Research Article
Software Defect Prediction through Neural Network and Feature Selections
Table 2
Example of data set used in this study.
| Attribute number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 13 | Data set |
| CM1 | 1.1 | 1 | 1.4 | 5 | 24 | 19.5 | 1287 | 0.03 | 74 | 80 | 90 | F | JM1 | 695 | 0 | 0.09 | 1 | 2.3 | 4 | 19 | 7.6 | 3 | 33 | 11 | T | KC1 | 0 | 546 | 22 | 245 | 4 | 1 | 57 | 0.09 | 78 | 4 | 33 | F | KC2 | 4 | 2 | 0.7 | 33 | 673 | 157 | 3 | 49 | 3 | 0 | 406 | T | KC3 | 22 | 1 | 0 | 148 | 36 | 71 | 901 | 33 | 378 | 222 | 1 | T | KC4 | 0.01 | 1.01 | 1 | 2 | 55 | 19 | 35 | 28 | 75 | 20 | 33 | F | MC1 | 12 | 120 | 0 | 256 | 1 | 52 | 0.09 | 5 | 33 | 0 | 1.5 | T | MC2 | 2 | 1 | 999 | 535 | 43 | 58 | 96 | 40 | 1 | 0 | 4 | F | MW1 | 3 | 0 | 1 | 0.1 | 2 | 0.25 | 0.33 | 11 | 333 | 6 | 55 | F | PC1 | 1 | 80 | 0 | 0.4 | 7 | 1 | O.79 | 77 | 65 | 4 | 34 | T | PC2 | 2 | 80 | 95 | 0.8 | 36 | 0.70 | 50 | 21 | 60 | 5 | 123 | F | PC3 | 456 | 5 | 22 | 44 | 44 | 0.66 | 0.50 | 0.35 | 1 | 35 | 76 | T | PC4 | 180 | 32 | 55 | 44 | 25 | 0.15 | 33 | .09 | 0 | 80 | 5 | T | PC5 | 1.1 | 5.9 | 4 | 678 | 390 | 5.7 | 6 | 1 | 2.5 | 65 | 0 | F |
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