SENSORY: Leveraging Code Statement Sequence Information for Code Snippets Recommendation

Software developers often have to implement unfamiliar programming tasks. When faced with these problems, developers often search online for code snippets as references to learn how to solve the unfamiliar tasks. In recent years, some researchers propose several approaches to use programming context to recommend code snippets. Most of these approaches use information retrieval based techniques and treat code snippets as a set of tokens. However, in code, the smallest meaningful unit is code statement, in general, the line of code. Since these studies did not consider this issue, there is still room for improvement in the code snippets recommendation. In this paper, we propose a code Statement sEquence iNformation baSed cOde snippets Recommendation sYstem (SENSORY). Different from existing token based approaches, SENSORY performs code snippets recommendation at code statement granularity. It uses the Burrows Wheeler Transform algorithm to search relevant code snippets, and uses the structure information to re-rank the results. To evaluate the effectiveness of our proposed method, we construct a code database with 1000000 real world code snippets which contain more than 15000000 lines of code. The experimental results show that SENSORY outperforms the two strong baseline work in terms of precision and NDCG.