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ACM Transactions on Software Engineering and Methodology
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
DBLP
Article . 2022
Data sources: DBLP
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Verification Witnesses

Authors: Dirk Beyer 0001; Matthias Dangl; Daniel Dietsch; Matthias Heizmann; Thomas Lemberger 0002; Michael Tautschnig;

Verification Witnesses

Abstract

Over the last years, witness-based validation of verification results has become an established practice in software verification: An independent validator re-establishes verification results of a software verifier using verification witnesses, which are stored in a standardized exchange format. In addition to validation, such exchangable information about proofs and alarms found by a verifier can be shared across verification tools, and users can apply independent third-party tools to visualize and explore witnesses to help them comprehend the causes of bugs or the reasons why a given program is correct. To achieve the goal of making verification results more accessible to engineers, it is necessary to consider witnesses as first-class exchangeable objects, stored independently from the source code and checked independently from the verifier that produced them, respecting the important principle of separation of concerns. We present the conceptual principles of verification witnesses, give a description of how to use them, provide a technical specification of the exchange format for witnesses, and perform an extensive experimental study on the application of witness-based result validation, using the validators CPAchecker , UAutomizer , CPA-witness2test , and FShell-witness2test .

Countries
United Kingdom, Germany
Keywords

certifying algorithm, Violation witness, formal methods, program analysis, software verification, witness validation, data-flow analysis, correctness witness, model checking, 004

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
36
Top 10%
Top 10%
Top 1%
Green
hybrid