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Inspecting Maude variants with GLINTS*
Published online by Cambridge University Press: 24 August 2017
Abstract
This paper introduces GLINTS, a graphical tool for exploring variant narrowing computations in Maude. The most recent version of Maude, version 2.7.1, provides quite sophisticated unification features, including order-sorted equational unification for convergent theories modulo axioms such as associativity, commutativity, and identity. This novel equational unification relies on built-in generation of the set of variants of a term t, i.e., the canonical form of tσ for a computed substitution σ. Variant generation relies on a novel narrowing strategy called folding variant narrowing that opens up new applications in formal reasoning, theorem proving, testing, protocol analysis, and model checking, especially when the theory satisfies the finite variant property, i.e., there is a finite number of most general variants for every term in the theory. However, variant narrowing computations can be extremely involved and are simply presented in text format by Maude, often being too heavy to be debugged or even understood. The GLINTS system provides support for (i) determining whether a given theory satisfies the finite variant property, (ii) thoroughly exploring variant narrowing computations, (iii) automatic checking of node embedding and closedness modulo axioms, and (iv) querying and inspecting selected parts of the variant trees.
- Type
- Regular Papers
- Information
- Theory and Practice of Logic Programming , Volume 17 , Special Issue 5-6: 33rd International Conference on Logic Programming , September 2017 , pp. 689 - 707
- Copyright
- Copyright © Cambridge University Press 2017
Footnotes
This work has been partially supported by EU (FEDER) and Spanish MINECO grant TIN 2015-69175-C4-1-R and by Generalitat Valenciana PROMETEO-II/2015/013. Angel Cuenca-Ortega is supported by SENESCYT, Ecuador (scholarship program 2013), and Julia Sapiña by FPI-UPV grant SP2013-0083. Santiago Escobar is supported by the Air Force Office of Scientific Research under award number FA9550-17-1-0286.
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