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Graph Link‐Based Prediction for API Usage Recommendation.

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Title: Graph Link‐Based Prediction for API Usage Recommendation.
Authors: Guo, Junxia1 (AUTHOR) gjxia@mail.buct.edu.cn, Tao, Yingjie1 (AUTHOR), Lai, Baoqiang1 (AUTHOR), Yu, Bowen1 (AUTHOR), Li, Zheng1 (AUTHOR) lizheng@mail.buct.edu.cn
Source: Journal of Software: Evolution & Process. May2026, Vol. 38 Issue 5, p1-16. 16p.
Subjects: Graph neural networks, Computer software reusability, Java programming language, Software engineering
Abstract: Application programming interface (API) is an important form and is widely used in software projects in the software reuse field. However, it is not easy for developers to find out the appropriate APIs for their programming tasks, especially for unfamiliar tasks. Technologies that recommend suitable APIs for developers can help a lot. Although those technologies make the process of API reuse more convenient than searching directly on the Web, the recommendation performance still needs to be further improved, especially when a new project starts. How to get as much information as possible is very important. In this paper, we focus on this problem and incorporate multi‐source information to improve API recommendation effectively. Here, we propose a new approach, named graph link‐based prediction for API recommendation (GLBPAPI), which uses graph neural networks to learn fine‐grained representations between APIs and predict potential links inductively based on the API similarity. In addition, we combine the graph link prediction and probability model together as the recommendation engine in GLBPAPI to improve the effectiveness of the API recommendation. We evaluate our approach on 2210 open‐source Java projects extracted from GitHub and Maven Central. The experimental results show that it outperforms existing approaches in terms of mean average precision (MAP). [ABSTRACT FROM AUTHOR]
Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Graph Link‐Based Prediction for API Usage Recommendation.
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  Data: <searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+reusability%22">Computer software reusability</searchLink><br /><searchLink fieldCode="DE" term="%22Java+programming+language%22">Java programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink>
– Name: Abstract
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  Data: Application programming interface (API) is an important form and is widely used in software projects in the software reuse field. However, it is not easy for developers to find out the appropriate APIs for their programming tasks, especially for unfamiliar tasks. Technologies that recommend suitable APIs for developers can help a lot. Although those technologies make the process of API reuse more convenient than searching directly on the Web, the recommendation performance still needs to be further improved, especially when a new project starts. How to get as much information as possible is very important. In this paper, we focus on this problem and incorporate multi‐source information to improve API recommendation effectively. Here, we propose a new approach, named graph link‐based prediction for API recommendation (GLBPAPI), which uses graph neural networks to learn fine‐grained representations between APIs and predict potential links inductively based on the API similarity. In addition, we combine the graph link prediction and probability model together as the recommendation engine in GLBPAPI to improve the effectiveness of the API recommendation. We evaluate our approach on 2210 open‐source Java projects extracted from GitHub and Maven Central. The experimental results show that it outperforms existing approaches in terms of mean average precision (MAP). [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194054115
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        Value: 10.1002/smr.70114
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        Text: English
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        Type: general
      – SubjectFull: Computer software reusability
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      – SubjectFull: Java programming language
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      – SubjectFull: Software engineering
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            – D: 01
              M: 05
              Text: May2026
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              Y: 2026
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