Inferring Canonical Register Automata

Howar, Falk; Steffen, Bernhard; Jönsson, Bengt; Cassel, Sofia

doi:10.1007/978-3-642-27940-9_17

Cited by 82 publications

(68 citation statements)

References 23 publications

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“…Howar et al's approach is an extension of Angluin's L * algorithm [4], and is built into the LearnLib tool [25]. Aarts et al's approach [1] is built upon the notion of Counter Example Guided Abstraction Refinement [11].…”

Section: Related Workmentioning

confidence: 99%

Inferring extended finite state machine models from software executions

2015

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The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering techniques focus either on control-specific behaviour (e.g., in the form of Finite State Machines), or data-specific behaviour (e.g., as pre / post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software's operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these techniques can be non-deterministic, the inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presentsWe thank Ivo Krka at the University of Southern California for providing us with some of the traces for the evaluation. Ramsay Taylor and John Derrick are supported by the EU FP7 PROWESS projects. Neil Walkinshaw is grateful for the support provided by the UK Ministry of Defence (MOD) through the BATS and HASTE projects. Information contained in this document should not be interpreted as representing the views of the MOD, nor should it be assumed that it reflects any current or future MOD policy. The information cannot supersede any statutory or contractual requirements or liabilities and is offered without prejudice or commitment.

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Section: Related Workmentioning

confidence: 99%

Inferring extended finite state machine models from software executions

2015

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“…The original L * algorithm has originally been presented for DFAs, but has since been adapted to Mealy Machines, which are a better fit for learning actual reactive systems as they can encode system output in a natural way. A major and recent increase in expressiveness is achieved with Register Automata [5], which are described in the following section.…”

Section: Active Automata Learningmentioning

confidence: 99%

Demonstrating Learning of Register Automata

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Howar

Steffen

et al. 2012

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. We will demonstrate the impact of the integration of our most recently developed learning technology for inferring Register Automata into the LearnLib, our framework for active automata learning. This will not only illustrate the unique power of Register Automata, which allows one to faithfully model data independent systems, but also the ease of enhancing the LearnLib with new functionality.

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“…Recently, automata dealing with potentially infinite data as first class citizens have been studied. Seminal works in this area are that of [1,15] and [14]. While the first two use abstraction and refinement techniques to cope with infinite data, the second approach learns a sub-class of register automata.…”

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“…Session automata correspond to the model from [7] without stacks. They are incomparable with the model from [14].Session automata accept data words, i.e., words over an alphabet Σ × D, where Σ is a finite set of labels and D an infinite set of data values. A data word can be mapped to a so-called symbolic word where we record for each different data value the register in which it was stored (when appearing for the first time) or from which it was read later.…”

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A Fresh Approach to Learning Register Automata

Bollig

Habermehl

Leucker

et al. 2013

Developments in Language Theory

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Abstract. This paper provides an Angluin-style learning algorithm for a class of register automata supporting the notion of fresh data values. More specifically, we introduce session automata which are well suited for modeling protocols in which sessions using fresh values are of major interest, like in security protocols or ad-hoc networks. We show that session automata (i) have an expressiveness partly extending, partly reducing that of register automata, (ii) admit a symbolic regular representation, and (iii) have a decidable equivalence and model-checking problem (unlike register automata). Using these results, we establish a learning algorithm to infer session automata through membership and equivalence queries. Finally, we strengthen the robustness of our automaton by its characterization in monadic second-order logic. IntroductionLearning automata deals with the inference of automata based on some partial information, for example samples, which are words that either belong to their accepted language or not. A popular framework is that of active learning defined by Angluin [2] in which a learner may consult a teacher for so-called membership and equivalence queries to eventually infer the automaton in question. Learning automata has a lot of applications in computer science. Notable examples are the use in model checking [12] and testing [3]. See [18] for an overview.While active learning of regular languages is meanwhile well understood and is supported by freely available libraries such as learnlib [19] and libalf [8], extensions beyond plain regular languages are still an area of active research. Recently, automata dealing with potentially infinite data as first class citizens have been studied. Seminal works in this area are that of [1,15] and [14]. While the first two use abstraction and refinement techniques to cope with infinite data, the second approach learns a sub-class of register automata.In this paper, we follow the work on learning register automata. However, we study a different model than [14], having the ability to require that input data is fresh in the sense that it has not been seen so far. This feature has been proposed in [24] in the context of semantics of programming languages, as, forThis work is partially supported by EGIDE/DAAD-Procope (LeMon).example, fresh names are needed to model object creation in object-oriented languages. Moreover, fresh data values are important ingredients in modeling security protocols which often make use of so-called fresh nonces to achieve their security assertions [17]. Finally, fresh names are also important in the field of network protocols and are one of the key ingredients of the π-calculus [20].In general, the equivalence problem of register automata is undecidable (even without freshness). This limits their applicability in active learning, as equivalence queries cannot be implemented (correctly and completely). Therefore, we restrict the studied automaton model to either store fresh data values or read data values from registers. In the termi...

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Inferring Canonical Register Automata

Cited by 82 publications

References 23 publications

Inferring extended finite state machine models from software executions

Inferring extended finite state machine models from software executions

Demonstrating Learning of Register Automata

A Fresh Approach to Learning Register Automata

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