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Reusing Predicate Precision in Value Analysis

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Integrated Formal Methods (IFM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13274))

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Abstract

Software verification allows one to examine the reliability of software. Thereby, analyses exchange information to become more effective, more efficient, or to eliminate false results and increase trust in the analysis result. One type of information that analyses provide are precisions, which describe an analysis’ degree of abstraction (tracked predicates, etc.). So far, analyses mainly reuse their own precision to reverify a changed program. In contrast, we aim to reuse the precision of a predicate analysis within a value analysis. To this end, we propose 13 options to convert a predicate precision into a precision for value analysis. All options compute precisions with various degrees of abstraction and are broadly evaluated on three applications (cooperative verification, result validation, and regression verification). Also, we compare our options against using the coarsest and finest precision as well as a state-of-the-art approach for each application. Our evaluation reveals that coarser precisions work better for proof detection, while finer precisions perform better in alarm detection. Moreover, reusing a predicate precision in value analysis can be beneficial in cooperative verification and works well for validating and reverifying programs without property violations.

This work was funded by the Hessian LOEWE initiative within the Software-Factory 4.0 project.

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Data Availability Statement

All experimental data is made publicly available in a replication package [69].

Notes

  1. 1.

    Our implementation supports C programs.

  2. 2.

    Many safety properties can be encoded by the unreachability of an error location [61].

  3. 3.

    In our scenarios, the predicates have been used for the (incomplete) verification of the same or a previous version of the program and therefore are likely relevant.

  4. 4.

    https://cpachecker.sosy-lab.org/

  5. 5.

    Configurations \(\texttt {config}=(\bot ,\bot ,\bot ,\bot ,\bot )\), \(\texttt {config}=(\top ,\cdot ,\bot ,\bot ,\cdot )\), \(\texttt {config}=(\top ,\cdot ,\top ,\bot ,\cdot )\), and \(\texttt {config}=(\top ,\cdot ,\top ,\top ,\cdot )\), where \(\cdot \) is either \(\top \) (true) or \(\bot \) (false).

  6. 6.

    https://github.com/sosy-lab/sv-benchmarks/

  7. 7.

    https://www.sosy-lab.org/research/cpa-reuse/supplementary-archive.zip

  8. 8.

    Since we did not retrieve the temporary predicate precision, we checked that \(\varPi _\mathcal {V}^0\ne \emptyset \) if the value analysis with configuration no adapt (\((\bot ,\bot ,\bot ,\bot ,\bot )\)) reports a result.

  9. 9.

    We use a CPU time limit of 900 s for all unsolved programs.

  10. 10.

    We checked that the predicate analysis refined at least once and that \(\varPi _\mathcal {V}^0\ne \emptyset \) if the value analysis with configuration no adapt (\((\bot ,\bot ,\bot ,\bot ,\bot )\)) reports a result.

  11. 11.

    We checked that the predicate analysis refined at least once and that \(\varPi _\mathcal {V}^0\ne \emptyset \) if the value analysis with configuration no adapt (\((\bot ,\bot ,\bot ,\bot ,\bot )\)) reports a result.

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  1. Technical University of Darmstadt, Department of Computer Science, Darmstadt, Germany

    Marie-Christine Jakobs

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    Maurice H. ter Beek

  2. Maynooth University, Maynooth, Ireland

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Jakobs, MC. (2022). Reusing Predicate Precision in Value Analysis. In: ter Beek, M.H., Monahan, R. (eds) Integrated Formal Methods. IFM 2022. Lecture Notes in Computer Science, vol 13274. Springer, Cham. https://doi.org/10.1007/978-3-031-07727-2_5

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