Ensemble methods for app review classification: An approach for software evolution

Emitza Guzman, Muhammad El-Haliby, Bernd Bruegge

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

App marketplaces are distribution platforms for mobile applications that serve as a communication channel between users and developers. These platforms allow users to write reviews about downloaded apps. Recent studies found that such reviews include information that is useful for software evolution. However, the manual analysis of a large amount of user reviews is a tedious and time consuming task. In this work we propose a taxonomy for classifying app reviews into categories relevant for software evolution. Additionally, we describe an experiment that investigates the performance of individual machine learning algorithms and its ensembles for automatically classifying the app reviews. We evaluated the performance of the machine learning techniques on 4550 reviews that were systematically labeled using content analysis methods. Overall, the ensembles had a better performance than the individual classifiers, with an average precision of 0.74 and 0.59 recall.

Original languageEnglish
Title of host publicationProceedings - 2015 30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages771-776
Number of pages6
ISBN (Electronic)9781509000241
DOIs
Publication statusPublished - 4 Jan 2016
Externally publishedYes
Event30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015 - Lincoln, United States
Duration: 9 Nov 201513 Nov 2015

Publication series

NameProceedings - 2015 30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015

Conference

Conference30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015
CountryUnited States
CityLincoln
Period9/11/1513/11/15

Keywords

  • App Reviews
  • Software Evolution
  • Text Classification
  • User Feedback

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