Abstract
Software systems have grown in size and complexity. These characteristics increase the difficulty of preventing software errors. As a result, forecasting the frequency of software module failures is critical to a developer's efficiency. Many methods for defect detection and correcting problems exist. Hence, Machine Learning (ML) classification performance has to be greatly improved. Thus, in this study, a novel approach is proposed for predicting the number of software defects based on relevant variables using ML. First, feature entropy on each raw features is performed and then identifying the un-pruned random feature. Then is selected the relevant feature through the identical existence among the entropy and un-pruned feature. And finally, the software defect dataset of National Aeronautics and Space Administration (NASA) PC-1 is sent to an ML-based model to estimate the number of faults. Initial PC-1 dataset comprises 37 raw features from this only 8 critical characteristics are utilized to enhance the ML model. A random tree feature selection strategy is shown to be accurate and potentially outperform existing methods in the experimental results. The proposed method considerably outperformed the performance of current ML models by obtaining the accuracy of 97.76% in Random Forest (RF) model.