# Improving Software Defect Prediction in Noisy Imbalanced Datasets
## 3 Materials and Methods
### Predictor Construction
\begin{tabular}{c} **SMOTE,** \\ **IF** \\ \end{tabular}  & \begin{tabular}{c} **SMOTE,** \\ **FS5B** \\ \end{tabular}  & \begin{tabular}{c} **SMOTE,** \\ **PSST\_2T** \\ \end{tabular}  & \begin{tabular}{c} **SMOTE** \\ **TSOCLELILIHS** \\ \end{tabular}  & \begin{tabular}{c} **SMOTE** \\ **SNOTE** \\ \end{tabular}  & \begin{tabular}{c} **Borderlineline-** \\ **ADASNN** \\ \end{tabular}  & \ \begin{tabular}{c} **MSMOTE** \\ **SNOTE** \\ \end{tabular}  & \begin{tabular}{c} **Gaussian,** \\ **SMOTE** \\ \end{tabular}  & **CCR** \\ \hline
**ant** & & & & & & & & & & & & & \\
**10\% noise** & 0.8576 & 0.8624 & 0.8871 & 0.8630 & 0.8500 & 0.8716 & 0.7355 & 0.7481 & 0.6997 & 0.7201 & 0.7388 & 0.7288 & 0.8358 \\
20\% noise & **0.7952** & 0.8012 & 0.7388 & 0.7287 & 0.0115 & 0.7280 & 0.6900 & 0.7280 & 0.6572 & 0.7133 & 0.7216 & 0.7457 & 0.7270 \\
30\% noise & **0.7717** & 0.7399 & 0.7461 & 0.7185 & 0.7438 & 0.7314 & 0.7461 & 0.7041 & 0.7008 & 0.6929 & 0.7185 & 0.6945 & 0.7618 \\
**US-PON** & **SOMO** & **MWMOTE** & **SMOTE** & **SMOTE** & **SMOTE** & **SMOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** \\
**camd** & & & & & & & & & & & & \\
**10\% noise** & 0.8522 & **0.8549** & 0.8508 & 0.8470 & 0.8528 & 0.8433 & 0.7653 & 0.7078 & 0.7443 & 0.7730 & 0.7358 & 0.7664 & 0.7823 \\
20\% noise & **0.7958** & 0.7354 & 0.7428 & 0.7610 & 0.7459 & 0.7112 & 0.7251 & 0.7678 & 0.7232 & 0.7366 & 0.7398 & 0.7264 & 0.7021 \\
**30\% noise** & 0.7747 & 0.7315 & 0.7366 & 0.7508 & 0.7228 & 0.6833 & 0.6900 & 0.7389 & 0.6849 & 0.7309 & 0.7105 & 0.6989 \\
**US-PON** & **SOMO** & **MWMOTE** & **SMOTE** & **SMOTE** & **SMOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** \\
**JOT** & & & & & & & & & & & & & \\
**10\% noise** & 0.8960 & 0.7035 & 0.8271 & 0.7648 & 0.7091 & 0.7596 & 0.7999 & 0.8139 & 0.7406 & 0.8004 & 0.7775 & 0.8766 & 0.8899 \\
**20\% noise** & 0.8166 & 0.6999 & 0.7025 & 0.7402 & 0.7452 & 0.7993 & 0.7962 & 0.7220 & 0.7685 & 0.7540 & 0.8523 & 0.7100 \\
**30\% noise** & 0.8320 & 0.6447 & 0.8453 & 0.7318 & 0.7091 & 0.7201 & 0.7738 & 0.7588 & 0.7227 & 0.7758 & 0.7410 & 0.8827 & 0.7028 & 0.7028 \\
**SU-PON** & **SOMO** & **MWMOTE** & **SMOTE** & **SMOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** & **SNOTE** \\
**jedit** & & & & & & & & & & & & & \\
**10\% noise** & 0.8865 & 0.8073 & 0.8289 & 0.8762 & 0.8364 & 0.8443 & 0.8720 & 0.8771 & 0.8437 & 0.8324 & 0.8308 & 0.916 & 0.9061 \\
**20\% noise** & 0.8882 & 0.8169 & 0.8866 & 0.8467 & 0.8273 & 0.8173 & 0.8588
**10\%** noise & 0.612 & 0.6852 & 0.7107 & 0.6414 & 0.8921 & 0.6897 & 0.7647 & 0.7747 & 0.7823 & 0.7412 & 0.6479 & 0.6667 & 0.7571 & **0.8966** \\
**20\%** noise & 0.6497 & 0.6914 & 0.8852 & 0.7107 & 0.6461 & 0.912 & 0.6897 & 0.7813 & 0.7143 & 0.7273 & 0.7412 & 0.6013 & 0.743 Experiment results show that at the 30% noise level, our proposed method had a very significant advantage over all other methods for most datasets, especially for Gaussian SMOTE and CCR (which outperforms US-PONR at the low noise level in some datasets). This suggests that US-PONR is the best overall method in a noisy environment, except for the Mylyn and PDE datasets. The reason for the method's poor performance on these datasets may be that they have heavy distributions (such as the \"levy distribution\"), which make it impossible to distinguish between good and bad samples in the feature space using PSM, leading to improper oversampling or noise reduction. The reason for US-PONR's superior performance lies in the way it reduces noise using the PSM score, which can effectively capture noise samples. ### Answer to RQ3: Is US-PONR Especially Good at Eliminating Label Noise Samples? In order to verify the effectiveness of US-PONR in eliminating label noise, we designed an experiment to discover how many introduced noise samples were found and eliminated by each method in the 10%, 20%, and 30% noise environments.