关键词: 程序静态分析, 序列到序列模型, 代码片段推荐

Abstract:

In the process of software development, developers often reuse code so as to improve the efficiency of software development. Existing researches usually leverage information retrieval technologies to implement the performance of code recommendation. There exists mismatch between the high-level intent in natural language queries and the low-level implementation details for these traditional approaches. This paper proposes DeepCR which is a code snippets recommendation approach based on sequence to sequence model. This approach leverages program static analysis and sequence to sequence model to train a query generation model which can generate queries for code snippets. Code snippets recommendation is then implemented by calculating the similarity between generated queries and natural language queries from developers. The data in the code repository origin from Stack Overflow website to ensure the reality of the collected data. The effectiveness of DeepCR is evaluated by calculating mean reciprocal rank (MRR) and Hit@K scores of code snippets recommendation results. The experimental results show that DeepCR is superior to existing approaches and can improve the performance of code snippets recommendation effectively.

Key words: program static analysis, sequence to sequence model, code snippets recommendation