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

Table 14

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

Technique	AR1	AR3	CM1	KC2	KC3	MW1	PC1	PC2	PC3	PC4
CDT	1	1	0.9911	0.9229	0.1944	0.9919	0.9855	1	0.99	0.3652
CS-Forest	0.8929	0.8909	0.8753	0.7904	0	0.9301	0.9467	0.9991	0.8738	0.1124
DS	0.9821	0.9818	1	0.8434	0.3611	0.9704	1	1	1	0
Forest-PA	0.9911	0.9818	0.9978	0.9446	0	0.9973	0.9961	1	0.9957	0.309
HT	1	0.9091	1	0.9373	0	1	1	1	1	0.0337
J48	0.9554	0.9273	0.9688	0.8964	0.3333	0.9839	0.9855	1	0.9587	0.5899
LMT	0.9911	0.9636	0.9866	0.9566	0.25	0.9946	0.9903	0.9998	0.9907	0.3596
RF	0.9732	0.9636	0.9822	0.9253	0.1389	0.9812	0.9845	1	0.9829	0.3989
RT	0.9464	0.9273	0.8953	0.9012	0.1944	0.9167	0.9506	0.9959	0.9223	0.4438
REP-T	1	0.9818	0.9889	0.9349	0.1667	0.9892	0.9864	1	0.9936	0.4101