TY - GEN
T1 - Self-reported activities of Android developers
AU - Pascarella, Luca
AU - Geiger, Franz-Xaver
AU - Palomba, Fabio
AU - Di Nucci, Dario
AU - Malavolta, Ivano
AU - Bacchelli, Alberto
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85051682506&partnerID=MN8TOARS
U2 - 10.1145/3197231.3197251
DO - 10.1145/3197231.3197251
M3 - Conference contribution
BT - Proceedings - International Conference on Software Engineering
ER -