Software Defect Prediction Based on Fourier Learning

Abstract

Modern software systems have grown significantly in their size and complexity, therefore software systems have more and more potential defects. Software defect prediction uses a defect data set to build a predictive model, where the data set is composed of software defect metrics. Then, this predictive model is used to predict potential defect program modules in the project. This paper uses the Fourier expression of Boolean function to build a software defect prediction model. We provide the algorithms to calculate the Fourier coefficients and get the predicted function which can predict software defect. And, we compare the Fourier learning algorithm with the traditional machine learning algorithms, such as the random forest algorithm. Finally, the experiment results show that the Fourier learning algorithm is not only better than other algorithms, but also more stable.