Machine Learning Model for Fault Prediction in Object-Oriented Systems

Predicting whether the module is faulty is vital to concentrating on the testing and evaluation process. We proposed a machine learning model to predict the faulty model in object-oriented systems. We employ a set of metrics to learn and test the module. The results show averages of 0.82, 0.98, 0.82, and 0.90 for precision, recall, and F1-score, respectively.This proposed model can be used to predict faulty models and decrease the effort of software testing.