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Source Code Recommendation with Sequence Learning of Code Functions

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Advanced Information Networking and Applications (AINA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 451))

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Abstract

For finding desired source codes and articles using existing source code search engines, it is necessary to compare them in the different results to examine which source codes are the best practice for developing the target software. In this paper, we propose a method of source code recommendation based on the prediction of code function. The feature of the proposed method is that by interpreting the context of the processing procedure of the source code, it recommends source code which complements or follow the processing procedures. In the experiment using actual source codes, we evaluate the feasibility of the proposed method.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 17K00498.

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Authors and Affiliations

  1. Course of Information and Computer Sciences, Graduate School of Engineering, Kanagawa Institute of Technology, 1030 Shimo-ogino, Atsugi, Kanagawa, 243-0202, Japan

    Erika Saito

  2. Department of Information and Computer Sciences, Kanagawa Institute of Technology, 1030 Shimo-ogino, Atsugi, Kanagawa, 243-0292, Japan

    Kosuke Takano

Authors
  1. Erika Saito
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  2. Kosuke Takano
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Corresponding author

Correspondence to Kosuke Takano .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Technology Sydney, Sydney, NSW, Australia

    Farookh Hussain

  3. Faculty of Bussiness Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Saito, E., Takano, K. (2022). Source Code Recommendation with Sequence Learning of Code Functions. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_30

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