Research Article
Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults
Table 1
Characteristics of the NASA dataset (PC1, PC2, KC1, and KC3).
| | Dataset | Project | Number of attributes | Number of instances | Number of defective entities | Number of nondefective entities |
| | NASA | PC1 | 22 | 1109 | 77 (6.9%) | 1032 (93.05%) | | NASA | PC2 | 37 | 745 | 16 (2.10%) | 729 (97.90%) | | NASA | KC1 | 22 | 2109 | 326 (15.45%) | 1783 (84.54%) | | NASA | KC3 | 40 | 194 | 36 (18.6%) | 158 (81.4%) |
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