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KG2Lib: knowledge-graph-based convolutional network for third-party library recommendation

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Abstract

In the process of software system evolution, software users constantly put forward a large number of expectations. For these expectations, software developers usually use the existing third-party libraries and other software resources to accelerate their development processes. At present, tons of third-party libraries are available. Therefore, appropriate recommendation methods are very important for developers to find suitable libraries for their development projects. In this paper, we present KG2Lib, a recommendation method to assist software developers in selecting suitable software libraries for their current projects. KG2Lib exploits a knowledge-graph-based convolutional network to recommend software libraries by relying on a set of libraries which were already called by current projects. The interaction matrix, weight matrix and knowledge graph are the inputs of KG2Lib. What’s more, KG2Lib recommends libraries to developers from project level and library level, which can better capture the fine-grained information to achieve better recommend performance. The performance of KG2Lib was evaluated on three datasets with four existing baseline models. The experimental results show that KG2Lib achieves better performance and helps software developers accurately select the appropriate third-party libraries.

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Data availability statement

All the datasets analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61862063, 61502413, 61262025; the National Social Science Foundation of China under Grant No. 18BJL104; the Science Foundation of Young and Middle-aged Academic and Technical Leaders of Yunnan under Grant No. 202205AC160040; the Science Foundation of Yunnan Jinzhi Expert Workstation under Grant No. 202205AF150006; the Natural Science Foundation of Key Laboratory of Software Engineering of Yunnan Province under Grant No. 2020SE301; the Science Foundation of “Knowledge-driven intelligent software engineering innovation team.”

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Authors and Affiliations

  1. School of Software, Yunnan University, Kunming, 650000, China

    Jing-zhuan Zhao, Xuan Zhang, Zhu-dong Li & Bao-lei Wang

  2. Key Laboratory of Software Engineering of Yunnan Province, Kunming, 650000, China

    Xuan Zhang

  3. Engineering Research Center of Cyberspace, Kunming, 650000, China

    Xuan Zhang

  4. School of Information Science & Engineering, Yunnan University, Kunming, 650000, China

    Chen Gao

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  1. Jing-zhuan Zhao
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  4. Zhu-dong Li
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  5. Bao-lei Wang
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Correspondence to Xuan Zhang.

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Zhao, Jz., Zhang, X., Gao, C. et al. KG2Lib: knowledge-graph-based convolutional network for third-party library recommendation. J Supercomput 79, 1–26 (2023). https://doi.org/10.1007/s11227-022-04603-3

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