Code recommendation based on joint embedded attention network

Due to the heterogeneity of program language and natural language query, it is difficult to identify the semantic relationship between them, which leads to the low efficiency of code recommendation. In order to solve the problems of the above code recommendation technology, a code recommendation method based on joint embedded attention network (JEAN) is proposed in this paper. The method uses GRU Network to embed code snippets and describe queries into vector representation, which solves the problem of heterogeneous code snippets and natural language queries. The Attention mechanism is then used to distribute totally different weights to different components of every mode of the code snippet. The reason for the Attention mechanism is that different components of every mode of the code snippet contribute differently to the semantic vector of the final code snippet, making it interpretable. Finally, two commonly used evaluation indexes of information retrieval, SuccessRate@k and MRR, are used for experimental comparison with other baseline models. The experimental results show that the code recommendation method based on joint embedded attention network proposed in this paper can effectively recommend appropriate code snippets according to the needs of developers, and its performance is better than other baseline methods.

Automated summary

This paper proposes a code recommendation method based on joint embedded attention network (JEAN) to address the heterogeneity of program language and natural language query. By using GRU Network for embedding and describing, the method solves the problem of heterogeneous code snippets and queries. The Attention mechanism is used to distribute different weights to different components, making the code recommendation more interpretable. Experimental results demonstrate that the proposed method outperforms other baseline models in recommending appropriate code snippets for developers' needs.