DOI

  • Luca Pascarella
  • Franz-Xaver Geiger
  • Fabio Palomba
  • Dario Di Nucci
  • Ivano Malavolta
  • Alberto Bacchelli
To gain a deeper empirical understanding of how developers work on Android apps, we investigate self-reported activities of Android developers and to what extent these activities can be classified with machine learning techniques. To this aim, we firstly create a taxonomy of self-reported activities coming from the manual analysis of 5,000 commit messages from 8,280 Android apps. Then, we study the frequency of each category of self-reported activities identified in the taxonomy, and investigate the feasibility of an automated classification approach. Our findings can inform be used by both practitioners and researchers to take informed decisions or support other software engineering activities.
Original languageEnglish
Title of host publicationin Proceedings of the 5th IEEE/ACM International Conference on Mobile Software Engineering and Systems
PublisherACM / IEEE
Pages144-155
Number of pages12
ISBN (Electronic)978-1-4503-5712-8
DOIs
StatePublished - 23 Feb 2018
Event5th IEEE/ACM International Conference on Mobile Software Engineering and Systems - Gothenburg, Sweden
Duration: 27 May 201828 May 2018
Conference number: 5
http://mobilesoftconf.org/2018/

Conference

Conference5th IEEE/ACM International Conference on Mobile Software Engineering and Systems
Abbreviated titleMobileSoft
CountrySweden
CityGothenburg
Period27/05/1828/05/18
Internet address

    Research areas

  • Android, Empirical Study, Mining Software Repositories

ID: 36483359