Graph Link‐Based Prediction for API Usage Recommendation.
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| Title: | Graph Link‐Based Prediction for API Usage Recommendation. |
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| 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] |
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| Database: | Engineering Source |
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