Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-Cycle (SDLC). As the prevalence of software systems increases and becomes more integrated into our daily lives, so the complexity of these systems increases the risks of widespread defects. With reliance on these systems increasing, the ability to accurately identify a defective model using Machine Learning (ML) has been overlooked and less addressed. Thus, this article contributes an investigation of various ML techniques for SDP. An investigation, comparative analysis and recommendation of appropriate Feature Extraction (FE) techniques, Principal Component Analysis (PCA), Partial Least Squares Regression (PLS), Feature Selection (FS) techniques, Fisher score, Recursive Feature Elimination (RFE), and Elastic Net are presented. Validation of the following techniques, both separately and in combination with ML algorithms, is performed: Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree (DT), and ensemble learning methods Bootstrap Aggregation (Bagging), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest(RF), and Generalized Stacking (Stacking). Extensive experimental setup was built and the results of the experiments revealed that FE and FS can both positively and negatively affect performance over the base model or Baseline. PLS, both separately and in combination with FS techniques, provides impressive, and the most consistent, improvements, while PCA, in combination with Elastic-Net, shows acceptable improvement.
Keywords: ensemble learning; feature extraction; feature selection; machine learning; software defects prediction; software development life-cycle.