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/Conference proceedings/Edited volumeConference contributionScientificpeer-review

16 Citations (Scopus)
330 Downloads (Pure)

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 publication5th IEEE/ACM International Conference on Mobile Software Engineering and Systems. ACM, New York, NY, to appear
PublisherACM/IEEE
Pages144-155
ISBN (Electronic)978-1-4503-5712-8
DOIs
Publication statusPublished - 2018
Event5th IEEE/ACM International Conference on Mobile Software Engineering and Systems - Gothenburg, Sweden
Duration: 27 May 201828 May 2018
Conference number: 5

Conference

Conference5th IEEE/ACM International Conference on Mobile Software Engineering and Systems
Abbreviated titleMOBILESoft 2018
Country/TerritorySweden
CityGothenburg
Period27/05/1828/05/18

Bibliographical note

Acknowledgments: European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642954

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