Self-Reported Activities of Android Developers

Conference paper (2018)

Authors

L. Pascarella Software Engineering - EEMCS
Franz-Xaver Geiger Vrije Universiteit Amsterdam
F. Palomba University of Zürich
Dario Di Nucci Vrije Universiteit Brussel
Ivano Malavolta Vrije Universiteit Amsterdam
A. Bacchelli University of Zürich
Research Group
Software Engineering (EEMCS) (TU Delft)
Copyright: © 2018 L. Pascarella, Franz-Xaver Geiger, F. Palomba, Dario Di Nucci, Ivano Malavolta, A. Bacchelli
DOI: https://doi.org/10.1145/3197231.3197251
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Software Engineering

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.

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