Efficient Active Automata Learning via Mutation Testing

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Abstract

System verification is often hindered by the absence of formal models. Peled et al. proposed black-box checking as a solution to this problem. This technique applies active automata learning to infer models of systems with unknown internal structure. This kind of learning relies on conformance testing to determine whether a learned model actually represents the considered system. Since conformance testing may require the execution of a large number of tests, it is considered the main bottleneck in automata learning. In this paper, we describe a randomised conformance testing approach which we extend with fault-based test selection. To show its effectiveness we apply the approach in learning experiments and compare its performance to a well-established testing technique, the partial W-method. This evaluation demonstrates that our approach significantly reduces the cost of learning. In multiple experiments, we reduce the cost by at least one order of magnitude.
Original languageEnglish
Pages (from-to)1 - 32
Number of pages32
JournalJournal of Automated Reasoning
DOIs
Publication statusE-pub ahead of print - 25 Oct 2018

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Efficient Active Automata Learning via Mutation Testing. / Aichernig, Bernhard; Tappler, Martin.

In: Journal of Automated Reasoning, 25.10.2018, p. 1 - 32.

Research output: Contribution to journalArticleResearchpeer-review

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