Learning Register Automata with Fresh Value Generation

Aarts, Fides; Fiterau-Brostean, Paul; Kuppens, Harco; Vaandrager, Frits

doi:10.1007/978-3-319-25150-9_11

Cited by 40 publications

(43 citation statements)

References 22 publications

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“…Learning algorithms that construct the abstractions automatically could potentially solve this problem. We hope that extensions of the learning algorithms for register automata as implemented in the Tomte [4] and RALib [12] tools will be able to construct abstractions for TCP fully automatically.…”

Section: Discussionmentioning

confidence: 99%

Combining Model Learning and Model Checking to Analyze TCP Implementations

Fiterau-Brostean

Janssen

Vaandrager

2016

Lecture Notes in Computer Science

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Abstract. We combine model learning and model checking in a challenging case study involving Linux, Windows and FreeBSD implementations of TCP. We use model learning to infer models of different software components and then apply model checking to fully explore what may happen when these components (e.g. a Linux client and a Windows server) interact. Our analysis reveals several instances in which TCP implementations do not conform to their RFC specifications.

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Section: Discussionmentioning

confidence: 99%

Combining Model Learning and Model Checking to Analyze TCP Implementations

Fiterau-Brostean

Janssen

Vaandrager

2016

Lecture Notes in Computer Science

Self Cite

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“…For this reason, we send acknowledgements automatically from client to broker and do not learn behaviour related to that. This opens up a possibility for future work: it would be possible to carry out experiments with the tool Tomte [1], [2]. This tool is able to learn a restricted class of extended finite state machines (EFSMs), Mealy machines with guards and state updates.…”

Section: Approachmentioning

confidence: 99%

Model-Based Testing IoT Communication via Active Automata Learning

Tappler

Aichernig

Bloem

2017

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)

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This paper presents a learning-based approach to detecting failures in reactive systems. The technique is based on inferring models of multiple implementations of a common specification which are pair-wise cross-checked for equivalence. Any counterexample to equivalence is flagged as suspicious and has to be analysed manually. Hence, it is possible to find possible failures in a semi-automatic way without prior modelling.We show that the approach is effective by means of a case study. For this case study, we carried out experiments in which we learned models of five implementations of MQTT brokers/servers, a protocol used in the Internet of Things. Examining these models, we found several violations of the MQTT specification. All but one of the considered implementations showed faulty behaviour. In the analysis, we discuss effectiveness and also issues we faced. Implementations Abstract Models Single Abstract ModelSingle Abstract Model

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“…Following this line of research, an increasing number of efficient active learning methods (cf. [35]) have been proposed to learn, e.g., Mealy machines [31,27], I/O automata [2], register automata [22,1,12], nondeterministic finite automata [9], Büchi automata [16,25], symbolic automata [26,15,8] and Markov decision processes [33], to name just a few. Full-fledged libraries, tools and applications are also available for automata-learning tasks [10,24,17,18].…”

Section: Introductionmentioning

confidence: 99%

Learning One-Clock Timed Automata

Chen

Zhan

et al. 2020

Lecture Notes in Computer Science

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We present an algorithm for active learning of deterministic timed automata with a single clock. The algorithm is within the framework of Angluin's L * algorithm and inspired by existing work on the active learning of symbolic automata. Due to the need of guessing for each transition whether it resets the clock, the algorithm is of exponential complexity in the size of the learned automata. Before presenting this algorithm, we propose a simpler version where the teacher is assumed to be smart in the sense of being able to provide the reset information. We show that this simpler setting yields a polynomial complexity of the learning process. Both of the algorithms are implemented and evaluated on a collection of randomly generated examples. We furthermore demonstrate the simpler algorithm on the functional specification of the TCP protocol.

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Learning Register Automata with Fresh Value Generation

Cited by 40 publications

References 22 publications

Combining Model Learning and Model Checking to Analyze TCP Implementations

Combining Model Learning and Model Checking to Analyze TCP Implementations

Model-Based Testing IoT Communication via Active Automata Learning

Learning One-Clock Timed Automata

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