English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we present a new approach for measuring the expected runtimes (hardness) of SMT problems. The required features, the statistical hardness model used and the machine learning technique which we used are presented. The method is applied to estimate the hardness of problems in the Quantifier Free Bit Vector (QFBV) theory and we used four of the contesting solvers in SMTCOMP2011 to demonstrate the technique. We have qualitatively expanded some propositional SAT features existing in the literature to directly work on general SMT problem instances without preprocessing. Experimental results with the standard set of benchmarks are promising and our implementation proves the concept.

A Machine Learning Technique for Hardness Estimation of QFBV SMT Problems