Classification of open source software bug report based on transfer learning

Currently, the feature richness of text encoding vectors in the bug report classification model based on deep learning is limited by the size of the domain dataset and the quality of the text. However, it is difficult to further enrich the features of text encoding vectors. At the same time, most existing bug report classification methods ignore the submitter's personal information. To solve these problems, we construct nine personal information characteristics of bug report submitters in GitHub by survey. Then, we propose a GitHub bug report classification method named personal information fine-tuning network (PIFTNet) based on transfer learning and the submitter's personal information. PIFTNet transfers the general text feature vectors in bidirectional encoder representation from transformers (BERT) to the domain of bug report classification by fine-tuning the pre-training parameters in BERT. It also combines the text characteristics and the characteristics of the submitter's personal information to construct the classification model. In addition, we propose a two-stage training method to alleviate the catastrophic changes in the pre-training parameters and loss of the initially learned knowledge caused by direct training of PIFTNet. We verify the proposed PIFTNet on the dataset extracted from GitHub and empirical results prove the effectiveness of PIFTNet.