A parametric interpolation framework for first-order theories

Abstract

Craig interpolation is successfully used in both hardware and software model checking. Generating good interpolants, and hence automatically determining the quality of interpolants is however a very hard problem, requiring non-trivial reasoning in first-order theories. An important class of state-of-the-art interpolation algorithms is based on recursive procedures that generate interpolants from refutations of unsatisfiable conjunctions of formulas. We analyze this type of algorithms and develop a theoretical framework, called a parametric interpolation framework, for arbitrary first-order theories and inference systems. As interpolation-based verification approaches depend on the quality of interpolants, our method can be used to derive interpolants of different structure and strength, with or without quantifiers, from the same proof. We show that some well-known interpolation algorithms are instantiations of our framework. © Springer-Verlag 2013.