Improvement Study on Active Learning-based Cross-Project Defect Prediction System

Taeyeun Yang, Hakjoo Oh

http://doi.org/10.5626/JOK.2023.50.11.931

This study proposes a practical improvement method for an active learning-based system for cross-project defect prediction. A previous study applied active learning tech- niques to practically improve the performance of cross-project defect prediction, but it used a traditional machine learning model that used hand-made features as input for active learning target selection and defect prediction, therefore feature extraction was expensive and performance was limited. In addition, the problem of performance deviation according to the selection of the input project remained. In this study, the following methods were proposed to overcome these limitations. First, we used a deep learning model that can use the source code as an input to lower the model building cost and improve prediction performance. Second, a Bayesian convolutional neural network is applied to select an active learning target using a deep learning model. Third, instead of considering a single source project, we applied a method that automatically extracts a training data set from multiple projects. Applying the system proposed in this study to 7 open source projects improved the average prediction performance by 13.58% compared to the previous latest research.