Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects

Table 15

Recall analysis by each TF-ML technique on individual dataset.

Technique	AR1	AR3	CM1	KC2	KC3	MW1	PC1	PC2	PC3	PC4
CDT	0.9256	0.873	0.9008	0.8705	0.5385	0.9295	0.9469	0.9959	0.8996	0.5909
CS-Forest	0.9434	0.9245	0.9269	0.9371	?	0.9428	0.9578	0.9961	0.9526	0.8333
DS	0.9244	0.9153	0.9016	0.8951	0.52	0.9352	0.9306	0.9959	0.8976	?
Forest-PA	0.925	0.9	0.9014	0.8615	0	0.9229	0.938	0.9959	0.9001	0.7143
HT	0.9256	0.9091	0.9016	0.8644	0	0.9231	0.9306	0.9959	0.8976	0.75
J48	0.9386	0.9273	0.9044	0.8732	0.4286	0.9337	0.9452	0.9959	0.9212	0.5615
LMT	0.925	0.8983	0.9022	0.8612	0.4091	0.9343	0.9325	0.9959	0.8985	0.7033
RF	0.9237	0.9464	0.9055	0.8727	0.5	0.9359	0.9495	0.9959	0.9139	0.71
RT	0.9381	0.9273	0.9178	0.8637	0.2	0.9368	0.9534	0.996	0.9171	0.454
REP-T	0.9256	0.8852	0.9006	0.849	0.375	0.9246	0.947	0.9959	0.9011	0.6186