An approach to software defect prediction for small-sized datasets

Software defect prediction (SDP) is an active research subject in the software engineering domain. The earlier works on SDP use the same project’s data for prediction in future releases, called within-project defect prediction (WPDP). WPDP may not perform well when the data available for training is small in size. In this work, to address the issue of small-size data, we suggest enhancing the data by borrowing data from other software projects. For better prediction accuracy of learning models, both train and test data must follow the same distribution. However, this may not be true in the case of data being transferred from the other project. Data from different projects may follow different distributions. So, to handle this issue, we have proposed a data preprocessing method, namely data transfer-based WPDP (DT-WPDP). Next, we have shown the use of the deep neural network (DNN) for WPDP and compared it with other classical machine learning (ML) models such as k nearest neighbor, decision tree, logistic regression, and Naive Bayes classifiers. Further, we have performed experimental analysis to assess the effect of the proposed DT-WPDP data preprocessing method with DNN and other ML models. Experimental results show that the proposed approach significantly improves the accuracies of different models. Among different models, the DNN model performed best for all datasets. In the case of very small-sized datasets, which is our main concern in this work, the accuracy of the DNN model is improved by 7% after using the proposed approach.