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Code recommendation based on joint embedded attention network

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Abstract

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.

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Data availability

The datasets analyzed during the current study are not publicly available.

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Acknowledgements

This work was supported in part by the Nantong Science and Technology Research Project under Grant JC2021125, JCZ21087.

Funding

This work is supported by the Nantong Science and Technology Research Project (Grant Numbers JC2021125 and JCZ21087).

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Authors and Affiliations

  1. School of Information Science and Technology, Nantong University, Nantong, China

    Wanzhi Wen, Tian Zhao, Shiqiang Wang & Jiawei Chu

  2. College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China

    Deepak Kumar Jain

Authors
  1. Wanzhi Wen
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  2. Tian Zhao
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  3. Shiqiang Wang
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  4. Jiawei Chu
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  5. Deepak Kumar Jain
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis, coding were performed by SW. All authors read and approved the final manuscript.

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Correspondence to Wanzhi Wen.

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Wen, W., Zhao, T., Wang, S. et al. Code recommendation based on joint embedded attention network. Soft Comput 26, 8635–8645 (2022). https://doi.org/10.1007/s00500-022-07244-z

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