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Deadlock Detection in Linear Recursive Programs

  • Chapter
Formal Methods for Executable Software Models (SFM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8483))

Abstract

Deadlock detection in recursive programs that admit dynamic resource creation is extremely complex and solutions either give imprecise answers or do not scale.

We define an algorithm for detecting deadlocks of linear recursive programs of a basic model. The theory that underpins the algorithm is a generalization of the theory of permutations of names to so-called mutations, which transform tuples by introducing duplicates and fresh names.

Our algorithm realizes the back-end of deadlock analyzers for object-oriented programming languages, once the association programs/basic-model-programs has been defined as front-end.

Partly funded by the EU project FP7-610582 ENVISAGE: Engineering Virtualized Services.

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Authors and Affiliations

  1. Dept. of Computer Science and Egineering – INRIA FOCUS, Università di Bologna, Italy

    Elena Giachino & Cosimo Laneve

Authors
  1. Elena Giachino
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  2. Cosimo Laneve
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Editor information

Editors and Affiliations

  1. Dipartimento di Scienze di Base e Fondamenti, Università di Urbino, Piazza della Republica 13, 61029, Urbino, Italy

    Marco Bernardo

  2. Dipartimento di Informatica, Università di Torino, Corso Svizzera 185, 10149, Torino, Italy

    Ferruccio Damiani

  3. Fachbereich Informatik, Technische Universität Darmstadt, Hochschulstraße 10, 64289, Darmstadt, Germany

    Reiner Hähnle

  4. Department of Informatics, University of Oslo, P.O. Box 1080 Blindern, 0316, Oslo, Norway

    Einar Broch Johnsen

  5. Institut für Softwaretechnik und Fahrzeuginformatik, Technische Universität Braunschweig, Mühlenpfordtstraße 23, 38106, Braunschweig, Germany

    Ina Schaefer

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Giachino, E., Laneve, C. (2014). Deadlock Detection in Linear Recursive Programs. In: Bernardo, M., Damiani, F., Hähnle, R., Johnsen, E.B., Schaefer, I. (eds) Formal Methods for Executable Software Models. SFM 2014. Lecture Notes in Computer Science, vol 8483. Springer, Cham. https://doi.org/10.1007/978-3-319-07317-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-07317-0_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07316-3

  • Online ISBN: 978-3-319-07317-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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