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

Table 13

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

Technique	AR1	AR3	CM1	KC2	KC3	MW1	PC1	PC2	PC3	PC4
CDT	?	?	0	0.6098	0.8398	0.5	0.5714	?	0.2632	0.9162
CS-Forest	0.2	0.4	0.2432	0.4942	0.8144	0.2778	0.3820	0.1667	0.3587	0.8898
DS	0	0.75	?	0.5038	0.8639	0.3530	?	?	?	0.8779
Forest-PA	0	0.6667	0	0.6567	0.8115	0	0.6923	?	0.4545	0.9109
HT	?	0.375	?	0.6389	0.8135	?	?	?	?	0.8814
J48	0.2857	0.5	0.1765	0.5521	0.8554	0.4546	0.5455	?	0.4369	0.9426
LMT	0	0.5	0.1429	0.7049	0.843	0.7143	0.2308	0	0.1875	0.9166
RF	0	0.7143	0.2727	0.622	0.8315	0.4615	0.5897	?	0.5556	0.9212
RT	0.25	0.5	0.2167	0.5393	0.8176	0.2051	0.362	0.0417	0.2829	0.9229
REP-T	?	0.5	0	0.5846	0.8315	0.2	0.5882	?	0.4375	0.9216