Self-reported activities of Android developers

Luca Pascarella, Franz Xaver Geiger, Fabio Palomba, Dario Di Nucci, Ivano Malavolta, Alberto Bacchelli

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

Abstract

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 publicationProceedings - 2018 ACM/IEEE 5th International Conference on Mobile Software Engineering and Systems, MOBILESoft 2018
Place of PublicationNew York, NY
PublisherACM, IEEE Computer Society
Pages144-155
Number of pages12
ISBN (Print)9781450357128
DOIs
Publication statusPublished - 27 May 2018
Event5th ACM/IEEE 5th International Conference on Mobile Software Engineering and Systems, MOBILESoft 2018, collocated with the 40th International Conference on Software Engineering, ICSE 2018 - Gothenburg, Sweden
Duration: 27 May 201828 May 2018

Conference

Conference5th ACM/IEEE 5th International Conference on Mobile Software Engineering and Systems, MOBILESoft 2018, collocated with the 40th International Conference on Software Engineering, ICSE 2018
CountrySweden
CityGothenburg
Period27/05/1828/05/18

Keywords

  • Android
  • empirical study
  • mining software repositories

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