Learning the Language of Error

Chapman, Martin; Chockler, Hana; Kesseli, Pascal; Kroening, Daniel; Strichman, Ofer; Tautschnig, Michael

doi:10.1007/978-3-319-24953-7_9

Cited by 20 publications

(13 citation statements)

References 20 publications

(19 reference statements)

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“…Otherwise, the teacher returns a word w distinguishing A h from R. The learning algorithm uses w to refine the candidate automaton of the next iteration. In the last decade, automata learning algorithms have been frequently applied to solve formal verification and synthesis problems, c.f., [27], [24], [39], [38], [26], [32].…”

Section: Regular Model Checkingmentioning

confidence: 99%

Learning to prove safety over parameterised concurrent systems

Chen

Hong

Lin

et al. 2017

2017 Formal Methods in Computer Aided Design (FMCAD)

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Learning to prove safety over parameterised concurrent systems. In: Proc. 17th International Conference on Formal Methods in Computer-AidedDesign (pp. 76-83 Abstract-We revisit the classic problem of proving safety over parameterised concurrent systems, i.e., an infinite family of finite-state concurrent systems that are represented by some finite (symbolic) means. An example of such an infinite family is a dining philosopher protocol with any number n of processes (n being the parameter that defines the infinite family). Regular model checking is a well-known generic framework for modelling parameterised concurrent systems, where an infinite set of configurations (resp. transitions) is represented by a regular set (resp. regular transducer). Although verifying safety properties in the regular model checking framework is undecidable in general, many sophisticated semi-algorithms have been developed in the past fifteen years that can successfully prove safety in many practical instances. In this paper, we propose a simple solution to synthesise regular inductive invariants that makes use of Angluin's classic L * algorithm (and its variants). We provide a termination guarantee when the set of configurations reachable from a given set of initial configurations is regular. We have tested L * algorithm on standard (as well as new) examples in regular model checking including the dining philosopher protocol, the dining cryptographer protocol, and several mutual exclusion protocols (e.g. Bakery, Burns, Szymanski, and German). Our experiments show that, despite the simplicity of our solution, it can perform at least as well as existing semi-algorithms.

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Section: Regular Model Checkingmentioning

confidence: 99%

Learning to prove safety over parameterised concurrent systems

Chen

Hong

Lin

et al. 2017

2017 Formal Methods in Computer Aided Design (FMCAD)

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“…This learning setting now is widely known as active automata learning. In recent years, active automata learning algorithms have attracted increasing attention in the computer aided verification community: it has been applied in black-box model checking [24], compositional verification [12], program verification [10], error localization [8], and model learning [26].…”

Section: Introductionmentioning

confidence: 99%

ROLL 1.0: $$\omega $$ -Regular Language Learning Library

Sun

Turrini

et al. 2019

Tools and Algorithms for the Construction and Analysis of Systems

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We present ROLL 1.0, an ω-regular language learning library with command line tools to learn and complement Büchi automata. This open source Java library implements all existing learning algorithms for the complete class of ω-regular languages. It also provides a learningbased Büchi automata complementation procedure that can be used as a baseline for automata complementation research. The tool supports both the Hanoi Omega Automata format and the BA format used by the tool RABIT. Moreover, it features an interactive Jupyter notebook environment that can be used for educational purpose.

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“…Learning algorithms are applied to construct environmental assumptions of components in the rule automatically. For the latter, automata learning has been used to automatically generate interface model of computer programs [5,22,26,36,40], a model of system error traces for diagnosis purpose [16], behavior model of programs for statistical program analysis [18], and model-based testing and verification [24,34,39].…”

Section: Introductionmentioning

confidence: 99%

A Novel Learning Algorithm for Büchi Automata Based on Family of DFAs and Classification Trees

Chen

Zhang

et al. 2017

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. In this paper, we propose a novel algorithm to learn a Büchi automaton from a teacher who knows an ω-regular language. The algorithm is based on learning a formalism named family of DFAs (FDFAs) recently proposed by Angluin and Fisman [10]. The main catch is that we use a classification tree structure instead of the standard observation table structure. The worst case storage space required by our algorithm is quadratically better than the table-based algorithm proposed in [10]. We implement the first publicly available library ROLL (Regular Omega Language Learning), which consists of all ω-regular learning algorithms available in the literature and the new algorithms proposed in this paper. Experimental results show that our tree-based algorithms have the best performance among others regarding the number of solved learning tasks.

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Learning the Language of Error

Cited by 20 publications

References 20 publications

Learning to prove safety over parameterised concurrent systems

Learning to prove safety over parameterised concurrent systems

ROLL 1.0: $$\omega $$ -Regular Language Learning Library

A Novel Learning Algorithm for Büchi Automata Based on Family of DFAs and Classification Trees

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