Abstract
Proving safety of programs relies principally on discovering invariants that are inductive and adequate. Obtaining such invariants, therefore, has been studied widely from diverse perspectives, including even mining them from the input program’s source in a guess-and-check manner [13]. However, guessing candidates based on syntactical constructions of the source code has its limitations. For one, a required invariant may not manifest on the syntactic surface of the program. Secondly, a poor guess may give rise to a series of expensive checks. Furthermore, unlike conjunctions, refining disjunctive invariant candidates is unobvious and may frequently cause the proof search to diverge. This paper attempts to overcome these limitations, by learning from both – appearance and behaviours of a program. We present an algorithm that (i) infers useful invariants by observing a program’s syntactic source as well as its semantics, and (ii) looks for conditional invariants, in the form of implications, that are guided by counterexamples to inductiveness. Our experiments demonstrate its benefits on several benchmarks taken from SV-COMP and the literature.
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Notes
- 1.
behaviour refers to facts derivable from the program’s meaning, not necessarily limited to its concrete runs; we use the terms behaviours and semantics interchangeably.
- 2.
- 3.
Thanks to Grigory Fedyukovich, the sources of FreqHorn-2 are available at https://github.com/grigoryfedyukovich/aeval/tree/rnd.
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Prabhu, S., Madhukar, K., Venkatesh, R. (2018). Efficiently Learning Safety Proofs from Appearance as well as Behaviours. In: Podelski, A. (eds) Static Analysis. SAS 2018. Lecture Notes in Computer Science(), vol 11002. Springer, Cham. https://doi.org/10.1007/978-3-319-99725-4_20
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