A hybrid feature selection for fault prediction
Software fault prediction plays a vital role in software quality assurance. Identifying the faulty modules helps to better concentrate on those modules and helps improve the quality of the software. With increasing complexity of software nowadays feature selection is important to remove the redundant, irrelevant and erroneous data from the dataset. In general, Feature selection is done mainly based on filter and wrapper. In this paper a hybrid feature selection method is proposed which gives a better
prediction than the traditional methods. NASA’s public dataset KC1 available at promise software engineering repository is used. To evaluate the performance of the software fault prediction models Accuracy, Mean absolute error (MAE), Root mean squared error (RMSE) values are used.