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

Table 10

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

Technique	AR1	AR3	CM1	KC2	KC3	MW1	PC1	PC2	PC3	PC4
CDT	0.2627	0.3377	0.3046	0.3627	0.3818	0.2605	0.2358	0.064	0.2971	0.278
CS-Forest	0.2985	0.3449	0.3307	0.379	0.3766	0.2919	0.2504	0.0645	0.3088	0.3036
DS	0.2995	0.3046	0.297	0.3569	0.3712	0.2565	0.2457	0.0631	0.2908	0.2923
Forest-PA	0.2667	0.2836	0.292	0.3422	0.3709	0.2538	0.2349	0.642	0.2867	0.2687
HT	0.2628	0.3871	0.2979	0.402	0.3987	0.2665	0.2542	0.0639	0.3031	0.3236
J48	0.2997	0.3424	0.3301	0.3968	0.43	0.2751	0.2441	0.064	0.3151	0.299
LMT	0.4646	0.3254	0.4322	0.339	0.3918	0.243	0.315	0.4199	0.3917	0.2683
RF	0.2856	0.2724	0.2951	0.349	0.3667	0.2613	0.2223	0.0647	0.2715	0.247
RT	0.3309	0.3563	0.4089	0.4392	0.542	0.3652	0.3014	0.0899	0.3786	0.3652
REP-T	0.2627	0.3438	0.3102	0.3733	0.4093	0.275	0.2365	0.064	0.2944	0.2768