Predicting Tags for Learner Questions on Stack Overflow

Abstract

AbstractOnline question answering sites, such as Stack Overflow (SO), have become an important learning and support platform for computer-science learners and practitioners who are seeking help. Learners on SO are currently faced with the problem of unanswered questions, inhibiting their lifelong-learning efforts and contributing to delays in their software development process. The major reason for this problem is that most of the technical problems posted on SO are not seen by those who have the required expertise and knowledge to answer a specific question. This issue is often attributed to the use of inappropriate tags when posting questions. We developed a new method, BERT-CBA, to predict tags for answering user questions. BERT-CBA combines a convolutional network, BILSTM, and attention layers with BERT. In BERT-CBA, the convolutional layer extracts the local semantic features of an SO post, the BILSTM layer fuses the local semantic features and the word embeddings (contextual features) of an SO post, and the attention layer selects the important words from a post to identify the most appropriate tag labels. BERT-CBA outperformed four existing tag recommendation approaches by 2-73% as measured by F1@K=1-5. These findings suggest that BERT-CBA could be used to recommend appropriate tags to learners before they post their question which would increase their chances of getting answers.