API recommendation method based on natural nearest neighbors and collaborative filtering

被引：0
|
作者：
Zheng H.-H. [1 ]
Huang Z.-Q. [1 ]
Li W.-W. [1 ]
Yu Y.-S. [1 ]
Wang Y.-C. [1 ]
机构：
[1] College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2022年 / 56卷 / 03期
关键词：
API recommendation; BM25; Code reuse; Collaborative filtering; Natural nearest neighbors;
D O I：
10.3785/j.issn.1008-973X.2022.03.008
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
An API recommendation method based on natural nearest neighbors and collaborative filtering named N-APIRec was proposed in order to solve the problem of recommendation performance degradation caused by improper neighbor selection. In this model, BM25 algorithm was used to transform the projects into vectors. Then the natural neighbor algorithm was used to filter the similar projects in the dataset to reduce the search scope, and the similar method declarations were filtered from the similar projects. Finally, the APIs were recommended through collaborative filtering. N-APIRec was compared with the state-of-the-art approach on MV and SH data sets. The results were verified the effectiveness of N-APIRec, the success rate of MV and SH data sets recommendation was 77.38%and 30.00% respectively, which was better than the existing methods. Copyright ©2022 Journal of Zhejiang University (Engineering Science). All rights reserved.
引用
收藏
页码：494 / 502
页数：8
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