Software Technology

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Section: Empirical Evaluationmentioning

confidence: 99%

“…Set1 A benchmark of 335 FSMs recently collected [63]. From this, we selected the 241 deterministic FSMs.…”

Section: Experimental Subjectsmentioning

confidence: 99%

Using mutual information to test from Finite State Machines: Test suite selection

Ibias

Núñez

Hierons

2021

Information and Software Technology

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“…We have used stubbed versions of the Python FIFO-Queue and Set modules 4 for learning the FIFO and Set models, while the Combination Lock automata were constructed manually. Source code for all other models was obtained by translating existing benchmarks from [18] (see also automata.cs.ru.nl) to Python code. We also utilise a 'reset' operation: A 'reset' operation brings an SUT back to its initial state, and is counted as an 'input' for our purposes.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Grey-Box Learning of Register Automata

Garhewal

Vaandrager

Howar

et al. 2020

Lecture Notes in Computer Science

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Article 25fa pilot End User AgreementThis publication is distributed under the terms of Article 25fa of the Dutch Copyright Act (Auteurswet) with explicit consent by the author. Dutch law entitles the maker of a short scientific work funded either wholly or partially by Dutch public funds to make that work publicly available for no consideration following a reasonable period of time after the work was first published, provided that clear reference is made to the source of the first publication of the work.This publication is distributed under The Association of Universities in the Netherlands (VSNU) 'Article 25fa implementation' pilot project. In this pilot research outputs of researchers employed by Dutch Universities that comply with the legal requirements of Article 25fa of the Dutch Copyright Act are distributed online and free of cost or other barriers in institutional repositories. Research outputs are distributed six months after their first online publication in the original published version and with proper attribution to the source of the original publication.You are permitted to download and use the publication for personal purposes. All rights remain with the author(s) and/or copyrights owner(s) of this work. Any use of the publication other than authorised under this licence or copyright law is prohibited.If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons.

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“…Following Angluin's original algorithm, several algorithmic improvements have been proposed [30,22,20] and implemented in learning tools [21,5]. Active learning has been successfully combined with model-checking and testing [35,28] and used in promising case studies [9,18,32] (see also [27] for a list of applications).…”

Section: Introductionmentioning

confidence: 99%

Active Learning for Sound Negotiations

Muscholl¹,

Walukiewicz²

2021

Preprint

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We present two active learning algorithms for sound deterministic negotiations. Sound deterministic negotiations are models of distributed systems, a kind of Petri nets or Zielonka automata with additional structure. We show that this additional structure allows to minimize such negotiations. The two active learning algorithms differ in the type of membership queries they use. Both have similar complexity to Angluin's L * algorithm, in particular, the number of queries is polynomial in the size of the negotiation, and not in the number of configurations.

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Benchmarks for Automata Learning and Conformance Testing

Cited by 34 publications

References 55 publications

Using mutual information to test from Finite State Machines: Test suite selection

Using mutual information to test from Finite State Machines: Test suite selection

Grey-Box Learning of Register Automata

Active Learning for Sound Negotiations

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