Code-Enhanced Fine-Grained Semantic Matching for Tag Recommendation in Software Information Sites

Tag recommendation in software information sites is a significant task to help developers make distinctions among software objects. Most existing methods usually ignore the semantic information of code snippets in software information sites. To tackle this issue, we regard the code as a semantic enhancement signal, and propose a novel Code- Enhanced fine-grained semantic matching method for Tag Recommendation in software information sites (CETR) to learn the matching score between tags and software objects. In our CETR, code-enhanced semantic interaction is designed to capture fine-grained semantic relevance between tags and software objects. Specifically, code snippets and text descriptions in software objects are first encoded in two specialized ways to capture semantic information from the perspective of natural language and structural language respectively. The semantic interaction is then learned between code snippets and text descriptions. Besides, a hierarchy-aware semantic learning block attentively combines the features from different layers as the final feature to capture semantic information of different levels. Lastly, a pairwise sentence matching computation block is exploited to predict matching probability. Experimental results on four software information site datasets have demonstrated the effectiveness of our proposed CETR for tag recommendation compared with the state-of- the-art methods.