Deep feature learning to quantitative prediction of software defects

Defect prediction forecasts defect proneness or the number of defects contained in software systems. It is frequently employed to efficiently prioritize and allocate the limited testing resources to the modules that are more likely to be defective during the process of software development and maintenance. Consequently, a number of defect prediction approaches have been proposed. Most of the existing approaches on defect prediction regard defect prediction as a classification problem in which programs are classified as buggy or non-buggy. However, identifying the defect proneness of a given software module is not sufficient in practical software testing. The research on predicting the number of defects is limited and the performances of these approaches are constantly being optimized and improved. Therefore, in this paper, we propose a novel approach that leverages a convolutional neural network to predict the number of defects in software systems automatically. First, we preprocess the PROMISE dataset, which involves performing natural logarithm transformation and data normalization. Second, we feed the preprocessed dataset to a specially designed convolutional neural network-based model to predict the number of defects. Third, we rank the software modules according to the corresponding predicted number of defects in descending order. We also evaluate the proposed approach on a well-known dataset by cross-validation. The evaluation results suggest that the proposed approach is both accurate and robust, and it improves the state of the art. On average, it significantly improves the Kendall correlation coefficient by 16% and the fault-percentile-average by 4%.