Prediction of software faults using machine learning algorithms and mitigating risks with feature selection

Software fault prediction, a crucial component of software engineering, strives to detect probable flaws before they appear, thus enhancing the quality and reliability of software. Effective risk analysis is essential for reducing the risks and uncertainties that could arise during the development of software. The proposed work uses machine learning approaches to predict software faults and highlights the significance of risk analysis and feature selection. The accuracy of predictions can be increased by using feature selection approaches to help discover the features that strongly influence the prediction of software fault occurrence. The feature importance was identified by the algorithms using the decision trees (DT), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) techniques. The models also underwent comparison by removing the features to understand the importance of the features and their correlation. Finally, a comparison is done to recognize the best model for software fault prediction.