Inductive Validity Cores
2018-09
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Inductive Validity Cores
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2018-09
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Symbolic model checkers can construct proofs of properties over very complex models. However, the results reported by the tool when a proof succeeds do not generally provide much insight to the user. It is often useful for users to have traceability information related to the proof: which portions of the model were necessary to construct it. This traceability information can be used to diagnose a variety of modeling problems such as overconstrained axioms and underconstrained properties, and can also be used to measure completeness of a set of requirements over a model. We propose the notion of inductive validity cores (IVCs), which are intended to trace a property to a minimal set of model elements necessary for proof. Such cores are not unique, and algorithms for efficiently producing both single IVC and all IVCs are pre- sented. IVCs can be used for several interesting analyses, including regression analysis for testing/proof, determination of the minimum (as opposed to minimal) number of model elements necessary for proof, the diversity examination of model elements leading to proof, and analyzing fault tolerance.
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University of Minnesota Ph.D. dissertation. September 2018. Major: Computer Science. Advisors: Michael Whalen, Mats Heimdahl. 1 computer file (PDF); 1 vii, 137 pages.
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Ghassabani, Elaheh. (2018). Inductive Validity Cores. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/201700.
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