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IC3 - Flipping the E in ICE

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10145)

Abstract

Induction is a key element of state-of-the-art verification techniques. Automatically synthesizing and verifying inductive invariants is at the heart of Model Checking of safety properties. In this paper, we study the relationship between two popular approaches to synthesizing inductive invariants: SAT-based Model Checking (SAT-MC) and Machine Learning-based Invariant Synthesis (MLIS). Our goal is to identify and formulate the theoretical similarities and differences between the two frameworks. We focus on two flagship algorithms: IC3 (an instance of SAT-MC) and ICE (an instance of MLIS). We show that the two frameworks are very similar yet distinct. For a meaningful comparison, we introduce RICE, an extension of ICE with relative induction and show how IC3 can be implemented as an instance of RICE. We believe this work contributes to the understanding of inductive invariant synthesis and will serve as a foundation for further improvements to both SAT-MC and MLIS algorithms.

Keywords

  • Reachable State
  • Symbolic Execution
  • Relative Induction
  • Rice State
  • Active Learning Algorithm

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgments

The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement No. [321174].

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Correspondence to Yakir Vizel .

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Vizel, Y., Gurfinkel, A., Shoham, S., Malik, S. (2017). IC3 - Flipping the E in ICE. In: Bouajjani, A., Monniaux, D. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2017. Lecture Notes in Computer Science(), vol 10145. Springer, Cham. https://doi.org/10.1007/978-3-319-52234-0_28

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  • DOI: https://doi.org/10.1007/978-3-319-52234-0_28

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