教育支援の適用に向けた自動バグ修正手法の性能調査

Recently, various automatic bug fixing methods have been developed. In this study, we focus on DeepFix, which can fix syntax errors using deep learning. In the proposal paper of DeepFix, the performance of DeepFix was evaluated using programs collected by Prutor, an online programming course. In this study, we conducted several investigations from the viewpoint of educational support. We use the dataset collected from a programming course at Kyushu University for our investigations. Our investigations show that: (1) the models created with Prutor’s data have an accuracy of about 20% when applied to programs created in Kyushu University. (2) the accuracy of DeepFix can be improved by adding new training data obtained from other educational settings to the training data of the previous study. (3) the number of training data has a significant impact on the performance up to a certain number, but the performance converges when the number exceeds a certain.