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

Table 11

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

Technique	AR1	AR3	CM1	KC2	KC3	MW1	PC1	PC2	PC3	PC4
CDT	99.9593	100.959	102.252	89.8344	98.1628	97.7466	92.7793	99.9995	97.9979	84.9229
CS-Forest	113.601	103.118	111.028	93.8871	96.8295	109.526	98.4938	100.808	101.864	92.7438
DS	113.975	91.0648	99.7001	88.4132	95.4458	96.2335	96.6546	98.5476	95.9299	89.2681
Forest-PA	101.48	84.7947	98.0292	84.7724	95.3681	95.2364	92.397	100.288	94.5733	82.0605
HT	100	115.734	100	99.572	102.503	100	100	99.9618	100	98.8565
J48	114.05	102.391	110.822	98.2924	110.557	103.215	96.0121	99.9995	103.953	91.331
LMT	176.823	97.3056	145.11	83.9701	100.743	91.1714	123.907	655.858	129.23	81.9477
RF	108.688	81.4368	99.0872	86.4543	94.2824	98.0574	87.469	101.053	89.569	75.4394
RT	125.935	106.549	137.265	108.7792	139.372	137.037	118.574	140.375	124.894	111.563
REP-T	99.9593	102.791	104.148	92.4714	105.23	103.187	93.0209	99.9995	97.118	84.557