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An Approach of Extracting Feature Requests from App Reviews

  • Zhenlian Peng
  • Jian WangEmail author
  • Keqing He
  • Mingdong Tang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)

Abstract

With the rapid development of mobile technologies, developing high-quality mobile apps becomes increasingly important. App reviews, which are collaboratively collected from various users, are viewed as important sources for enhancing or evolving mobile apps, wherein how to accurately extract feature requests becomes an important issue. However, the scale of app reviews is so large that it is intractable to manually identify feature requests from these reviews. In this paper, we propose a semi-automated approach to extract feature requests based on machine learning approaches. In our approach, we firstly identify reviews on feature requests by defining suitable classification features and selecting appropriate classification approaches. Afterwards, these identified reviews are clustered using topic models, and phrases are extracted as feature requests, which serve as the basis of feature modeling. Experiments conducted on a real world data set show that the proposed approach can contribute to extracting feature requests from app reviews.

Keywords

Feature requests App review Classification Word dependencies 

Notes

Acknowledgments

The work is supported by the National Basic Research Program of China under grant No. 2014CB340404, and the National Key Research and Development Program of China under grant No. 2016YFB0800400, and the National Natural Science Foundation of China under Nos. 61672387, 61373037, 61572186 and 61562073. The authors would like to thank anonymous reviewers for their valuable suggestions.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Zhenlian Peng
    • 1
    • 2
  • Jian Wang
    • 1
    Email author
  • Keqing He
    • 1
  • Mingdong Tang
    • 2
  1. 1.State Key Lab of Software Engineering, Computer SchoolWuhan UniversityWuhanChina
  2. 2.Computer SchoolHunan University of Science and TechnologyXiangtanChina

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