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Scalable Thread-Modular Approach for Data Race Detection

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Frontiers in Software Engineering Education (FISEE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12271))

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Abstract

Most of the state-of-the-art verifiers do not scale well on complicated software. Concurrency benchmarks from SV-COMP based on Linux device drivers cause significant difficulties for any software model checker tool.

We suggest a method, which is based on the Thread-Modular approach and Configurable Program Analysis theory. It overapproximates a potential thread iteration by a “worst case” assumption, that the threads may change the shared data in any way. The suggestion allows to avoid construction of a precise thread environment and simplifies the analysis.

For data race detection we use an extension of the Lockset algorithm based on compatibility of partial states. A BnB memory model allows to deal with complicated data structures without a precise alias analysis.

The approach was evaluated on benchmarks set, based on Linux device drivers. The approach allows verifying industrial software, as it was shown on the Linux drivers benchmarks. Predicate abstraction keeps false alarms rate on a reasonable level.

The research was carried out with funding from the Ministry of Science and Higher Education of the Russian Federation (the project unique identifier is RFMEFI60719X0295).

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Notes

  1. 1.

    sv-benchmarks/c/ldv-linux-3.14-races/directory.

  2. 2.

    https://gitlab.com/sosy-lab/software/ldv-benchmarks.git, directory linux-4.2.6-races.

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Authors and Affiliations

  1. Ivannikov Institute for System Programming of the RAS, Moscow Institute of Physics and Technology, Moscow, Russia

    Pavel Andrianov & Vadim Mutilin

Authors
  1. Pavel Andrianov
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  2. Vadim Mutilin
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Corresponding author

Correspondence to Pavel Andrianov .

Editor information

Editors and Affiliations

  1. University of Toulouse, Blagnac, France

    Jean-Michel Bruel

  2. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Alfredo Capozucca

  3. Innopolis University, Innopolis, Russia

    Manuel Mazzara

  4. Schaffhausen Institute of Technology, Schaffhausen, Switzerland

    Bertrand Meyer

  5. Innopolis University, Innopolis, Russia

    Alexandr Naumchev

  6. Innopolis University, Innopolis, Russia

    Andrey Sadovykh

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Andrianov, P., Mutilin, V. (2020). Scalable Thread-Modular Approach for Data Race Detection. In: Bruel, JM., Capozucca, A., Mazzara, M., Meyer, B., Naumchev, A., Sadovykh, A. (eds) Frontiers in Software Engineering Education. FISEE 2019. Lecture Notes in Computer Science(), vol 12271. Springer, Cham. https://doi.org/10.1007/978-3-030-57663-9_24

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  • DOI: https://doi.org/10.1007/978-3-030-57663-9_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57662-2

  • Online ISBN: 978-3-030-57663-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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