Title: Predicting the Results of LTL Model Checking using Multiple Machine Learning Algorithms,
Abstract: In this paper, we study how to predict the results of LTL model checking
using some machine learning algorithms. Some Kripke structures and LTL formulas
and their model checking results are made up data set. The approaches based on
the Random Forest (RF), K-Nearest Neighbors (KNN), Decision tree (DT), and
Logistic Regression (LR) are used to training and prediction. The experiment
results show that the average computation efficiencies of the RF, LR, DT, and
KNN-based approaches are 2066181, 2525333, 1894000 and 294 times than that of
the existing approach, respectively.