Active Model Learning of Stochastic Reactive Systems.

Martin Tappler, Edi Muskardin, Bernhard Aichernig, Ingo Pill

    Research output: Conference proceeding/Chapter in Book/Report/Conference Paperpeer-review


    Black-box systems are inherently hard to verify. Many verification techniques, like model checking, require formal models as a basis. However, such models often do not exist, or they might be outdated. Active automata learning helps to address this issue by offering to automatically infer formal models from system interactions.Hence, automata learning has been receiving much attention in the verification community in recent years. This led to various efficiency improvements, paving the way towards industrial applications. Most research, however, has been focusing on deterministic systems. Here, we present an approach to efficiently learn models of stochastic reactive systems. Our approach adapts $L^*$-based learning for Markov decision processes, which we improve and extend to stochastic Mealy machines. Our evaluation demonstrates that we can reduce learning costs by a factor of up to $8.7$ in comparison to previous work.
    Original languageEnglish
    Title of host publicationSoftware Engineering and Formal Methods
    Subtitle of host publicationLecture Notes in Computer Science (LNCS), volume 13085
    Number of pages20
    Publication statusPublished - 2021


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