| 1. |
- Bennaceur, A., et al.
(author)
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Preface
- 2018
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - : Springer Verlag. ; , s. V-VI
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Conference paper (other academic/artistic)
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| 2. |
- Gurov, Dilian, 1964-, et al.
(author)
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Who Carries the Burden of Modularity? : Introduction to ISoLA 2020 Track on Modularity and (De-)composition in Verification
- 2020
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In: Leveraging Applications of Formal Methods, Verification and Validation. - Cham : Springer Nature. ; , s. 3-21
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Conference paper (peer-reviewed)abstract
- Modularity and compositionality in verification frameworks occur within different contexts: the model that is the verification target, the specification of the stipulated properties, and the employed verification principle. We give a representative overview of mechanisms to achieve modularity and compositionality along the three mentioned contexts and analyze how mechanisms in different contexts are related. In many verification frameworks one of the contexts carries the main burden. It is important to clarify these relations to understand the potential and limits of the different modularity mechanisms.
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| 3. |
- Hähnle, Reiner, 1962, et al.
(author)
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Model Generation Theorem Proving with Finite Interval Constraints
- 2003
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In: Information Processing Society of Japan. - 0387-5806. ; 43:12, s. 4059-4067
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Journal article (peer-reviewed)abstract
- Model Generation Theorem Proving (MGTP) is a class of deduction procedures for first-order logic that were successfully used to solve hard combinatorial problems. For some applications the representation of models in MGTP and its extension CMGTP causes redundancy. Here we suggest to extend members of model candidates in such a way that a predicate p can have not only terms as arguments, but at certain places also subsets of totally ordered finite domains. The ensuing language and deduction system relies on constraints based on finite intervals in totally ordered sets and is called IV-MGTP. We show soundness/completeness of the procedure, and the experimental results that show considerable potential of the method.
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