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Effective Monotone Knowledge Integration in Kernel Support Vector Machines Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Recommending Features of Mobile Applications for Developer

Authors : Hong Yu, Yahong Lian, Shuotao Yang, Linlin Tian, Xiaowei Zhao

Published in: Advanced Data Mining and Applications

Publisher: Springer International Publishing

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Abstract

Features recommendation is an important technique for getting the requirements to develop and update mobile Apps and it has been one of the frontier study in requirements engineering. However, the mobile Apps’ descriptions are always free-format and noisy, the classical features recommendation methods cannot be effectively applied to mobile Apps’ features recommendation. In addition, most mobile Apps’ source codes that contain API calling information can be obtained by software tools, which can accurately indicate the functional features. Therefore, this paper proposes a hybrid feature recommendation method of mobile Apps, which is based on both explicit description and implicit code information. A self-adaptive similarity measure and KNN is used to find relevant Apps, and functional features are extracted from the Apps and recommended for developers. Experimental results on four categories Apps show that the proposed features recommendation method with hybrid information is more effective than the classical method.

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Metadata
Title
Recommending Features of Mobile Applications for Developer
Authors
Hong Yu
Yahong Lian
Shuotao Yang
Linlin Tian
Xiaowei Zhao
Copyright Year
2016
Book
Advanced Data Mining and Applications

Print ISBN: 978-3-319-49585-9

Electronic ISBN: 978-3-319-49586-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-49586-6_24

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