Comparative Analysis of Statistical Model Checking Tools [chapter]

Mehmet Emin Bakir, Marian Gheorghe, Savas Konur, Mike Stannett
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Statistical model checking is a powerful and flexible approach for formal verification of computational models, e.g. P systems, which can have very large search spaces. Various statistical model checking tools have been developed, but choosing the most efficient and appropriate tool requires a significant degree of experience, not only because different tools have different modelling and property specification languages, but also because they may be designed to support only a certain subset of
more &raquo; ... roperty types. Furthermore, their performance can vary depending on the property types and membrane systems being verified. In this paper, we evaluate the performance of various common statistical model checkers based on a pool of biological models. Our aim is to help users select the most suitable SMC tools from among the available options, by comparing their modelling and property specification languages, capabilities and performances.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-54072-6_8">doi:10.1007/978-3-319-54072-6_8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mtrtkgwr4zd7nemowsuazwu6ky">fatcat:mtrtkgwr4zd7nemowsuazwu6ky</a> </span>
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