Related Questions Retrieval Model in Stack Overflow based on Semantic Matching

As one of the most popular programming forums, Stack Overflow has helped many developers with massive high-quality questions and answers. Particularly, the related questions identified by developers can supply targeted knowledge to solve the programming problems. However, it is difficult to identify all relevant questions by developers from massive questions in Stack Overflow. Although some studies have raised methods for automatically identifying relatedness between questions, only a few of them provided related questions to new query. In addition, the existing methods can not extract the global information between query and candidate questions in a proper way. In this paper, we propose a novel method that recommends the related questions to developers' new queries based on the semantic matching. We introduce a novel integral fusion to improve the global information extraction and use the inter-attention to capture the local interactive information. Besides, we have pre-trained domain-specific word embeddings to enhance the processing of software engineering information. The experiment results show that our model achieves competitive performance in MRR, nDCG@5, and nDCG@10 metrics in the related questions retrieval on Stack Overflow,