CRAM: Code Recommendation With Programming Context Based on Self-Attention Mechanism

Abstract

Code recommendation with programming context is to use the contextual code surrounding the missing code to automatically find that which of the code snippets would be useful to assist in the completion of the program. In this way, the developers need not take time to formulate explicit queries or write descriptions. Existing work only treats code as textual documents and use information retrieval techniques to retrieve relevant code snippets, which is difficult to capture the semantics of code adequately. The self-attention mechanism have achieved promising progress in various natural language processing tasks, especially for extracting deep semantic information from long sequences. Inspired from this, we propose a novel code recommendation with programming context based on self-attention mechanism (CRAM). The proposed approach first builds a small-scale candidate set from codebase. Then, it utilizes self-attention networks in the abstract syntax tree to capture the deep semantics of code, and finally recommend the relevant code to developers. We conduct several experiments to evaluate our approach in a large-scale codebase containing 741 148 code snippets. The experimental results show that CRAM can effectively recommend code and outperforms related work in recall, precision, and NDCG.