Mining Software Repositories for Automatic Interface RecommendationReport as inadecuate

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Scientific Programming - Volume 2016 2016, Article ID 5475964, 11 pages -

Research Article

School of Information Engineering, Yangzhou University, Yangzhou 225127, China

State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China

Hainan University, Haikou 570228, China

Received 30 January 2016; Revised 3 May 2016; Accepted 18 May 2016

Academic Editor: Laurence T. Yang

Copyright © 2016 Xiaobing Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


There are a large number of open source projects in software repositories for developers to reuse. During software development and maintenance, developers can leverage good interfaces in these open source projects and establish the framework of the new project quickly when reusing interfaces in these open source projects. However, if developers want to reuse them, they need to read a lot of code files and learn which interfaces can be reused. To help developers better take advantage of the available interfaces used in software repositories, we previously proposed an approach to automatically recommend interfaces by mining existing open source projects in the software repositories. We mainly used the LDA Latent Dirichlet Allocation topic model to construct the Feature-Interface Graph for each software project and recommended the interfaces based on the Feature-Interface Graph. In this paper, we improve our previous approach by clustering the recommending interfaces on the Feature-Interface Graph, which can recommend more accurate interfaces for developers to reuse. We evaluate the effectiveness of the improved approach and the results show that the improved approach can be more efficient to recommend more accurate interfaces for reuse over our previous work.

Author: Xiaobing Sun, Bin Li, Yucong Duan, Wei Shi, and Xiangyue Liu



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