Assessing the impact of crucial feature sets in cross-project for defect prediction employing a hybrid feature selection method

Cross-project defect prediction is an important method of identifying defects in a project. We extract knowledge from the source project and apply it to predict labels for the target project in cross-project defect prediction. However, insignificant and irrelevant features can have an impact on model performance. By selecting only significant and relevant features, hybrid feature selection can help achieve high prediction accuracy. Our goal is to investigate the effect of significant feature selection along with CNN classifier on cross-project defect prediction for multi-class datasets using a hybrid approach. We took advantage of the strengths of Random Forest and Recursive Feature Elimination Cross Validation, which can constructively select a few significant features. Our controlled experiment has a 1 Factor 2 Treatments design. Exploratory Data Analysis demonstrates that all versions of the PROMISE repository are multi-class and contain duplicated rows of data, a distribution gap between values, and imbalance classes. After removing duplicated rows, narrowing the data distribution gap, and balancing classes, we chose a significant feature set using a hybrid approach that included Random Forest and Recursive Feature Elimination Cross Validation. To predict Cross project defects, we used Convolutional Neural Network as a classifier, with SoftMax as the final layer. In terms of Area Under Curve, our experimental setup resulted in an average 78 % prediction accuracy measure across all 14 versions. Our experimental results showed that CNN classifier using features selected through Hybrid Feature Selection has a significant impact on defect prediction accuracy for different datasets in the Cross Project.