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Heterogeneous Graph Neural Network-Based Software Developer Recommendation

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2022)

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Abstract

In software maintenance, it is critical for project managers to assign software issues to the appropriate developers. However, finding suitable developers is challenging due to the general sparsity and the long-tail of developer-issue interactions. In this paper, we propose a novel Heterogeneous Graph Neural Network-based method for Developer Recommendation (called HGDR), in which text information embedding and self-supervised learning (SSL) are incorporated. Specifically, to alleviate the sparsity of developer-issue interactions, we unify developer-issue interactions, developer-source code file interactions and issue-source code file relations into a heterogeneous graph, and we embed text descriptions to graph nodes as information supplements. In addition, to mitigate the long-tail influence, e.g., recommendation bias, the proficiency weight suppression link supplementation is proposed to complement the tail developers by adjusting proficiency weights. Finally, to fully utilize rich structural information of heterogeneous graph, we use the joint learning of metapath-guided heterogeneous graph neural network and SSL to learn the embedding representation. Extensive comparison experiments on three real-world datasets show that HGDR outperforms the state-of-the-art methods by 6.02% to 44.27% on recommended metric. The experimental results also demonstrate the efficacy of HGDR in the sparse and long-tail scenario. Our code is available at https://github.com/1qweasdzxc/HGDR.

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Notes

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Acknowledgement

This work is supported by the National Natural Science Key Foundation of China grant No. 61832014 and No. 62032016, the National Natural Science Foundation of China grant No. 62102281, the Natural Science Foundation of Tianjin City grant No. 19JCQNJC00200, and the Foundation of Jiangxi Educational Committee (GJJ210338).

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Authors and Affiliations

  1. Tianjin University, Tianjin, China

    Zhixiong Ye, Zhiyong Feng, Guodong Fan, Huwei Zhang & Shizhan Chen

  2. Jiangxi Normal University, Jiangxi, China

    Jianmao Xiao

  3. Fujian Normal University, Fujian, China

    Yuqing Gao

Authors
  1. Zhixiong Ye
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  2. Zhiyong Feng
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  3. Jianmao Xiao
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  4. Yuqing Gao
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  5. Guodong Fan
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  6. Huwei Zhang
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  7. Shizhan Chen
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Corresponding author

Correspondence to Jianmao Xiao .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  3. Zhejiang University City College, Hangzhou, China

    Wei Wei

  4. London South Bank University, London, UK

    Tasos Dagiuklas

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Ye, Z. et al. (2022). Heterogeneous Graph Neural Network-Based Software Developer Recommendation. In: Gao, H., Wang, X., Wei, W., Dagiuklas, T. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 460 . Springer, Cham. https://doi.org/10.1007/978-3-031-24383-7_24

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  • DOI: https://doi.org/10.1007/978-3-031-24383-7_24

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-24383-7

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