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

Table 9

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

Technique	AR1	AR3	CM1	KC2	KC3	MW1	PC1	PC2	PC3	PC4
CDT	95.475	90.104	97.589	70.29	91.982	89.352	79.525	97.671	88.203	64.957
CS-Forest	127.68	109.45	121.64	89.521	98.066	108.64	97.525	100.87	99.451	113.5
DS	100.05	56.431	95.674	73.372	86.727	87.145	92.794	95.159	90.149	79.504
Forest-PA	96.114	67.856	93.351	68.794	88.439	88.169	86.666	97.098	88.415	69.191
HT	100	90.312	100	50.341	97.885	100	100	104.2	100	94.236
J48	87.977	69.248	98.213	72.675	77.888	81.303	80.946	97.671	75.478	53.491
LMT	302.3	82.477	204.91	70.219	85.175	83.399	149.57	4238.8	163.77	65.806
RF	87.961	63.786	91.195	67.493	84.379	87.357	72.995	93.456	80.296	61.913
RT	77.286	54.752	93.743	63.146	96.462	93.925	71.115	99.202	78.198	62.266
REP-T	95.475	92.809	101.17	71.812	97.438	94.043	79.355	97.671	88.505	60.846