LIV: Loop-Invariant Validation Using Straight-Line Programs

Validation of program invariants (a.k.a. correctness witnesses) is an established procedure in software verification. There are steady advances in verification of more and more complex software systems, but coming up with good loop invariants remains the central task of many verifiers. While it ofte...

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Veröffentlicht in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] S. 2074 - 2077
Hauptverfasser: Beyer, Dirk, Spiessl, Martin
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 11.09.2023
Schlagworte:
Automatic Verification
Benchmark testing
Data mining
Formal Methods
Logic
Program Analysis
Program Correctness
Program Invariants
Provers
Safety
Software engineering
Software systems
Software Verification
Syntactics
Verification Tools
ISSN:2643-1572
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Zusammenfassung:Validation of program invariants (a.k.a. correctness witnesses) is an established procedure in software verification. There are steady advances in verification of more and more complex software systems, but coming up with good loop invariants remains the central task of many verifiers. While it often requires large amounts of computation to construct safe and inductive invariants, they are more easy to automatically validate. We propose LIV, a new tool for loop-invariant validation, which makes it more practical to check if the invariant produced by a verifier is sufficient to establish an inductive safety proof. The main idea is to apply divide-and-conquer on the program level: We split the program into smaller, loop-free programs (a.k.a. straight-line programs) that form simpler verification tasks. Because the verification conditions are not encoded in logic syntax (such as SMT), but as programs in the language of the original program, any off-the-shelf verifier can be used to verify the generated straight-line programs. In case the validation fails, useful information can be extracted about which part of the proof failed (which straight-line programs are wrong). We show that our approach works by evaluating it on a suitable benchmark. Supplementary website: https://www.sosy-lab.org/research/liv/
ISSN:2643-1572
DOI:10.1109/ASE56229.2023.00214