Learning-based assume-guarantee verification (tool paper)

  • Authors:
  • Dimitra Giannakopoulou;Corina S. Păsăreanu

  • Affiliations:
  • RIACS;QSS, NASA Ames, Moffett Field, CA

  • Venue:
  • SPIN'05 Proceedings of the 12th international conference on Model Checking Software
  • Year:
  • 2005

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Abstract

Despite significant advances in the development of model checking, it remains a difficult task in the hands of experts to make it scale to the size of industrial systems. A key step in achieving scalability is to “divide-and-conquer”, that is, to break up the veri.cation of a system into smaller tasks that involve the verification of its components. Assume-guarantee reasoning [9, 11] is a widespread “divide-and-conquer” approach that uses assumptions when checking individual components of a system. Assumptions essentially encode expectations that each component has from the rest the system in order to operate correctly. Coming up with the right assumptions is typically a non-trivial manual process, which limits the applicability of this type of reasoning in practice.