From: Machine learning-based dynamic analysis of Android apps with improved code coverage
Classifier | PrecM | RecM | PrecB | RecB | W-FM |
---|---|---|---|---|---|
Random-based approach | |||||
RF | 0.855 | 0.835 | 0.804 | 0.827 | 0.8319 |
PART | 0.828 | 0.805 | 0.77 | 0.796 | 0.8011 |
J48 | 0.819 | 0.79 | 0.754 | 0.787 | 0.7887 |
MLP | 0.825 | 0.741 | 0.719 | 0.809 | 0.7720 |
SMO | 0.733 | 0.795 | 0.721 | 0.647 | 0.7265 |
SL | 0.731 | 0.794 | 0.719 | 0.643 | 0.7241 |
NB | 0.741 | 0.536 | 0.577 | 0.772 | 0.6391 |
State-based approach | |||||
RF | 0.904 | 0.87 | 0.848 | 0.887 | 0.8774 |
PART | 0.872 | 0.84 | 0.813 | 0.849 | 0.8447 |
J48 | 0.862 | 0.828 | 0.8 | 0.838 | 0.8333 |
MLP | 0.868 | 0.814 | 0.789 | 0.85 | 0.8301 |
SMO | 0.866 | 0.759 | 0.745 | 0.856 | 0.8031 |
SL | 0.847 | 0.772 | 0.749 | 0.83 | 0.7985 |
NB | 0.744 | 0.665 | 0.639 | 0.721 | 0.6917 |
Hybrid approach | |||||
RF | 0.891 | 0.864 | 0.84 | 0.871 | 0.8671 |
PART | 0.862 | 0.835 | 0.806 | 0.838 | 0.8358 |
J48 | 0.848 | 0.825 | 0.793 | 0.82 | 0.8229 |
MLP | 0.842 | 0.831 | 0.797 | 0.81 | 0.8217 |
SL | 0.837 | 0.773 | 0.747 | 0.816 | 0.7932 |
SMO | 0.846 | 0.754 | 0.735 | 0.833 | 0.7898 |
NB | 0.732 | 0.646 | 0.622 | 0.711 | 0.6761 |