Conference Paper


Automatically Classifying Requirements from App Stores: A Preliminary Study

Abstract

In this paper we apply self-labeling algorithms as Semi-Supervised Classification (SSC) techniques in order to automate the classification of functional and non-functional requirements contained in reviews in the App Store. In this domain, where it is easy collect a large number of review but difficult to manually annotate then, we found that SSC techniques can successfully perform this task and that only a small amount of data is needed to achieve results similar to classical supervised techniques. We also found that the models learned can properly assign labels to the collected data and can classify unseen future reviews. We believe SSC techniques can be of particular use during requirements classification.

Attached files

Authors

Deocadez, Roger
Harrison, Rachel
Rodriguez, Daniel

Oxford Brookes departments

Faculty of Technology, Design and Environment\Department of Computing and Communication Technologies

Dates

Year of publication: 2017
Date of RADAR deposit: 2017-08-11



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Related resources

This RADAR resource is the Accepted Manuscript of Automatically Classifying Requirements from App Stores: A Preliminary Study

Details

  • Owner: Daniel Croft
  • Collection: Outputs
  • Version: 1 (show all)
  • Status: Live